Indirect Rapid Tooling

10.13

Indirect Rapid Tooling

Nagahanumaiah, CSIR – Central Mechanical Engineering Research Institute, Durgapur, India B Ravi, Indian Institute of Technology Bombay, Mumbai, India Ó 2014 Elsevier Ltd. All rights reserved.

10.13.1 10.13.2 10.13.3 10.13.4 10.13.4.1 10.13.4.2 10.13.4.3 10.13.4.4 10.13.4.5 10.13.4.6 10.13.4.7 10.13.5 10.13.5.1 10.13.5.2 10.13.5.2.1 10.13.5.2.2 10.13.5.2.3 10.13.5.2.4 10.13.5.2.5 10.13.5.3 10.13.5.3.1 10.13.5.3.2 10.13.5.3.3 10.13.5.3.4 10.13.5.3.5 10.13.5.4 10.13.5.4.1 10.13.5.4.2 10.13.5.4.3 10.13.5.4.4 10.13.5.4.5 10.13.5.4.6 10.13.5.4.7 10.13.5.4.8 10.13.5.4.9 10.13.6 References

10.13.1

Introduction Tooling Requirements and Rapid-Tooling Challenges Rapid Hard Tooling Indirect Rapid Tooling Silicone Rubber Molds by Vacuum Casting Epoxy Molds Nickel Electroformed Tooling Rapid-Prototyping Investment Cast Metal Mold Spray Metal Tooling 3D Keltool Rapid Solidification Process Tooling Rapid Tooling Process Selection and Manufacturability Evaluation Overall Methodology Tooling Requirements Prioritization Tooling Requirements Tooling Attributes and Their Issues in RT Molds Structuring the Attributes Determining Relative Priorities and Weights Checking the Consistency The QFD–AHP Approach for RT Process Selection Identifying RT Processes Determining Process Capability Weights Determining the Correlations between RT Processes Normalization of the QFD Matrix Selection of the Best Process Manufacturability Evaluation Primary Manufacturability Evaluation Quality Criteria for Manufacturability Evaluation Determining the Relative Manufacturability Weights Identifying Difficult-to-Manufacture Features Secondary Mold Elements Compatibility Parting Surface Multicavity Mold Cooling Lines Ejectors Summary

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Introduction

The ability to create complex geometries by employing near net shape (NNS) manufacturing processes, such as injection molding and die casting, in fewer steps triggers techno-economical manufacturing. The applications are many in diverse areas, including automobiles, electronics, biomedical devices, and consumer goods. However, it needs to be appreciated that net shape or NNS processes do not become functional themselves: they require a strong interfacing with rapid and cost-effective manufacturing protocols to produce molds and dies. This is because use of molds and dies in these processes enables material savings of 30% or more compared to material removal processes (1). In commercial tool rooms, among others, molds for plastic molding have a dominant role (up to 33%), compared to 31% of punches and dies, 13% each of pressured die casting and jigs and fixtures, and 10% of gauges (2). Currently, the tooling industries are striving hard to be competitive under growing demands to reduce the time and cost of die and mold development, offer better accuracy and surface finish, and provide flexibility to accommodate future design changes and the molds for shorter production runs. To reduce the manufacturing lead time of molds, two widely different approaches have been

Comprehensive Materials Processing, Volume 10

http://dx.doi.org/10.1016/B978-0-08-096532-1.01014-1

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explored in the last two decades: high-speed machining and additive-manufacturing (AM)-based or rapid-prototyping (RP)-based methods. The natural advantages of AM-based tooling fabrication methods lie in handling any complex geometry, saving tooling material, and obtaining a higher reduction in lead time, which prompted many of the industries to integrate AM and tooling techniques in their new product–process development chain in the following manner: 1. The AM models are used as mold design aids, where mold designers are aware of the complexity of part geometry, to make mold design decisions appropriately. 2. Few AM processes could produce the mold cavity and core directly from tool design CAD (computer-aided design) data, which are known as ‘direct rapid tooling.’ 3. AM models are used as patterns in some other manufacturing processes like investment casting, slip casting, powder sintering, and so on to develop the injection molds. These are known as ‘indirect rapid tooling.’ The above concept of fabricating tooling (dies and molds), directly or indirectly based on AM processes, is referred to as rapid tooling (RT). As discussed in Chapter 10.12, the direct RT process fabricates dies and molds directly from the CAD model of the tooling using an appropriate AM system. In contrast, indirect RT is a multistep process, starting from AM to produce a master, and converting it into a mold using secondary processes. The mold material, size limitation, accuracy, and geometric constraints depend on the specific RT process chain. Over 30 RT processes have been reported in the literature, and based on the expected mold life (the number of parts that can be produced in a mold), RT methods are also classified as soft, bridge, and hard tooling. The soft- and bridgetooling methods are suitable for producing molds that can be used to produce a limited number of parts. These molds usually cannot withstand the pressures and temperatures involved in conventional injection molding. The hard-tooling methods enable fabricating metal tooling that can be used in injection-molding machines, and they result in a better quality and larger quantity of parts (compared to soft and bridge tooling). The most important RT processes useful for developing injection molds are listed in Table 1, indicating the related AM process as well as classification in terms of direct, indirect, soft, bridge, and hard tooling. In this chapter, a comprehensive review on indirect RT processes highlighting the process methodology, the performance indices, and their significance in RT process selection is presented with an objective of increasing the penetration of RT techniques in rapid manufacturing.

10.13.2

Tooling Requirements and Rapid-Tooling Challenges

In injection molding, the plastic melt is injected into the mold cavity at high pressure and temperature, which solidifies to produce the desired geometry, and is finally ejected out. The molding cycle follows the sequence: mold closing, filling, packing, holding, cooling (solidification), mold opening, and part ejection. The mold cooling typically accounts for half the cycle time. The highest temperature and pressure occur at the beginning of the cycle: during mold filling, as shown in Figure 1. The material, construction, accuracy, and surface finish of the injection mold directly influence the part quality and cost. A typical injection mold is made in two halves, which are clamped together under force during the mold filling, packing, and cooling stages, and moved apart during part ejection (Figure 2). The mold cavity is formed by two inserts, called core and cavity inserts, which are the functional elements of mold imparting the desired geometry to the incoming melt. An insert can be made of a single block or comprise multiple segments, for ease of manufacturability and part ejection. The feeding system, comprising a sprue bush, runner, and gate, enables melt flow from the machine nozzle to the cavity. The cooling system enables controlling the solidification rate. The ejection system enables the ejection of the molded part that shrinks onto the core insert. All of these elements are housed in the mold base set, which also includes support blocks and guides to make it rigid and withstand the pressures and temperatures encountered in the process. Table 1

Important rapid tooling processes and their parent AM processes Type of rapid tooling

Sl.

Rapid tooling process

Parent AM process

Direct

1 2 3 4 5 6 7 8 9 10 11 12 13 14

SLA direct-AIM SL EP 250 molds SLS-rapid steel Direct metal laser sintering Direct shell production casting Prometal RTS300 Metal laminated tooling Multimetal layer tooling SDM wax mold Investment cast (RP-IC) molds 3D Keltool Spray metal tooling Vacuum casting AM pattern-based powder sintering

SLA SLA SLS DMLS/SLS 3DP 3DP LOM SDM SDM SLA, FDM SLA, Keltool SLA, SLS SLA, SLS, FDM SLA, SLS

O O O O O

Indirect O

Soft O

O

O O O O O O

Bridge O

O O

O

O O

Hard

O O O O O O O O

Indirect Rapid Tooling

Figure 1

Pressure and temperature in typical plastic injection molding cycle.

Figure 2

Construction of a typical injection mold.

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The core and cavity inserts, feeding system, and cooling channels are specific to a molded part and conventionally manufactured using a computer numerical control (CNC) machining center and electric discharge machining machines. The mold base, along with guides, support plates, ejector plates, and other accessories, are procured as standard items. The production time for typical injection mold inserts using conventional practice is 1300–2400 h (3), making it uncompetitive. The built-in capability of AMbased tooling fabrication methods in handling complex geometry, ensuring material saving and lead time reduction, prompted several RT developments in both the direct and indirect approaches. For rapid manufacturing, molds are expected to produce parts that are very close to their final specifications (tolerances and microstructure). In this regard, the thermal, mechanical, and metallurgical properties of RT material have a combined influence on product quality, mold life, and cost competitiveness. However, RT methods have certain challenges that need to be addressed before one can use them for manufacturing: l

It is evident that many RT methods could produce the mold inserts in either nonmetallic or nontool steel materials, unlike tool steel molds produced by the conventional tooling route. This would alter the molding cycle, mold wear pattern, and so on, which are not fully understood and are not generic. l RT is manufactured either directly from a layer-by-layer process (direct RT) or by using layered manufactured models as master patterns (indirect RT). Hence, the inherent limitations of AM processes, including stair step effects, differential shrinkage, and geometric constraints, lead to inaccuracy, poor surface finish, and limited material range.

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l

The core and cavity inserts (functional mold elements) fabricated by the RT process chain often need to be machined either to incorporate accurate feeding systems (runners, gates, vents, etc.) and/or to mount inside mold boxes; poor machinability of RT molds becomes a bottleneck. l Mold polishing, providing drafts for easy part ejection, mold repair, or maintenance, is a daunting task for many RT fabrication methods. The growing number of RT processes having said limitations makes selection of an appropriate RT process difficult. The incompatibility between the mold design and RT process capabilities (having poor information) further aggravates process selection and manufacturability problems, leading to expensive tooling iterations or underutilization of emerging technologies. In this regard, rapid manufacturing research over the last decade had a paradigm shift toward finding logical solutions for the problem, which has three components. First is further investigation and improvement on the accuracy, surface finish, and materials range for AM, which are mother processes for RT. Second is developing new manufacturing processes by integrating regenerative processes to fabricate mold inserts using AM master patterns (indirect RT). Third is the RT selection and manufacturability evaluation, where the cost modeling and database development become obvious. In this chapter, a comprehensive overview on indirect RT process technologies to fabricate hard tooling is discussed, followed by methodologies to evaluate their manufacturability, leading to appropriate selection of an RT process.

