How to integrate additive manufacturing technologies into manufacturing systems successfully: A perspective from the commercial vehicle industry

Journal of Manufacturing Systems 53 (2019) 195–211

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Journal of Manufacturing Systems journal homepage: www.elsevier.com/locate/jmansys

How to integrate additive manufacturing technologies into manufacturing systems successfully: A perspective from the commercial vehicle industry

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Li Yi , Christopher Gläßner, Jan C. Aurich Institute for Manufacturing Technology and Production Systems, TU Kaiserslautern, Germany

A R T I C LE I N FO

A B S T R A C T

Keywords: Additive manufacturing Commercial vehicle industry Manufacturing systems Cost model Hybrid additive-subtractive process chain Quality management

Additive Manufacturing (AM) is the umbrella term for manufacturing processes that add materials layer by layer to create parts. AM technologies show numerous potentials in terms of rapid prototyping, tooling and direct manufacturing of functional parts and imply revolutionary benefits for the manufacturing industry. Currently, many industrial areas are marching to a more comprehensive application of AM. Hence, the development of new tools, methods, and concepts for guiding companies to implement AM technologies requires more research attention. This paper introduces the results of a research project carried out by academic and industrial partners from the German commercial vehicle industry. The research project addressed four issues for a long-term application of AM technologies: identification of barriers for AM applications, cost estimation for AM application, design of hybrid additive-subtractive process chains, and quality management with AM.

1. Introduction Additive manufacturing (AM) is the umbrella term for the manufacturing technologies that add material layer by layer to create parts [1,2]. The absence of forming or cutting tools in AM indicates that the design, manufacture, maintenance, and other application requirements for the tools are also absent in AM. As a benefit, users are able to obtain more design freedom with AM and then to use this advantage to create more benefits on the process and system level [3]. Since the first AM technology was commercialized in 1980s, many industrial areas have already applied AM technologies [4,5]. For example, the aerospace industry applied polymer AM parts for nonstructural production application in middle 1990s, and today, Boeing produced tens of thousands of unique aircraft parts with AM, and the medical industry has also adopted AM technologies to produce models, cutting and drill guides, and orthopedic implants [4]. Today, AM technologies have shown a high degree of maturity and are rapidly entering the practical application stage [6]. Hence, a transformation process towards a more comprehensive AM application across different areas is taking place [6]. In order to support this transformation, the development of new methods, concepts, and tools for guiding companies to apply AM technologies on both strategical and operational dimensions is an important research task. The commercial vehicle industry is one of the industries that are interested in wider AM applications [7]. Customers of commercial

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vehicles are usually investment organizations and have to customize vehicles according to their unique investment requirements [8]. In addition, different markets have different legal regulations and infrastructure conditions [9]. Hence, the manufacturers and suppliers have to extend their product portfolio and produce more non-standardized and personalized parts with limited batch size in order to satisfy these requirements. Consequently, they also have to establish more processes and systems for handling the product diversity and the small batch size production, and eventually, the complexity of their manufacturing systems is increased. As a promising solution, AM can help the commercial vehicle manufacturers to gain more flexibility on both product and system level. For example, on the product level, AM enables the design and manufacture of complex geometrical features by using topology optimization and lightweighting, and hence, manufacturers can design and produce more complex parts to improve the overall performance of their vehicles [10]. Moreover, tooling with AM is another important benefit that the manufacturers can use AM to produce assembling tools at a lower cost in one-off or small batch size production [6]. On the system level, AM enables cost savings and shortening of the process chain. In conventional manufacturing, an increased geometrical complexity of products results in increased manufacturing cost and longer process chains because more tools and manufacturing steps are required [11]. In AM, it is evident that the application of AM is not sensitive to the geometrical complexity of products [12,13], and hence, the process chain and manufacturing cost with AM are reduced,

Corresponding author. E-mail address: [email protected] (L. Yi).

https://doi.org/10.1016/j.jmsy.2019.09.007 Received 27 March 2019; Received in revised form 13 September 2019; Accepted 14 September 2019 0278-6125/ © 2019 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

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Fig. 1. Classification and characterization of AM processes (according to [[4,14–17]).