10.13.3

Rapid Hard Tooling

As it is very well appreciated in literature, the concept of RT originated from the need to fabricate a relatively large number of prototypes using the same material and production process as will be used in full-scale production. Current AM technologies are neither capable of producing the prototypes in a wide range of materials nor economical for their production in large numbers. This has led to the development of core and cavity inserts, which are in turn used in conventional molding or casting processes. Presently, the importance of RT, however, goes far beyond this need. RT processes complement the AM techniques by being able to provide higher quantities of prototypes in desired (designed) materials. It needs to be appreciated that these developments were accelerated with rapid improvement that took place on different technological aspects of AM processes. This development path was getting populated with many developments that took place in parallel during 1995–2005, when several researchers were involved in evolving alternative ways of manufacturing injection molds by integrating AM techniques. Early review reports by Karpatis (4) and Eric Radstock (5), presented the status of direct RT and findings of web-based conferences conducted to find the process capabilities of many RT processes, respectively. Later, Rosochowski presented the RT state of the art, which gives cost and lead time comparisons between several RT processes (6). The comprehensive review presented by Levy et al. highlighted several promising RT tooling methods, which are based on AM techniques. These researchers discussed the different RT process chains and working principles. Moreover, this was when the importance and need for future research in the directions of design for layered manufacturing, process selection, RT process modeling, and so on to enjoy the real potential of RT techniques in rapid manufacturing were recognized (7). In summary, RT found significant growth during 1999–2005. As soft- and bridge-tooling methods cannot produce tooling in conventional tool steels, their use is limited to prototyping. In order to realize the potential of rapid manufacturing, newer processes for fabricating RT in steels or equivalent composite materials, which are required for better quality and batch production, have been developed using both direct and indirect approaches. This includes 3D Keltool, selective laser-sintering (SLS) molds, direct metal laser-sintering (DMLS) molds, shape deposition modeling (SDM) steel molds, and hot isostatic pressing of SLS tooling. However, their process capability and performance in practical injection molding vary significantly. For example, the 3D Keltool is an indirect RT process, which uses an AM model as a pattern to produce A6 steel production mold inserts, which can withstand up to 0.5 million injection-molding cycles (8). The SLS process can directly build carbon steel or stainless-steel molds (5). The DMLS process uses a laser for liquid phase sintering of metal powder, both bronze-based powder and steel-based powder (9). The SDM process can build the tool steel dies with hardness up to 40 HRC, which can withstand high pressure and produce good-quality castings comparable with those produced from H13 die material (10). In addition, there were several exploratory studies conducted across the globe, but a detailed techno-economic analysis of this route is unavailable. Figure 3 illustrates some of the rapid hard tooling that can produce metallic molds and have shown promising trends in producing a few thousand plastic parts under real injection-molding process conditions. While detailed discussion on direct RT is beyond the scope of this chapter and some of the details are already provided in Chapter 10.12, indirect RT processes are presented in this chapter.

10.13.4

Indirect Rapid Tooling

The advantage of indirect RT processes is their built-in capability of producing molds in a wide range of materials. In these methods, the limitations of mold material in direct tooling methods can be overcome by using the AM masters in one or more conversions (secondary processes) to finally produce tooling in harder materials. While several attempts have been reported in the literature, many of them have not matured to reach the test bench; therefore, discussions here are limited to the indirect RT processes, which have been used in producing prototypes and functional parts in batches.

Indirect Rapid Tooling

Figure 3

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Rapid hard-tooling processes as low-order production tooling.

10.13.4.1 Silicone Rubber Molds by Vacuum Casting Silicone rubber molds can be used to produce wax patterns and plastic or low-melting-point metal parts. In this process, initially the patterns for runners are attached to an AM model of a desired part, release coating is applied, and a block of silicone rubber is then cast around the pattern assembly. This process is widely known as vacuum casting, which is the simplest and oldest RT technique. An AM positive pattern is suspended in a vat of liquid silicone or room-temperature vulcanizing rubber. When the rubber hardens, the cured block is cut along the parting line into two halves, and the AM pattern is removed (Figure 4). The major advantage of these molds is their flexibility, which facilitates easy removal of intricate and undercut shapes. However, their use is limited to low-pressure, low-volume, and low-temperature production such as vacuum casting of prototypes in a polyurethane material and spin casting of Zn alloys. In recent years, commercialized vacuum-casting systems have been used to cast the silicone rubber mold around the AM pattern, and then the mold is used to produce the plastic part or wax pattern using the same setup. In this process, polyurethane can be formulated with a wide variety of physical properties. In most instances, it will reproduce up to 20 parts with a gradual deterioration of surface quality. As a variant, spin casting has been integrated into this process chain to produce the mold by high temperature vulcanizing (HTV) rubber under pressure, using AM models as master patterns. After removing the masters, gates, runners, and air vents are cut into the mold, and then the metal or plastic is cast by pouring the melt as the mold rotates. The expected life of an HTV rubber mold varies from 100 to 300 cycles. Advantages 1. Silicon rubber molds are flexible, which enables easy demolding of intricate components. 2. Better options to produce concept models in polymers in 1–2 days. 3. Silicon rubber molds’ surface finish is very good: they impart the same finish on the polymer parts in vacuum casting, provided proper venting (risers) have been incorporated in the mold. Disadvantages 1. Limited mold life; applications are limited to concept prototypes in small batch sizes. 2. Suitable for a limited range of materials such as a few types of polymers (vacuum casting) and Zn alloys (spin casting). 3. Undercut features with a high aspect ratio make demolding complex, leading to spoiling of the mold with one-piece molding.

10.13.4.2 Epoxy Molds Epoxy molds are used in injection molding very often for short runs of functional prototypes. First, the positive master, produced in any of the AM processes (e.g., the stereolithography (SLA), SLS, or laminated object manufacturing (LOM) model), is buried in clay or plaster up to the parting line. Alternatively, one can also use a prototype model for the first half of the part. After coating the master with a release agent, epoxy resin is poured into the mold box and cured. The second half of the mold is produced using the

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Figure 4

Indirect Rapid Tooling

Silicon rubber mold development process sequence.

same procedure except that the other half of the part is used as a pattern. Runners and gates are added to the master prior to casting or can be machined after casting. Air vents are usually added during the trials. Epoxy molds can withstand 50–500 shots, which can be improved by metal (aluminium) fillers (Figure 5). Advantages 1. Epoxy molds can withstand molding pressure and temperature in injection molding for a few hundred shots. 2. These molds can be used to produce small batches of functional components using end-use material by injection molding. 3. When used with back plates, assembly of the core and cavity with the mold base becomes easy. Disadvantages 1. The thermo-mechanical properties of this mold material are far from those of conventional steel molds, causing defects in the molded parts unless the process is optimized for the specific part. 2. The mold surface finish is very poor, as it varies with the RP pattern finish, curing of epoxy, and formation of blow holes during epoxy casting. 3. These molds are not recommended for highly abrasive plastics such as glass fiber filled polymers and high-temperature plastics such as polyether ether ketone.

10.13.4.3 Nickel Electroformed Tooling The electrodeposition process can obtain a nickel shell, which has good mechanical properties and good reproduction of the surface finish. However, these processes are very slow; typically, it takes about 2 weeks for 3 mm thickness. The electroformed shell formed over the AM model is then backed by metal-filled epoxy resins (Figure 6). Advantages 1. These molds overcome the surface finish problem of epoxy molds to a great extent. 2. With epoxy or aluminium backup, these molds could withstand the typical pressure and temperature of injection molding.

Indirect Rapid Tooling

Figure 5

Epoxy mold development process sequence.

Figure 6

Electroformed nickel-tooling development process sequence.

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3. Introducing the electroformed nickel shell ensures improved thermo-mechanical properties of the mold, which result in better control over the molding cycle and part quality. Disadvantages 1. Nonconformity in electroformed shell thickness is one of the primary causes for its limited life. 2. The adhesive properties of backup material and the electroformed shell play key roles in mold failure (i.e., there is a high risk in crack formation at high temperature and pressure). 3. Deformity of the shell often becomes an issue in mold assembly and for dimension control over the molded parts.

10.13.4.4 Rapid-Prototyping Investment Cast Metal Mold Several AM or RP processes can produce dispensable patterns for use in investment casting, giving functional parts in any desired metal or alloy. This includes QuickCast models produced in SLA, wax models produced in fused deposition modeling (FDM), and polymer models produced by Thermojet AM processes. By using the CAD models of core and cavity inserts of injection molds, these processes can produce metal molds (Figure 7). The metals include aluminium for prototyping purposes, or tool steel for highvolume production. These molds require finish machining, which is somewhat difficult owing to the absence of a machining reference. Their performance in injection molding is otherwise comparable with that of conventional molds. Advantages 1. This tooling route facilitates cost-effective manufacturing of rapid hard tooling. 2. Depending on the final mold material cast through this process chain, investment cast molds can be used for high-pressure and high-temperature conditions. 3. Mold inserts cast by this method can be machined for better surface finish and/or to alter the runner and gating positions, depending on the requirements. Disadvantages 1. Poor dimensional control over mold dimensions. Often, it needs finish machining for easy demolding. 2. Poor surface finish and significant surface porosities at the cavity surfaces. 3. Fettling of the ceramic shell after casting limits its use to moderately complex geometries. Otherwise, the user has to compromise by accepting the surface defects caused during fettling.