• Process chain: What is a general method to design a process chain

especially in terms of the small batch size production. During the transformation towards comprehensive AM applications in the commercial vehicle industry, there are still research questions to answer. With this background, a research project has been carried out to explore and to address the benefits of AM on a deeper scale. The research project involves partners from both academic and industrial area, and four basic research questions are addressed:

•

• Barriers: What are current barriers for AM applications on the

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company level? The motivation for addressing this question is that many companies have shown great interest in AM, but at the same time, they are often hesitant to apply AM in their manufacturing systems. Hence, this project first tries to explore the reasons and barriers for their AM application. Cost: What is the cost structure for AM applications? The reason for addressing this question is that companies need to calculate and to assess the manufacturing cost with AM in the decision-making phase.

with AM? The initial step to integrate AM in real manufacturing systems is to design a unique process chain with an AM process. Hence, a general method for developing a hybrid additive-subtractive process chain is called for. Quality management (QM): What is an appropriate QM strategy for AM? Since the process chains with AM are implemented, an appropriate QM system is important for a long-term product and process control with AM.

This paper introduces the results of the research project in order to help companies of the commercial vehicle industry and other industries with similar interests to overcome the challenges in integrating AM technologies. The paper is arranged as follows: Section 2 introduces the state of the art of AM; Section 3 explains the research tasks and methods; the results of the research project are respectively introduced and discussed in Sections 4–7; Section 8 summarizes the paper with a brief conclusion and outlook for the future work. 196

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2. State of the art

products with AM by analyzing the functional volumes and linking volumes of the product [22]. Schmelzle et al. carried out a case study for redesigning a hydraulic manifold by using AM [23]. Yang et al. proposed a part consolidation method including a redesign process to optimize a product by using AM, reducing the number of parts and improving the functionality at the same time [24]. Adam and Zimmer studied the suitable geometrical structures for laser sintering, laser melting and fused deposition modeling and worked out a catalog of AM design rules [25]. Soshi et al. carried out a case study for designing and manufacturing a molding tool with internal cooling channels by using an additive-subtractive hybrid machine [26]. Garden el al. applied topological optimization for internal patterns and supports with AM and addressed the design advantages of AM [27,28]. Similar works related to the product level can be further found in [29,30]. On the process chain level, Le et al. proposed design rules for process planning by combining AM and subtractive manufacturing, and validated the rules with a case study [31]. Newman et al. suggested a framework by combining additive, subtractive, and inspection processes to manufacture a new product based on a reused product [32]. In their framework, they first applied inspection process to analyze the existing geometrical features of a recycled product, and then analyzed the gaps between the existing product features and desired features of a new product. Finally, the manufacturing processes combining AM and the subtractive manufacturing were designed to fill those gaps. Zaman et al. explained an approach for process planning with AM by integrating product information in the process design phase [33]. Jin et al. proposed a framework of AM process planning to reduce resource consumption [34]. Besides works focusing on AM process planning strategies, development of new tools and systems for supporting the AM process planning has been also addressed in current literature, e.g. [35–39]. All these previous works indicate the progress of the development of new tools, methods, and concepts for supporting and accelerating the transformation process towards a comprehensive AM application. However, a more holistic discussion about how to integrate AM technologies into companies on the manufacturing system level is still called for. Hence, this paper aims at the above research background and incorporates with manufacturers and suppliers from the commercial vehicle industry to explore industrial application potentials of AM. The main contribution of this paper can be summarized in the following aspects that address the four research questions from Section 1. First, this paper has investigated and clustered the main barriers for comprehensive AM applications in the commercial vehicle industry and proposed solutions for overcoming these barriers. Second, a cost model is developed in which the specific cost factor is calculated in time reference, which improved the practical feasibility in the cost estimation with AM. Third, a generalized method for developing a hybrid additivesubtractive process chain is proposed based on four dimensions of AM technologies (product, process step, materials and information objects). The fourth contribution of this paper is the proposition of a closed-loop QM control model in which the essential QM activities with AM are described. Finally, the feasibility of the proposed methods, models and tools is confirmed in a validation case, and the results in this paper are helpful for other companies with similar questions to inspire them to find their own solutions to apply AM technologies successfully.