Figure 7

RP-IC mold development process sequence.

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10.13.4.5 Spray Metal Tooling Spray metal tooling is an indirect RT process in which a low-melting-point alloy is sprayed over an AM model (pattern) to produce a metal shell containing the corresponding mold cavity (Figure 8). The pattern material should have adequate strength and durability to withstand the thermal impact of the metal spray. The metal shell is supported by a backing material (such as epoxy) to create a mold insert, which can be used for a few hundred injection-molding cycles. Metal spray molds have been successfully used for low-pressure processes, such as vacuum forming, rotational molding, and rotational injection molding. Recent advances in spray metals and spraying techniques have made it possible to produce tooling for conventional injection molding also. A nickel spray mold exhibits excellent abrasion resistance and hardness in the range of HRC 50–58 (11,12). The tool life is several hundred injection-molded plastic parts. Advantages 1. This is one of the cost-effective rapid manufacturing methods for small-order production molds. 2. The use of metallic coating improves the thermo-mechanical properties, compared to those of epoxy-based RT. Disadvantages 1. Nonconformity in coating thickness, particularly on vertical walls, is one of the major issues. 2. Poor control over mold dimensions. 3. Shell deformation and porosity formation at the interface of the spray-coated shell and backup material lead to delamination of deposited layers.

10.13.4.6 3D Keltool The 3D Keltool process developed by 3D Systems (by integrating the SLA and Keltool processes) uses an AM model as a master for making interim molds in silicone rubber, which are then filled with a slurry of tool steel, tungsten carbide, and epoxy binder.

Figure 8

Spray metal mold manufacturing process sequence.

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The binder is then burnt out, and the voids are infiltrated with copper to produce production mold inserts. The geometry of the master model is usually the same as that of the mold. In the case of intricate parts, the geometry of the master model is a replica of the part, and this is converted to the mold geometry through an additional step (Figure 9). The mold material used in 3D Keltool is close to A6 tool steel (ASM Standard) and can withstand up to 0.5 millions injection-molding cycles (8). Advantages 1. This is a better alternative to investment cast molds for producing rapid hard tooling. 2. These molds are machinable, and they enable the reworking or modification of runners and gate dimensions and positions after trial runs, if necessary. 3. The thermo-mechanical properties of the molds are comparable to those of A6 tool steel. This ensures an optimized molding cycle and quality of the end products. Disadvantages 1. This multistage mold-making process has poor control over dimensions, although the lead time is short. 2. Mold machining to achieve the desired dimensional accuracy on mold features is cumbersome.

10.13.4.7 Rapid Solidification Process Tooling Rapid solidification process (RSP) is a widely used process in metallurgy to impart desired characteristics to alloys. The science behind this process is to create droplets of molten metal that are allowed to cool over a heatsink. The molten metal is atomized at a pressure that breaks down the liquid metal into small droplets by the shearing effect of pressurized media such as gas. These droplets, which have a larger surface area compared to their volume, cool rapidly over the heatsink. While this process is widely used in the fabrication of metallic glass, Idaho National Laboratory developed this process for production of RT that reduces the lead time by a factor of 5–10.

Figure 9

3D Keltool mold-manufacturing process sequence.

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In RSP tooling, the AM patterns are used as a master mold over which atomized liquid metal is sprayed layer by layer. RSP tooling includes the following processing steps, as illustrated in Figure 10: 1. Manufacturing of a master mold (pattern) using the appropriate AM method. While SLA is widely used, other AM and/or machined wax patterns are also used. 2. Manufacturing of the ceramic negative using a process similar to ceramic shell or mold coating in investment casting or ceramic casting. This ceramic pattern (negative) is used as the target surface over which the atomized molten metal is sprayed. 3. Melting and pressurizing of molten tool steel in the specially designed pressurized crucible. 4. Atomizing the molten metal from the nozzle using the pressurized nitrogen gas and spraying over the ceramic pattern (target surface). The atomized droplets cool very rapidly, resulting in the desired characteristics of the alloy used. In order to facilitate spraying over intricate shapes, specially designed pattern manipulators are used. The typical droplet size is 50 mm in diameter sprayed on the ceramic pattern, replicating the pattern’s contours, surface textures, and other details (13). 5. After cooling, the metal mold insert is separated from the ceramic pattern and machined off with irregular periphery and/or overspray zones to finish the insert for mold assembly and testing. Advantages 1. RSP produces uniform microstructure and allows the user to tailor the properties by optimized cooling rates and artificial aging. This makes molds to use as production molds and dies in injection molding, pressure die casting, and forging. 2. Molds can be used as production molds, and it can be a better option for larger molds. For larger molds, RP patterns can be fabricated in parts and assembled to produce a large ceramic negative or pattern for spraying. 3. The thermo-mechanical properties of mold inserts are better, hence net shape manufacturing of parts can be ensured. 4. As the finish machining is unavoidable, the surface of the mold can be finished and polished as required in cases of die casting and forging dies.

Figure 10

The processing sequence of RSP tooling.

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Disadvantages 1. The process is very stochastic in nature, and the response of the target surface for spraying varies from layer to layer of coating. Ensuring uniform properties across thick cross-section mold inserts requires expertise. Otherwise dies may fail under high operating pressure and temperature. 2. Finish machining is always cumbersome, because maintaining a machining reference over the sprayed mold insert always demands different thoughts from the mold designer. In summary, indirect RT methods developed over three decades primarily focused on enhancement of the performance of RT with respect to life and thermo-mechanical properties apart from reducing the lead time. The approaches explored include metallization of AM patterns, strengthening of patterns by providing appropriate backup support, the use of stronger and desired mold material, and the hybridization of the process for enhanced capabilities. The processes like metal casting, spraying, and deposition, using AM patterns as masters, have been explored. In addition to indirect RT (as discussed in this chapter), there are a few other alternatives such as Sprayform, MetalCopy, and Kirksite casting, which have been demonstrated. However, these have not been discussed as their fundamental principles are similar to others. For example, the Kirksite tooling process is similar to that of the epoxy molds except that two additional reversals are used to create desired mold features. Similarly, Sprayform tooling is an improved version of the initial method of spray metal tooling discussed here, and is also similar to RSP tooling, where a ceramic pattern cast over an AM master is used as a pattern to spray the jet of molten metal on it. As many of these RT processes have been developed in parallel by multiple teams, a lot of similarities exist in their processing techniques. Moreover, few of these developments failed to find better prospectus, further improvements did not take place, and they failed to reach bench-level applications. With this wide range of process alternatives with varying technological maturity, the selection of an appropriate RT process becomes difficult for toolmakers. Some of the attempts made toward development of computer-assisted methodologies are discussed in this chapter.

10.13.5

Rapid Tooling Process Selection and Manufacturability Evaluation

The selection of an appropriate tooling fabrication process has a direct impact on part quality and the economics of new product realization. While many researchers have developed various methodologies for reliable, repeatable, and quick process selection for different manufacturing domains (14), they have not been practiced widely in commercial tool rooms. The reasons could be the complexity associated with die and mold designs, the conventional process selection models often being based on process capability comparison, and cost modeling (15,16) becoming very approximate in die and mold making. However, with the growing body of research work leading to a range of RT processes based on both direct and indirect approaches, the application of RT has witnessed the high-level technology maturity of today. The choice of various secondary processes such as investment casting, slip casting, sintering, and vacuum casting multiplies the number of tooling routes. The selection of the most appropriate process route for a given application is therefore becoming a nontrivial task. Therefore, computer-assisted systematic methodologies for AM processes and/or tooling process selection would enable toolmakers to use these technologies decisively. The first few research attempts on AM process selection for different applications have been reported by Muller and Schimmel, who developed a decision framework for selection of AM processes for prototype production (17). Wang et al. developed a systematic methodology to AM process selection to produce sand-casting patterns (18). They considered part geometry and critical features, postprocessing, build material (strength and durability), production volume, time, cost, and accuracy as decision variables, in addition to process-planning task attributes such as building orientation, slicing strategy, and so on. For RT, few researchers have indeed highlighted the need for a ‘rapid intelligent tooling system’ for deciding the optimal process around the same time (19). In one such attempt, a rule-based approach (with 180 rules) was used for the selection and evaluation of four RT methods (20). However, establishing the rule base for each route, made of a combination of various processes, is a difficult task. The more generic framework integrated with decision-making tools such as the analytical hierarchical process (AHP) and quality function deployment (QFD) has been developed by Nagahanumaiah et al. (21). This method has been described in detail in the following subsections. Manufacturability evaluation involves measurement of relative ease of manufacturing, which uses documented design guidelines supported by cost modeling to determine if a part can be manufactured with acceptable manufacturing quality, cost, and time. This has been conventionally performed at two levels (22,23). A high-level evaluation is mainly aimed at selection of the manufacturing process using parameter matching, case-based reasoning (a comparison with past designs using group technology), or rule-based expert systems. A detailed manufacturability evaluation is usually performed for a selected process using heuristics, analytical methods, and plan-based methods. Gupta and Dana (24) surveyed the research progress toward automated manufacturability analysis. The major research challenges include manufacturing knowledge representation, a manufacturability rating system, handling multiple manufacturing processes, and automated generation of design changes. Several researchers have reported their work on material and process selection, and analysis of specific manufacturing processes such as machining (24), milling (25), sheet metal (26,27), powder metallurgy (28), and injection molding (29,30). A manufacturability evaluation of NNS processes such as plastic injection molding requires process planning and manufacturability evaluation of tooling (dies and molds) in addition to the part. However, very little work is performed in this direction. In one such attempt, an axiomatic design-based process-planning approach has been developed for scheduling a mold-manufacturing