2.1. Classification and properties of AM processes The standard ISO 17296 defines AM as a process joining materials layer by layer to create parts and proposes seven process categories [14]. Each category is characterized by its own process characteristics, processable materials, and suitable application areas [4,14–16]. Vat photopolymerization is characterized by using a light source (e.g. UV radiation) for hardening the photo-curable materials and is the first commercialized AM process [4]. Material extrusion displaces filamentary materials or paste (e.g. plastics or structural ceramics) on a platform to create a part. Material jetting and binder jetting are similar methods because both of them use a spray nozzle for dispositioning the liquid material. The difference between them lies in the displacement mechanism. While in material jetting the construction material is directly displaced, binder jetting just displaces the bonding agent to bond the construction material. Powder bed fusion fuses a specific area of a powder bed by using a heat source, e.g. laser beam or electron beam. Directed energy deposition uses a heat source to create a melting pool and displaces material filaments or powders in the melting pool, fusing them together in layers. Sheet lamination fuses material sheets by using thermal reaction, chemical reaction or ultrasound. For rapid prototyping using polymer, ceramic or composite, vat photopolymerization, material extrusion, material jetting, binder jetting are preferred because they have a wide range of processable materials, and the machines of are usually desktop equipment with lower prices. For tooling or direct manufacturing, powder bed fusion, directed energy deposition, and sheet lamination are suitable because these processes usually apply high-power devices like laser transmitter and can be used to produce metal parts. Fig. 1 shows the summary of categories, schemas, process characteristics, materials, applications, and representative technologies of AM. 2.2. Literatures related to the integration of AM in manufacturing systems Current studies have addressed the application of AM in the commercial vehicle industry with different research perspectives. Burkhardt and Aurich modified Life Cycle Assessment (LCA) into a framework to predict and reduce the life cycle environmental impact of a commercial vehicle by using AM [18]. In their framework, the use phase of a commercial vehicle is analyzed at first, and then the parts with optimization potentials for improvement by AM are identified. Afterward, the environmental impact of the optimization is quantified and analyzed in order to support further decision-making. Yi et al. proposed a three-phase based concept for a systematical evaluation of use potentials of AM technologies for the commercial vehicle industry [19]. The first phase aims at a general comparison of AM to the conventional manufacturing, identifying whether AM has a better performance against conventional methods for manufacturing a product. The second and third phase compare and evaluate different specific AM processes from technological and economic dimensions, respectively. The criteria on technological dimension are for example the function surface of a product and the complexity of manufacturing process chain, while the criteria on the economic dimension encompass manufacturing cost and build time. Ley et al. carried out a study for optimization, manufacturing, and test of a support arm of a tank by using a combination of AM with the conventional manufacturing [20]. In their study, the authors analyzed the original design of the support arm and then applied a topology optimization. Afterward, the optimized part was manufactured with AM and tested with a special test facility. Additionally, there are also studies that do not directly mention the commercial vehicle industry but can be still applied to it. On the product level, Becker et al. pointed out the general principles for product design for AM, e.g. less number of parts and use of hollows and undercuts [21]. Ponche et al. proposed a new framework for redesigning

3. Research framework 3.1. Research tasks and methods Derived from the objective and research questions of the research project, four research tasks have been carried out, see Fig. 2. The first task is to identify the barriers that impede AM application in the commercial vehicle industry. The second task is to develop a cost model for analyzing the cost structure of AM application to support the early decision-making of companies. The third task is to develop a method for 197

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Fig. 2. Research tasks and methods.

Fig. 3. The scenario and optimization of the validation part (Source: John Deere, printed with permission).

choosing the workshop method is that it is one of the most feasible and efficient methods for studying a complex and contemporary phenomenon with limited theoretic knowledge on a current research status [40]. For handling the second, third, and fourth research tasks, the general problem-solving process is modified in order to develop the cost model, the method for designing a hybrid additive-subtractive process

designing an individual process chain with AM. The last task is to develop a quality management (QM) strategy with AM to realize a longterm product and process control of AM. For handling the first research task, a workshop is used as the research method to address the barriers for AM applications directly from stakeholders of the commercial vehicle industry. The reason for 198

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chain, and the QM control model with AM. The five steps of a general problem-solving process are: Situation analysis, objective formulation, synthesis/analysis, evaluation, and decision [41]. In addition, the developed solutions are tested by a specific validation case in order to confirm their feasibility and reliability. The validation part is provided by the agricultural machinery manufacturer John Deere, and it is described in Section 3.2. The development and the validation build the two-phase based research method, see Fig. 2. The results of the four research tasks are introduced in following sections respectively. The research tasks and methods as well as their section arrangement are illustrated in Fig. 2.