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sequence in a multimold-manufacturing environment (31). Lee et al. (32) proposed a knowledge-based concurrent mold design methodology to consider mold manufacturability issues early in mold design. A design for an RT manufacturing system called a ‘rapid tooling test bed for injection molding’ has been developed by Rosen et al. (33) using a decision-based approach to design a product for SLA–AIM tooling manufacturability; AIM is short for accurate clear epoxy solid injection molding. They developed geometry-based decision templates to formulate product design requirements using a compromise decision support approach (34). Mold design guidelines for SLA–AIM manufacturability were generated through a series of studies and experimental validations related to mold expansion, percentage of crystallization, and the effect of a direct SLA resin mold on part quality and shrinkage (35,36). In that study, tensile failure of SLA-based tooling was studied using coupled thermo-mechanical finite element method analysis. A systematic approach has also been developed for analyzing the manufacturability of parts produced by powder-based direct RT processes, in terms of flatness tolerance (37). In that study, build direction feasibility regions were constructed over a sphere and then intersected to determine the common feasibility region, which should not be empty for better manufacturability. Most of the studies discussed here are specific to a particular AM and RT process, and they do not provide any general framework for manufacturability evaluation for a range of processes. The RT processes vary widely in terms of fabrication principle, material, and process chain. This problem can be overcome by a multicriteria decision-based manufacturability evaluation approach, supported by an accurate database of process capabilities and appropriate cost models. Recently, Nagahanumaiah et al. (38,39) developed a computer-assisted integrated framework to assist toolmakers for the prioritization of tooling requirements, selection of the appropriate RT process, and evaluation of tooling manufacturability by the selected RT process.

10.13.5.1 Overall Methodology A rich picture of mold development using RT processes, explaining various inputs and expected results in different steps, is shown in Figure 11. Here, a hybrid approach combining QFD and AHP has been developed for RT process selection, and a fuzzy-analytic hierarchy process (fuzzy-AHP)-based approach has been used for RT manufacturability evaluation. This framework has three steps. The first step involves analyzing the customer (tooling) requirements and determining their relative importance, considering attributes related to part material, geometric features, mold material, and production quantity by pairwise comparison using AHP. The second step involves selecting an appropriate tooling process using QFD, based on the process capability mapping against each prioritized tooling requirement. In the third step, the manufacturability of the mold is evaluated, considering the process capabilities against the prioritized tooling requirements using fuzzy-AHP-based decision methodology. The proposed approach decomposes the complexity of the overall problem into three simpler steps, making it easier to implement. It also allows experienced tool engineers to capture their knowledge and improve decision making by means of assigning or adjusting the weights. If the selected process is not available, the user can carry out iterations after adjusting the relative importance of tooling requirements.

10.13.5.2 Tooling Requirements Prioritization In new product development, where RT finds wider applications, the customer desires to produce the functional prototypes costeffectively within a short time. The dimensional accuracy, surface finish, mold strength, and flexibility to accommodate changes in feeding systems are becoming equally important in functional-prototype and low-order production. Since RT processes may not completely fulfill all the requirements, and conventional mold-making practice involves significant time and cost, it becomes necessary to compromise some of the requirements depending on the type of application. This leads to a debate between the toolmaker and product manufacturers (the tooling customer) related to tooling accuracy, life, material, and cost expectations. This has been alleviated by developing an AHP-based requirements analysis that determines the importance of the requirements by comparing the effects of the tooling attributes in a hierarchical manner. The AHP methodology involves (1) identifying tooling requirements and attributes, (2) developing a hierarchical structure of the decision problem, (3) determining the relative priorities of tooling requirements against each attributes by pairwise comparison, (4) checking the consistency of pairwise comparisons, and (5) calculating each requirement’s priority weight. Figure 12 shows the sequence of steps, and these are discussed in Section 10.13.5.2.1.

10.13.5.2.1

Tooling Requirements

Major requirements in mold making include better dimensional accuracy, good surface finish, low cost, short lead time, mold strength, and ease of modifying the feeding systems (changing gate and runner sizes, shapes, and positions). Their importance and desired quality characteristics in typical injection-molding applications are discussed here. Injection molding of thermoplastics is regarded as net shape manufacturing that is capable of producing parts with an accuracy of
0.02 mm. It is therefore expected that RT processes should produce even more precise molds for use as production molds. The mold accuracy expectations in injection molding are compared with other NNS manufacturing processes in Table 2. Similarly, the expected surface finish (Ra) varies from 0.2 to 0.8 mm depending on the product design and its applications. While RT processes have reduced the injection mold lead time by 50% or more, they are not cost competitive in many cases, owing to improper process selection and mold design incompatibility. Lower accuracy and inferior mold strength (shorter mold life) have further aggravated the situation. Mold strength, life, and flexibility are directly related to the properties of the mold

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Figure 11

Rich picture of rapid tooling development.

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Figure 12

359

Steps in calculating weights for tooling requirements using AHP. Table 2

Accuracy requirements for different dies and molds (in mm)

Types of molds

Typical dimensional error

Typical form error

Injection molds Forging dies Die-casting dies Stamping dies

0.020 0.028 0.046 0.061

0.015 0.023 0.041 0.043

material, and it is always expected that the injection mold material should have better thermo-mechanical properties to withstand cyclic loads in an injection-molding cycle. However, many RT materials are quite inferior in strength and have other issues, including porosity and differential shrinkage. Such materials impose constraints on high injection pressure, temperature, injection speed, and clamping forces, which would reduce the quality of the part being produced. In summary, RT processes fail to meet all of the requirements of the tool maker; however, they show huge potential for application in injection molding with appropriate decisions on tooling requirements, process selection, and manufacturability with respect to RT process capabilities. The decisions on tooling requirements are driven by the tool design attributes, and it has been analyzed prior to the process selection so that the process can be selected to meet the desired (prioritized) requirements. The important tooling attributes and their effects on tooling requirements are discussed throughout the remainder of this subsection.

10.13.5.2.2

Tooling Attributes and Their Issues in RT Molds

Tooling requirements prioritization is closely linked with the attributes related to part material, mold geometry, mold design, and construction. The role of these factors is briefly outlined here. Part material: The molding material plays an important role in achieving the identified tooling requirements. Considering injection mold development, each plastic material possesses different molding characteristics. For example, nylon is more rigid and

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Table 3

Effects of important mold attributes on tooling requirements

Tooling attributes

Cost

Lead time

Accuracy

Surface finish

Mold strength

Flexibility

Molding material Mold size Minimum gap Parting surface Side cores Mold material Built-in cavity Split cavity Number of cavities Order quantity Effects (mapping criteria)

1 9 5 (1) 9 (7) 9 3 (7) 5 9 7 (5) 7 [Increases

3 7 7 (1) 7 (3) 9 5 5 9 9 (5) 7 [ Increases

9 7 9 7 (9) 9 5 (9) 3 9 5 (7) 5 Y Decreases

3 3 5 7 7 5 (9) 3 3 3 3 Y Decreases

1 7 9 3 7 9 9 9 3 9 Y Decreases

1 1 9 9 9 9 9 3 9 1 Y Decreases

requires more ejection force compared to polystyrene and other flexible plastics. Similarly, low-density polyethylene is more prone to shrink marks in comparison to other rigid plastics like nylon. Shrinkage of individual plastics varies with thermal conductivity of the RT mold material, which ultimately effects part accuracy. Mold geometry: The important geometry attributes include mold size, minimum gap (minimum wall thickness of part), core size, number of side cores, and shape complexity. For example, it is economical to design the mold such that the cores are in line with the mold draw direction. The presence of side cores increases mold cost by 15–20%. Moreover, side core mounting is a major problem in RT due to their lower machinability. Increasing the mold complexity narrows the choice of processes, increases the cost, and reduces accuracy and flexibility. Mold design and construction: In injection molding, the core and cavity inserts are designed in such a way that they can be manufactured easily, while maintaining accuracy and other quality attributes. In conventional mold making, simple cavities are preferred to be made as a single piece, and more complex and deep cavities are generated using split inserts. In RT processes, generating a complex cavity is relatively easy, but finish machining is usually inevitable and may have size limitations. Hence, splittype mold construction is usually preferred for larger molds manufactured by RT processes. Moreover, machining of inserts for ejector holes, guides, and finishing of cavity also contributes significantly to cost and accuracy. Production attributes: The production attributes that play a significant role in process selection are order quantity and production rate. These ultimately determine the feasibility of meeting the desired requirements from a specific RT process. Order size and production rate have a direct influence on process selection, and in turn on mold cost and lead time. Very few studies have focused on establishing these data on the effects of important tooling attributes on identified requirements, even in conventional mold-making practices. Based on the authors’ experience, these effects on a few important tooling requirements are mapped to a scale representing values of 1 (very low), 3 (low), 5 (medium), 7 (high), and 9 (very high) for conventional tooling (Table 3). In the case of RT, slight variations are found in their effects. For example, a smaller gap (thinner walls of component) increases the mold-manufacturing cost in machined molds, whereas in RT process its generation is relatively simple. The corresponding impact values applicable to RT are represented inside parentheses wherever they are different. In summary, an RT process selection methodology has to address two aspects. First, how are these requirements (indicated by customers) important for a particular project, and which requirements can be compromised if necessary? Second, how are RT processes capable of meeting these requirements? Driven by these aspects, the QFD–AHP approach has been developed for RT process selection.

10.13.5.2.3

Structuring the Attributes

As discussed in Section 10.13.5.2.2, more than 10 tooling attributes related to part material, mold material, mold geometry, mold design, and mold construction and production have been identified. Figure 13 shows the hierarchical structure of tooling requirements and attributes. l

Part material-related attributes: plastic material, shrinkage, injection temperature, injection pressure, and viscosity. Geometry-related attributes: mold size, minimum gap, core size, number of cores, thin ribs, dimensional tolerance, and shape complexity. l Mold design- and construction-related attributes: parting surface, number of side cores, position of side cores, and built-in or split cavity. l Production-related attributes: order quantity, production rate, and functional-prototype mold or production mold. l

10.13.5.2.4

Determining Relative Priorities and Weights

The determination of weights involves the construction of a square matrix in which the set of attributes are compared with each other. Each entry of the matrix (called a ‘pairwise comparison matrix’) represents the comparison between two attributes

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Figure 13 Hierarchal structure of tooling requirements and attributes. Reproduced from Nagahanumaiah. Rapid Tooling Process Selection and Manufacturability Evaluation for Injection Molding. PhD Thesis, Indian Institute of Technology Bombay, 2006.

Table 4

AHP scale of relative importance

Intensity of importance

Definition

1 3 5 7 9 2, 4, 6, 8 Reciprocals

Equal importance Moderate importance Essential or strong importance Very strong importance Extreme importance Intermediate values If attribute i has one of the above numbers assigned to it when compared to attribute j, then j has the reciprocal value when compared with i

Reproduced from Saaty, T. Highlights and Critical Points in the Theory and Application of the Analytic Hierarchy Process. Eur. J. Oper. Res. 1994, 74 (3), 426–447.

corresponding to the respective row and column. Assuming n attributes, the pairwise comparison of attribute i with attribute j yields a square matrix of attributes called A1nn as shown in eqn [1]. Here, aij is the element in the pairwise comparison matrix that gives the comparative importance of attribute i with respect to attribute j. In matrix A1, aij ¼ 1 when i ¼ j and aji ¼ 1/aij. Table 4 shows the scale for different degrees of importance as suggested by Saaty (40). 2

A1nn

a11 6 a21 ¼6 4. an1

a12 a22 . an2

.. .. .. ..

3 a1n a2n 7 7 . 5 ann

[1]

A set of weights W1, W2,., Wn associated with each of the attributes is determined by calculating the geometric mean (GMi) of the ith row, as given in eqn [2], and then obtaining the relative weights of each attribute by normalizing geometric means of rows in the comparative matrix. 2 GMi ¼ 4

n Y j¼1

31=n aij 5

and

Wi ¼ GMi =

n X i¼1

GMi

[2]

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Table 5

Random index for matrix

Order of matrix

1

2

3

4

5

6

7

8

9

10

Random index (RI)

0.00

0.00

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

Reproduced from Saaty, T. Highlights and Critical Points in the Theory and Application of the Analytic Hierarchy Process. Eur. J. Oper. Res. 1994, 74 (3), 426–447.

10.13.5.2.5

Checking the Consistency

As weight calculation involves a number of pairwise comparisons of two attributes, there is a possibility of inconsistency in the judgments during comparison. Inconsistency in the pairwise comparison has been determined by calculating the maximum Eigenvalues. For this purpose, matrices A3 and A4 are determined, such that A3 ¼ A1  A2 and A4 ¼ A3/A2, where: A2 ¼ ½w1 w2 w3 .wn T : Then the maximum Eigenvalue (lmax), which is the average of matrix A4, is determined. From the maximum Eigenvalue, the consistency index (CI) and consistency ratio (CR) of comparison are calculated, as given by eqns [3] and [4]. Consistency index; ðCIÞ ¼ ðlmax  1Þ=ðn  1Þ

[3]

Consistency ratio; ðCRÞ ¼ CI=RI

[4]

where RI ¼ the value of the random index, which has been selected based on the number of criteria used in decision making from Table 5. Usually, a CR of 0.10 or less is considered acceptable; otherwise, it would bias the result by a considerable margin, and the comparison would need to be repeated more carefully. Calculation of the CR is very helpful where a large number of pairwise comparisons are to be done in a single matrix. Similarly, the relative weights of tooling requirements can be calculated for each attribute, followed by a pairwise comparison between attributes. Finally, the priority weights are identified for tooling requirements by adding the multiples of corresponding relative weights and attributes.

10.13.5.3 The QFD–AHP Approach for RT Process Selection Figure 14 shows a pictorial representation of the proposed QFD–AHP approach. An appropriate RT process to meet the prioritized tooling requirements is selected using QFD. This involves identification and representation of RT processes, retrieval of the degree of importance of the tooling requirements, mapping the process capability against each requirement, establishing the co-relationship coefficient between the processes, and normalizing the QFD relationship matrix to determine the technical capability index (TCI) of different RT processes.

10.13.5.3.1

Identifying RT Processes

As discussed in this chapter, there are more than 25 RT processes for injection-molding applications, but many of them are not very well established. Table 6 lists a few of the RT processes and their materials. The mold designer can shortlist suitable options based on the mold material for a specific project. This preselection minimizes the analysis time and avoids inconsistency, which is mainly caused by correlation weights.

10.13.5.3.2

Determining Process Capability Weights

In a QFD relationship matrix, the process capabilities of RT processes against each tooling requirement (ri,j) are mapped using a scale representing values of 1 (poor), 3 (fair), 5 (good), 7 (very good), and 9 excellent, respectively. This is direct and one-to-one mapping, and the process capability weights are calculated using if-then rules. In this study, a process capability database for a few important RT processes has been developed through experimental studies and from the reported literature. This feature-based database has been represented by three values representing the lowest, medium, and higher limits. The if-then rules use these quantitative data to calculate the process capability weights against a specific requirement. These weights are initially calculated at the feature level, and the average of them is used to map the net process capability of a specific RT process in the QFD relationship matrix. Presently, these rules are generated by taking the conventional mold-making capabilities (CNC-machined molding) as a reference capability. However, this methodology provides flexibility for the user to change the rules (if-then rules used for process capability mapping) to match their specific requirements. Accuracy: The accuracy of different mold features produced from a few important RT processes is represented by fuzzy triangular numbers mapped to [0, 1]. The accuracy that corresponds to the optimal performance (i.e., it corresponds to fuzzy mapping weight 1) is considered to determine the weights (Table 7). The mapping rules used in accurate weight estimation are: If accuracy Dd  20 mm, then process capability weight WRT ¼ 9 (excellent). If Dd ¼ 20–40 mm, then WRT ¼ 7 (very good). If Dd ¼ 40–100 mm, then WRT ¼ 5 (good). If Dd ¼ 100–200 mm, then WRT ¼ 3 (fair). If Dd > 200 mm, then WRT ¼ 1 (poor). The mean of the individual weights calculated for different features is used to represent the process capability of a specific process.

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Figure 14 The QFD–AHP approach for RT process selection. Reproduced from Nagahanumaiah; Ravi, B.; Mukherjee, N. P. Rapid Hard Tooling Process Selection Using QFD-AHP Approach. J. Manuf. Technol. Manage. 2006, 17 (3), 332–350.

Important rapid tooling processes and their materials

Table 6 No.

Tooling processes

Mold material

1 2 3 4

SLA direct-AIM SLS direct mold DMLS direct mold RP-investment cast mold (RP-IC) Spray metal mold (electric arc spray)

Epoxy resin mold fabricated layer-by-layer by photo-polymerization of resin Carbon steel mold fabricated layer-by-layer by sintering of polymer-coated powder, followed by copper infiltration Cu–Ni bronze mold fabricated layer-by-layer by liquid phase sintering of powder Aluminium or steel cast mold, using AM patterns as expendable patterns in investment casting.

5

Table 7 Features Round hole

Fabricated by spraying nonferrous material (Cu alloys and Zn alloys) over the AM master pattern, and supported by epoxy or any other materials

RT accuracy (mm) and weights for process selection Capability and weights Conventional mold SLA direct mold SLS direct mold DMLS direct mold RP-IC mold Spray metal tooling

Capability Weights Round boss Capability Weights Square protrusion Capability Weights Square cavity Capability Weights Thin wall or rib Capability Weights Small gaps Capability Weights Overall mold Capability Weights

5 9 8 9 2 9 5 9 20 9 16 9 10 9

80 5 50 5 60 5 80 5 20 9 16 9 100 3

100 3 100 3 100 3 100 3 80 5 80 5 150 3

100 3 80 5 80 5 100 3 80 5 80 5 150 3

100 3 100 3 120 3 120 3 40 7 40 7 180 3

300 1 200 3 200 3 300 1 100 3 100 3 600 1

Reproduced from Nagahanumaiah. Rapid Tooling Process Selection and Manufacturability Evaluation for Injection Molding. PhD Thesis, Indian Institute of Technology Bombay, 2006.

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Surface finish: In AM-based tooling (RT), the surface orientation has a major influence on the surface roughness (Ra), and hence the RT process capability database related to surface roughness has been developed for different surfaces (39). The mapping rules used in surface finish weights estimation are: If If If If If

Ra  Ra ¼ Ra ¼ Ra ¼ Ra >

0.8 mm, then process capability weight WRT ¼ 9 (excellent). 0.8–1.6 mm, then WRT ¼ 7 (very good). 1.6–3.2 mm, then WRT ¼ 5 (good). 3.2–8.0 mm, then WRT ¼ 3 (fair). 8 mm, then WRT ¼ 1 (poor).