during the workshop, and they are described as follows: 4.2.1. Type I: the lack of know-how The lack of knowledge related to AM technologies was identified as one of the most essential barriers for AM applications. From the perspective of the participants, the most important but still absent AM knowledge includes the lack of methods for product and process design with AM, the lack of guidelines of product construction, the lack of the knowledge about material performance, and the lack of AM software. The design rules and CAE software for conventional manufacturing processes are no longer suitable for AM because AM has different manufacturing mechanisms and technological properties. Therefore, companies should extend and update their knowledge bases. The possible solutions to overcome this type of barriers can be the recruit of engineers with AM experiences, purchase of professional AM software, and training for employees with AM topics. On the industry sector level, the limited numbers of AM standards are also a challenging factor for companies to build their AM knowledge. Currently, two standards organizations, the technical committee 261 of the International Organization for Standards (ISO/261) and the technical committee 105 of the Association of German Engineers (VDI/ FA105), try to propose their standard series for addressing basic principle, terms, process categories, material test and other issues of AM, see [42] and [43]. Some of them are already published and others are under development. In the future, these AM standards will be worked out, and they can be used as guidelines for applying AM technologies.

3.2. Introduction of the validation part The validation part is a control lever of a gear system from agriculture machines manufactured by John Deere. The lever is used to activate the power reverser gear. The lever is about 160 mm tall, 55 mm wide, and its mass is 366 g, see Fig. 3a. While the middle hole of the lever is connected with the shaft of the gear system, the small hole on the top and the hook on the bottom are, respectively, connected with wire rope and spring. By rotating the lever, the power reverser transmission can be activated or deactivated to change the driving direction in moving forward or reversing. In order to explore and to use the design freedom of AM, John Deere applied a topology optimization for the lever. First, the load on the lever is defined according to its use condition. Second, the available design space of the lever is defined, and third, the force and deformation under the load condition is analyzed using FEM. The volume with less force and deformation is removed, see Fig. 3b. Finally, the optimized lever with lattice structure is created, whose weight is reduced from 366 g to 170 g, see Fig. 3c. The optimized lever serves as the validation part for validating the cost model, the method for designing process chains with AM, and the QM control model with AM.

4.2.2. Type II: high investment cost According to Wohlers Report, the average selling price of an industrial metal AM machine was 566,570 US dollars in 2016 and the prices of some machines with larger build rooms exceed 1 million US dollars [4]. By considering the additional cost for material, peripheral units and machine tools for post-processing etc., the high investment cost has been another important barrier type for AM application by companies. In order to overcome this barrier, the business pattern of technical Product-Service Systems (PSS) is suggested [44]. Within the framework of PSS, the companies can make a long-term contract with the AM machine providers, purchasing services like maintenance and material delivering along with the AM machine in one package price. Hence, the expensive investment cost can be amortized over long periods in the application phase. Besides, the long-term business relationship with AM machine providers also implies an access to the professional knowledge of AM technologies.

4. Identification of barriers 4.1. Scale and participants of the workshop In order to ensure the diversity of perspectives, experts from academia, commercial vehicle manufacturers and their suppliers are invited. The participants of the workshop include, respectively, three academia researchers, three project managers from three commercial vehicle manufacturers, and three project managers from three suppliers. In the workshop, the agenda and the background about AM technologies were first introduced. Each participant from the industry introduced their opinions about AM technologies as well as the potential benefits for their companies. Some of them have already set internal pilot projects to explore the benefits of different AM technologies, and some of them are still in the information gathering phase. However, none of them has already adopted AM in their manufacturing systems. After their introduction, the objective of the workshop was set as identification of the barriers for AM applications of participants. In order to improve the efficiency and quality of the discussion, the participants were first divided into different groups, and each group discussed their individual barriers and wrote them on paper cards. The cards were afterward collected and fixed on the pinboard, and the barriers were discussed and evaluated by all participants together. Additionally, possible solutions to overcome those barriers are discussed as well. The arrangement and result of the workshop are shown in Fig. 4a.