Table 8 shows the achievable surface finish and corresponding process capability weights for different types of surfaces. Form tolerance: The form tolerance for two RT processes (DMLS and SLA direct–AIM) is estimated based on the experimental studies using a region elimination search-based sampling algorithm developed by Nagahanumaiah and Ravi (36). These estimated results are compared and mapped for the purpose of RT process selection, as given in Table 9. Since a form tolerance database for the other three processes (SLS, RP-IC, and spray metal tooling) is still not available, their capabilities are estimated based on experience gained from the previous RT projects. The mapping rules used for form tolerance weights estimation are: If If If If If

form tolerance, FT  10 mm, then process capability weight WRT ¼ 9 (excellent). FT ¼ 10–50 mm, then WRT ¼ 7 (very good). FT ¼ 50–100 mm, then WRT ¼ 5 (good). FT ¼ 100–300 mm, then WRT ¼ 3 (fair). FT > 300 mm, then WRT ¼ 1 (poor).

Mold strength and life: In injection molding, common mold failures include (1) failure of mold core features under tensile load, resulting from part ejection force; (2) wear of ejector holes, resulting in flashes in a part and preventing retraction of the ejector pins, which may cause a larger indentation at either the parting surface or the mold cavity; (3) mold expansion and deflection caused by cyclic thermo-mechanical loading; and (4) surface wear resulting from relative movement between mold elements and part sliding over the core surfaces. Since quantitative data on the wear resistance and hardness properties of many RT materials are currently not available, the process capabilities weights are estimated based on tensile strength (Table 10). Table 8

RT surface finish (Ra in mm) and weights for process selection

Type of surface

Capability and weights

Conventional mold

SLA direct-AIM

SLS metal mold

DMLS direct mold

RP-IC mold

Spray metal mold

Flat horizontal

Capability Weights Capability Weights Capability Weights Capability Weights Capability Weights

0.2 9 0.2 9 0.4 9 0.4 9 0.4 9

1.0 7 1.6 5 2.0 5 1.2 7 2.0 5

8 3 8 3 10 1 10 1 10 1

8 3 8 3 8 3 8 3 10 1

0.8 7 0.8 7 1.0 7 1.0 7 1.0 7

1.4 7 1.4 7 1.6 5 1.6 5 1.6 5

Flat vertical Inclined Circular horizontal Circular vertical

Reproduced from Nagahanumaiah. Rapid Tooling Process Selection and Manufacturability Evaluation for Injection Molding. PhD Thesis, Indian Institute of Technology Bombay, 2006.

Table 9

Rapid Tooling form tolerance and weights for process selection

Form tolerance

Capability and weights

Conventional mold

SLA direct-AIM

SLS direct mold

DMLS direct mold

RP-IC mold

Spray metal tooling

Straightness

Capability Weights Capability Weights Capability Weights

10–20 9 20–50 9 40–50 9

58 5 123 5 1346 1

45–65 5 100 5 1532 1

37 7 86 5 1532 1

146 3 360 1 690 1

>200 1 >500 1 NA 1

Flatness Circularity

Table 10

RT mold strength (tensile strength ¼ TS) weights for process selection

Capability and weights

Conventional mold

SLA direct-AIM

SLS direct mold

DMLS direct mold

RP-IC mold

Spray metal tooling

RT material TS (MPa) Weights

Mold steel 950 9

Epoxy 40 1

Carbon steel 475–580 7

Cu–Ni bronze 20–100 5

Al/Steel 570 7

MCP 400 58 3

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Mold cost and lead time: The cost and lead time comparisons reported by Ainsley and HaiQing (41) for different RT processes are adopted here to map their respective capabilities. The rules for cost weights calculation are designed based on the cost factors of RT (the ratio of RT cost to conventional mold cost). The mapping rules used in cost weight calculation are: If If If If If

RT cost factor CRT < 0.30, then low-cost weight WRT ¼ 9 (excellent). CRT ¼ 0.3–0.5, then WRT ¼ 7 (very good). CRT ¼ 0.5–0.8, then WRT ¼ 5 (good). CRT ¼ 0.8–1.0, then WRT ¼ 3 (fair). CRT > 1.0, then WRT ¼ 1 (poor).

Table 11 gives a cost comparison between RT and conventional molds, and the respective weights. Similarly for lead time, process capability weights shown in Table 12 are calculated using the following rules: If If If If If

RT lead time factor T < 0.10, then lead time weight WRT ¼ 9 (excellent). T ¼ 0.1–0.4, then WRT ¼ 7 (very good). T ¼ 0.4–0.6, then WRT ¼ 5 (good). T ¼ 0.6–0.8, then WRT ¼ 3 (fair). T > 0.8, then WRT ¼ 1 (poor).

10.13.5.3.3

Determining the Correlations between RT Processes

While a number of RT processes have been developed, indirect RT processes originate from AM processes, which might also be capable of producing the RT mold. Similarly, RT molds need finish machining for most applications. These interdependency needs are to be estimated to a known scale of 0.9 (representing very low), 0.7 (low), 0.5 (medium), 0.3 (high), and 0.1 (very high) dependencies, respectively. These correlations between RT processes are represented by a co-relational coefficient (gj,k) in a corelationship matrix of QFD, which has also been considered in normalizing the matrix and in turn has an influence on calculation of the RT process capability index for suitable RT process selection. High interdependency among processes is considered undesirable because in such multistage RT processes, it is very difficult to establish the true process capabilities and co-relationship weights. Rapid mold fabrication and controlling the effects of these variables on RT mold accuracy often lead to more iterations and longer lead times.

10.13.5.3.4

Normalization of the QFD Matrix

QFD consists of two matrices. In RT process selection, coefficients (ri,j) of the relationship matrix represent process capabilities, and coefficients (gj,k) of the co-relationship matrix represent the interdependency between processes in terms of resources and fabrication sequence. Therefore, we employed a Wesserman method for normalization of the relationship matrix, which considers the coefficients of both the relationship matrix and the co-relationship matrix. The coefficient of the normalized matrix is given by eqn [5] (42). Table 11

RT cost weights for process selection

Tooling process

Cost units reported by Ainsley and HaiQing (in £) (41)

RT mold cost or conventional mold cost

RT cost weights

Conventional mold SLA direct-AIM SLS direct mold DMLS direct mold RP-IC mold Spray metal mold

4800 2000 6500 – 2000 4000

1 0.42 1.35 0.65a 0.42 0.83

3 7 1 5 5 3

a

Cost of the DMLS mold was not reported in Ainsley, et al.; however, it has been found in earlier projects that the DMLS cost varies from 0.45 to 0.80 times the conventional cost.

Table 12

Rapid tooling lead time weights for process selection

Tooling processes

RT lead time reported by Ainsley (in weeks) (41)

Time multiples or fractions

RT lead time weights

Conventional mold SLA direct-AIM SLS direct mold DMLS mold RP-IC mold Spray metal mold

8 0.5 2 – 4 2

1 0.06 0.25 0.20a 0.50 0.25

1 9 7 7 5 7

a

Lead time of the DMLS mold was not reported in Ainsley, et al.; however, it has been found in earlier projects that the DMLS lead time varies from 1 to 2 weeks.

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m j¼1

 ri;j $gk;j  Pm  k¼1 ri;j $gj;k

k¼1

[5]

where: ri,j ¼ coefficient of relationship matrix gj,k ¼ coefficient of correlation matrix i ¼ rows of relationship matrix j ¼ columns of relationship matrix k ¼ columns of co-relationship matrix norm is given by For example, the coefficient of the normalized matrix r3;1  P5  r3;k $gk;1 norm   ¼ P k¼1 r3;1 P5 5 j¼1 k¼1 r3;j $gj;k

[6]

norm is given by If QFD consists of five columns (RT processes), then r3;1 norm ¼ r3;1

r3;1 $g1;1 þ r3;2 $g2;1 þ r3;3 $g3;1 þ r3;4 $g4;1 þ r3;5 $g5;1           r3;1 g1;1 þ . þ g1;5 þ r3;2 g2;1 þ . þ g2;5 þ r3;3 g3;1 þ . þ g3;5 þ r3;4 g4;1 þ . þ g4;5 þ r3;5 g5;1 þ . þ g5;5

10.13.5.3.5

Selection of the Best Process

Calculate the weighted sum of each column (representing a process) to obtain the TCI of each process to meet the set of desired requirements. Technical Capability Index ¼

n X

Ri rijnorm

[7]

1

where: Ri ¼ importance weight of tooling requirement i, norm ¼ coefficient of normalized QFD matrix. ri;j Then, the RT process that has the highest TCI will be considered as the best process among others. The steps involved in RT process selection are summarized as follows: 1. Building a QFD chart with feasible RT processes as columns and product requirements as rows. 2. Representing the relative importance of tooling requirements, derived from the AHP analysis in phase 1, against each requirement in a column. 3. Mapping the capability of each RT process against each product requirement (ri,j) using a scale (with values of 1 (poor), 3 (fair), 5 (good), 7 (very good), and 9 (excellent)) to complete the relationship matrix of QFD. 4. Building the co-relationship matrix above the relationship matrix, considering interdependency between pairs of tooling fabrication processes. These correlations (gj,k) are mapped using a scale representing 0.9 (very low), 0.7 (low), 0.5 (medium), 0.3 (high), and 0.1 (very high) dependencies, respectively. High interdependency among processes is considered undesirable. 5. Normalizing the rows of the relationship matrix using the Wesserman method, which considers the coefficients of both the relationship matrix and the co-relationship matrix. 6. Calculating the weighted sum of each column (representing a process) to obtain the TCI or capability of each process for a given (prioritized) set of tooling requirements. The process with the highest TCI value is considered the best. This QFD–AHP RT process selection approach identities RT processes in the order of their suitability against a set of tooling requirements, which are also prioritized based on the functional and quality attributes of a mold. The selected process is further studied in the third step to identify the critical process variable for the same set of requirements, prior to mold manufacturability evaluation. The process capability database, in terms of representation scheme and accuracy of values, plays an important role in process selection, planning, and manufacturability evaluation problems (43). Campbell et al. (44) emphasized and demonstrated the need to develop an AM database driven by the geometric features of the part. In most of the benchmarking studies, a new process is compared with conventional practice or a limited number of other RT processes, and each such study uses a different part. One study explored the effects of feature geometry on the accuracy, surface finish, cost, and lead time characteristics of four different AM processes – SLA, SLS, LOM, and FDM (45) – which are useful for AM process selection. Ainsley and HaiQing (41) provided a comparative evaluation of eight different RT processes in terms of cost and lead time competitiveness. In injection molding, form tolerances are important to achieve net shape parts. The least squares method is widely used to estimate form tolerances in the machining domain, but it requires a large amount of a coordinate-measuring machine’s measurement data. One way to reduce the number of experimental samples is by using an adaptive search-based sampling algorithm (46). Here, the sampling points are selected based on the manufacturing error patterns determined from an initial set of

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measurements, and form tolerance for RT processes has been developed by Nagahanumaiah and Ravi (47). Also, there appears to be no attempt at developing a feature-based RT process capability database on quality attributes (accuracy, surface finish, and geometric compatibility) and mold life attributes (wear resistance and mechanical properties).

10.13.5.4 Manufacturability Evaluation In general, a manufacturability evaluation involves measurement of the relative ease of manufacturing, using documented design guidelines that are generated in the company or hard coded by the external researchers. Part manufacturability evaluation indicates whether a part can be manufactured with acceptable manufacturing quality, cost, and time. Manufacturability evaluation of NNS processes involves manufacturability of tooling (dies and molds) also, in addition to the part. The primary goal of this evaluation is to determine mold geometric features that are difficult to manufacture by a selected RT process. It involves evaluating the features of functional mold elements (core, cavity, and side core) using fuzzy-AHP methodology, checking the secondary elements (parting surface, ejectors, multicavity mold, and cooling lines) for compatibility with RT mold inserts, and estimating the RT cost-effectiveness. This enables converging to the best combination of mold design and RT process, which in turn results in overall cost reduction while achieving the desired quality (Figure 15) (48). The steps involved in RT manufacturability evaluation are detailed here: l

Manufacturability of functional elements B Identifying the quality attributes of the mold to be considered for evaluation B Determining the manufacturability weights (fuzzy scale) using decision tables B Fuzzy operations and ranking in fuzzy-AHP to determine problem features for manufacturing by a selected RT process l Secondary elements design compatibility B Determining the compatibility of the parting design, multicavity, cooling line, and ejectors with properties of the selected RT mold using RT process knowledge l Cost-effectiveness of the RT process B Cost modeling of RT and conventional molds to estimate the percentage saving in cost These three results are finally used to evaluate RT molds for manufacturability, which can help the mold designer to improve the mold design and/or process selection, if necessary. The manufacturability of functional features, compatibility of secondary elements, and cost-effectiveness of selected RT methods are discussed in the remainder of this section.

Figure 15 RT manufacturability evaluation approach. Reproduced from Nagahanumaiah; Ravi, B.; Mukherjee, N. P. Rapid Tooling Manufacturability Evaluation Using Fuzzy AHP Methodology. Int. J. Prod. Res. 2007, 45 (5), 1161–1181.

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10.13.5.4.1

Primary Manufacturability Evaluation

This is carried out for the geometric features of the cavity, core, and side cores (hole, boss, slot, rib, etc.), which are directly affected by RT process capabilities. This involves evaluating the capabilities of a given RT process with respect to the quality requirements, using a fuzzy-AHP methodology. The features that are difficult to manufacture are identified through principles of ranking triangular fuzzy numbers (49). The steps of primary manufacturability evaluation are described in Section 10.13.5.4.2.

10.13.5.4.2

Quality Criteria for Manufacturability Evaluation

Manufacturability of mold (core and cavity) geometric features by a selected RT process can be evaluated based on quality attributes such as geometric compatibility (minimum section thickness, maximum size, and shape), dimensional accuracy, and surface finish. Therefore, a database of RT process capabilities in terms of the three quality criteria (geometry compatibility, dimensional accuracy, and surface finish) for different types of tooling features (hole, boss, rib, etc.) and types of surfaces has been established for five RT processes and conventional tooling. These capabilities of RT processes are represented by triangular fuzzy numbers (the lower, modal, and upper limits), which are used for determining the relative difficulty of manufacturing each feature in an AHP pairwise comparison. In feature manufacturability evaluation, the desired parameter (the feature size or accuracy or the surface finish) of a feature is compared with the achievable capabilities (normal and limits on either side) to determine their manufacturability weights, which are discussed in Section 10.13.5.4.3.

10.13.5.4.3

Determining the Relative Manufacturability Weights

In fuzzy-AHP, the manufacturing difficulties of specific geometric features are compared pairwise and have been represented by six levels of manufacturing difficulty: just equal, low difficult, medium difficult, high difficult, very high difficult, and absolutely more difficult. The fuzzy numbers shown in Table 13 are used to rank the manufacturing difficulty of m features (F1, F2,., Fm) against n quality criteria (C1, C2,., Cn). In this methodology, decision tables have been designed to ascertain the manufacturing difficulty of various mold features produced by a specific RT process. These tables use the capabilities of RT processes, and the relative weights of the manufacturability of two features have been determined based on predetermined conditions built into decision tables. These weights are used in a pairwise comparison of feature manufacturability in fuzzy-AHP under each criterion. The RT process database related to dimensional accuracy, form tolerance, surface finish, and geometric compatibility is used here to establish manufacturability decision rules. For example, the relative weight for the manufacturability of two features F1 and F2 under the objective criterion C1 is determined by constructing two decision tables (Tables 14 and 15). The steps are described here. Let the corresponding process capabilities represented by triangular fuzzy functions for features F1 and F2 be (l1, m1, u1) and (l2, m2, u2), respectively. Initially, the process capability represented in a fuzzy scale is used to determine the feature manufacturability weights (R1 and R2) using a decision table (Table 14). The relative weights for fuzzy-AHP pairwise comparison have been determined using the calculated weights R1 and R2 in another decision table (Table 15). If R1 > R2 (calculated in Table 14) and decision rule 3 of the decision table in Table 15 satisfies for the specific example, then it has been stated that the F1 manufacturing difficulty is relatively ‘high’ compared to that of F2, and hence the corresponding weights to be used for AHP comparison are (3/2, 2, 5/2). Table 13

Triangular fuzzy scales used in AHP pairwise comparison

Linguistic scale for difficulty

Triangular fuzzy scale

Reciprocal triangular fuzzy scale

Just equal Low difficult Medium difficult High difficult Very high difficult Absolutely more difficult

1, 1, 1 1 /2 , 1, 3/2 1, 3/2, 2 3/2, 2 5/2 2, 5/2, 3 5/2, 3, 7/2

1, 1, 1 2/3, 1, 2 1 /2 , 2/3, 1 2/5, 1/2 , 2/3 1/3, 2/5, 1/2 2/7, 1/3, 2/5

Reproduced from Chang, D. Y. Theory and Methodology: Applications of the Extent Analysis Method on Fuzzy AHP. Eur. J. Oper. Res. 1996, 95, 649–655.

Table 14

Decision table for feature manufacturability weights

Manufacturability criteria (conditions) Is size compatible? Dimensional accuracy Weights (actions) Qualitative terms Crisp weights (Ri)

Rules 3 Yes mi Action entries Absolutely more difficult Very high difficult Equal (normal) 0 0.2 1

1 Yes
2 Yes li to mi

4 5 Yes Yes (mi
10 mm) mi to ui Low difficult 0.8

6 Yes ui

Medium difficult High difficult 0.6 0.4

Reproduced from Nagahanumaiah. Rapid Tooling Process Selection and Manufacturability Evaluation for Injection Molding. PhD Thesis, Indian Institute of Technology Bombay, 2006.

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Table 15

Decision table for relative weights for the manufacturability of two features

Relation between crisp weights (conditions stubs) Lower Ri and higher Ri Relative weight Feature with higher Ri is _____ than (or to) that of lower Ri Fuzzy weights

1 Up to 0.2

Very high difficult

Rules 3 4 0.4–0.6 0.6–0.8 Action entries High difficult Medium difficult

2, 5/2, 3

3/2, 2 5/2

2 0.2–0.4

Absolutely more difficult 5/2, 3, 7/2

1, 3/2, 2

5 0.8–1.0

6 1

Low difficult

Equal

1/2, 1, 3/2

1, 1, 1

Reproduced from Nagahanumaiah. Rapid Tooling Process Selection and Manufacturability Evaluation for Injection Molding. PhD Thesis, Indian Institute of Technology Bombay, 2006.

Table 16

F1 F2 F3

10.13.5.4.4

Representation of a fuzzy-AHP decision matrix C1 (w1a, w1b, w1c)

C2 (w2a, w2b, w2c)

C3 (w3a, w3b, w3c)

Manufacturability index

(l11, m11, u11) (l21, m21, u21) (l31, m31, u31)

(l12, m12, u12) (l22, m22, u22) (l32, m32, u32)

(l13, m13, u13) (l23, m23, u23) (l33, m33, u33)

(l1, m1, u1) (l2, m2, u2) (l3, m3, u3)

Identifying Difficult-to-Manufacture Features

The principles for calculating the final priorities in fuzzy-AHP for two fuzzy functions (in this case, mold features) F1 and F2, with triangular fuzzy numbers F1 ¼ (l1, m1, u1) and F2 ¼ (l2, m2, u2), are as follows: For addition : ðF1 þ F2 Þ ¼ ðl1 þ l2 ; m1 þ m2 ; u1 þ u2 Þ

[8]

For multiplication : ðF1 F2 Þ ¼ ðl1  l2 ; m1  m2 ; u1  u2 Þ

[9]

For reciprocal : F11 ¼



1 1 1 ; ; u1 m1 l1

 [10]

The final fuzzy-AHP decision matrix representation is shown in Table 16. Here, (l11, m11, u11), (l12, m12, u12), and (l13, m13, u13) are the normalized fuzzy vectors of features F1, F2, and F3 for the criteria C1, C2, and C3. Similarly, (l1, m1, u1), (l2, m2, u2), and (l3, m3, u3) represent the final priorities of the features F1, F2, and F3. Features with low manufacturability are identified through the principle of comparison of fuzzy numbers in fuzzy-AHP, as shown in Figure 16. Let mi(x) denote the membership function for fuzzy number Fi: h  i for all i; j ¼ ð1; 2; 3; .; mÞ [11] eij ¼ max min mmi ðxÞ; mmj ðyÞ xy

When a pair (x, y) exists such that x  y and mmi ðxÞ ¼ mmj ðyÞ ¼ 1, then we have eij ¼ 1. The probability of Fj  Fi is given by eji ¼

l1  u2 : ðm2  u2 Þ  ðm1  l1 Þ

[12]

The condition Fi  Fj will be true if and only if eij ¼ 1 and eij < Q, where Q is the sensitivity factor, less than one. A value of 0.8 or 0.9 for Q is considered appropriate (50).

Figure 16

Ranking of three triangular fuzzy priority numbers: F1, F2, and F3.

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Parting surface compatibility index

Table 17 RT processes

Straight

Multilevel

Angular

Freeform

Conventional tooling SLA-AIM SLS direct mold DMLS Investment casting Spray metal tooling

1.0 0.8 0.8 0.9 1.0 0.6

0.9 0.5 0.7 0.8 0.8 0.4

0.6 0.4 0.5 0.5 0.5 0.4

0.7 0.5 0.5 0.6 0.5 0.3

Reproduced from Nagahanumaiah; Ravi, B.; Mukherjee, N. P. Rapid Tooling Manufacturability Evaluation Using Fuzzy AHP Methodology. Int. J. Prod. Res. 2007, 45 (5), 1161–1181.

10.13.5.4.5

Secondary Mold Elements Compatibility

While the functional elements of the mold (core and cavity inserts) are being manufactured by RT methods, there is a trend toward standardization of secondary elements (mold base, ejectors, slides, etc.) to expedite mold development activity in a tool room. The secondary elements must, however, be compatible with the accuracy, machinability, strength, and other characteristics of the functional elements produced by RT methods. The compatibility is evaluated in terms of the parting surface design, multicavity mold, cooling design, and ejector design, which are mapped to a 0–1 scale to facilitate overall evaluation.

10.13.5.4.6

Parting Surface

The parting surface is the most important decision in mold design, and it is driven by factors like undercuts, aesthetic requirements, accuracy, surface finish, cost competitiveness, and ease of assembly (51). Parting surfaces can be straight or complex; the latter includes stepped (multilevel), angular, and freeform surfaces. Complex parting surfaces can be more easily produced using RT methods than conventional machining, but the accuracy and surface finish are poor. It is also difficult to finish machine a complex parting surface of an RT mold insert. For processes like SLA direct–AIM that produce nonmetallic molds, finish machining to ensure proper mating between two halves is extremely difficult, reflecting a lower compatibility value with respect to the parting surface criterion. Table 17 gives the parting surface design compatibility index based on mold material, accuracy level, and machinability.

10.13.5.4.7

Multicavity Mold

The number of cavities is decided based on the molding machine’s capacities for injection, clamping, and plasticizing, besides production order quantity. A larger number of cavities increases the complexity of RT molds and leads to size constraints, poor dimensional accuracy, and assembly mismatch. A large mold with thickness variations (e.g., 2 mm thick ribs and 30 mm mold wall thickness between cavities) will have significant dimensional errors owing to differential shrinkages. The linear error in a DMLS mold of 200 mm length is up to 1.5 mm, and for an SLA direct AIM mold, it is up to 1.2 mm. This results in mismatch between the core and cavity (core shift) that has to be rectified by machining. Unfortunately, many RT materials have poor machinability compared to conventional die steels. Finish machining of RT molds imposes difficulties related to the machining reference, cutting tool, and thermal distortion of RT material (during machining), thereby making RT molds expensive. An alternative approach would be to build separate inserts for each cavity, and house them in the same mold. The runners and gates would be machined on the wall of the housing. This gives more flexibility to toolmakers for making larger multicavity molds as well as for facilitating their maintenance. These considerations are reflected in the multicavity mold compatibility index given in Table 18.

10.13.5.4.8

Cooling Lines

The mold-cooling rate influences the grain orientation and shrinkage of plastic parts being produced. An effective cooling rate depends on the temperature gradient, the thermal conductivity of the mold, the layout of the cooling lines, and the distance between the cooling channel and the cavity. RT technologies allow a greater flexibility in designing and producing a conformal cooling circuit, which can improve the quality of molded parts. In practice, some difficulties may be encountered. For example, AM models can be used for investment casting of core cavity inserts (the RP-IC indirect RT method) incorporating conformal cooling Table 18

Multicavity mold compatibility index

RT processes

Compatibility index

Conventional tooling SLA-AIM SLS direct mold DMLS Investment casting Spray metal tooling

1.0 0.4 0.7 0.9 0.8 0.4

Reproduced from Nagahanumaiah; Ravi, B.; Mukherjee, N. P. Rapid Tooling Manufacturability Evaluation Using Fuzzy AHP Methodology. Int. J. Prod. Res. 2007, 45 (5), 1161–1181.

Indirect Rapid Tooling

Cooling lines design compatibility index

Table 19 RT processes

Compatibility index

Conventional tooling SLA-AIM SLS direct mold DMLS Investment casting Spray metal tooling

0.4 0.8 1.0 1.0 0.5 0.3

Table 20

371

Ejectors compatibility index Compatibility index

RT processes

Ejector pins

Thin blades

Ring/sleeve

Plate ejector

Conventional tooling SLA-AIM SLS direct mold DMLS Investment casting Spray metal tooling

1.0 0.3 0.8 0.6 0.9 0.5

0.9 0.3 0.6 0.5 0.7 0.2

1.0 0.3 0.7 0.5 0.9 0.1

0.9 0.3 1.0 0.8 0.9 0.5

holes; however, fettling of the corresponding ceramic cores is very difficult. These aspects are considered while assigning the compatibility indices considering cooling design criteria for different RT processes (Table 19).

10.13.5.4.9

Ejectors

The type, size, number, and location of ejectors depend on the mold geometry and aesthetic requirements of the molded part. An improper design of ejectors may lead to deformation or deflection of the part, ejector, or core. The AM molds require more ejection force because of surface unevenness. Thus, the compatibility index for ejectors depends on the machinability, surface finish, and surface hardness of the RT material (Table 20).

10.13.6

Summary

The advantage of AM-based tooling, whether developed directly or through a multistage process sequence, enables the realization of molds in a shorter lead time. The other advantages include easy and rapid fabrication of complex geometry and saving of tool material. The widespread use of injection molding for producing plastic parts to net shape, coupled with continuous pressure to reduce the lead time to market, is attracting and driving RT applications for injection mold making. This chapter discussed indirect RT processes that can be used to develop molds indirectly using AM models as masters in secondary processes. Currently, there are several promising indirect RT processes capable of producing metal RT molds that can be used in conventional injection-molding processes; their applications are shifting from low-volume production for prototypes to medium-volume production for functional parts. This imposes higher demands on the RT process in terms of accuracy, surface finish, and influence on part quality, geometric constraints, mold life, and cost-effectiveness. This is because of wide variations in the basic processes and the incorporation of several multistage process chains to develop the mold inserts. Currently, there are several aspects that are either researched or in exploratory stages, which makes the indirect RT development process chain complex, as illustrated in Figure 17. The information shown in Figure 17 is largely unknown for many RT processes, leading to difficulties in RT process selection and its manufacturability evaluation. Moreover, early evaluation of the manufacturability of a mold is needed, but detailed manufacturability analysis is usually process specific and uses heuristics, analytical methods, and plan-based methods. This creates the need to describe the computer-aided process selection and manufacturability evaluation developed by the authors in their previous research. RT process selection is based on process capability mapping against a set of tooling requirements. The tooling requirements are prioritized considering the mold development attributes through pairwise comparison using AHP. These priority ratings are used in quality function deployment for selecting the most appropriate RT process. The mold manufacturability for the selected RT process is carried out using fuzzy-AHP to identify problem features, if any. In summary, tool rooms are striving hard to be competitive; some of the RT processes that can be used for producing functional prototypes and a few batches of production after optimizing the molding parameters ease this pressure. However, selection of the appropriate RT process needs expertise, which may not be available in all tool rooms. In addition, unlike direct RT processes, several multistage process chains have been developed over the last three decades by using an AM master to develop RT molds indirectly. The process chain needs better understanding, and users demand a process capabilities database that can be retrieved, assistance in automatic selection of tooling methods, and early prediction of its success under practical molding and casting process conditions.

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Figure 17

Typical process variables associated with the indirect RT development process chain.

See also: Direct Rapid Tooling.

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