4.2.3. Type III: organizational transformation Considering the differences between AM and conventional technologies, the existing management and production systems of companies should be adapted for AM. For instance, this includes the definition of new AM quality features, reorganization of supply chain, and new safety precaution. Moreover, the organizational transformation may lead to additional cost that will also increase the resistance for AM application. A possible solution to overcome this barrier type is to cooperate with academia or other consulting companies to identify the possible problems and to minimize the risks during the reorganization. 4.2.4. Type IV: unpredictable value and risk The last barrier type is the unpredictable value and risk of the AM application that many companies cannot exactly predict and define the benefits of AM for them, and hence, they prefer to stay with conventional technologies. For overcoming this barrier, companies should carry out internal projects to study the advantages and disadvantages of different AM technologies. Besides, many participants also state their worry about the product piracy and the intellectual property protection with AM. On the one hand, AM accelerates the product creation process of the original manufacturers, and on the other hand, AM may also help illegal competitors to counterfeit products more efficiently, especially

4.2. Findings of the workshop In order to show the common characteristics of those barriers in a more intuitive way, all barriers are clustered in four different types, see Fig. 4b. The barriers and the possible solutions that were discussed 199

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Fig. 4. Workshop for identifying the barriers for AM application and the result.

5.1. Cost model

in terms of the spare parts supply market. The possible solution to overcome this barrier can be the application of new identification technologies (e.g. barcode/matrix code, RFID-transponder) or the adaption of their business models.

According to [49], three cost factors should be considered in a cost model of polymer AM technologies: machine cost, labor cost, and material cost. This work adopted this approach and extended it by adding another two cost factors: electricity cost and protective gas cost. The reason for this extension is that SLM is a metal AM technology and involves high power laser and protective gas consumption [50,51]. Hence, the costs for electricity and gas consumptions should be considered separately in the cost model. The total manufacturing cost (Ctotal) for a build task in the validation case consists of machine cost (Cmachine), material cost (Cmaterial), electricity cost (Celectricity), protective gas cost (Cgas), and labor cost (Clabor), and it is expressed by:

4.3. Discussion of identification of barriers For companies, the application of a new manufacturing technology requires preliminary studies. This workshop highlights usual barriers for AM application of companies from the commercial vehicle industry, and proposes solutions for overcoming them. The findings of the workshop can be further referred by other companies to identify their own barriers. Nevertheless, this workshop just addressed the barriers from the side of users, and in practice, the barriers can also be found from the side of providers of AM technologies. Examples are slow build rate, small build size, and limited processable materials of AM machines, and these problems can only be solved by the AM machine manufacturers but not customers. In conclusion, the identification and overcoming of the barriers for AM application requires more cooperation between the customers and providers of AM technologies. Nevertheless, this workshop encompasses only the perspectives of participants and summarizes them in four types. For other companies and industrial areas, more barriers and solutions should be found and discussed in future works.

Ctotal = Cmachine + Cmaterial + Celectricity + Cgas + Clabor

(1)

The machine cost Cmachine can be calculated by the multiplication of the hourly rate of the machine (MHR) with the build time (tbuild), given by: (2)

Cmachine = MHR⋅tbuild

The process time tprocess includes the build time (tbuild), the time for machine preparation (tpre), and the time for post handling to remove the supports structures (tpos). The build time (tbuild) is influenced by the build rate (vbuild) and the part volume (Vpart). The equation for calculating tprocess is given by:

Vpart

5. Cost model and estimation

tprocess = tpre + tbuild + tpos, with tbuild =

The AM technology for cost estimation in the validation case is selective laser melting (SLM) which uses laser to scan a power bed selectively and fuses powders to layers [45]. The reason for choosing SLM to produce this part includes three aspects: First, SLM is one of the earliest invented metal AM processes and has been established for nearly 20 years [46]. Today, SLM already shows a higher degree of maturity for industrial application and the real application cases of SLM can be already found in other industrial areas, e.g. aerospace [4]. Second, in the market, the number of available SLM-machines is significant covering different build sizes and feedstocks. For other processes like laser welding, the machine choice is limited. Third, SLM has an overall better performance in manufacturing accuracy and geometrical limitation than other processes [4]. Considering that the validation part encompasses lattice structures, the precise manufacturing of the lattice structure is an essential factor to ensure that the function of the part can be realized as it was designed. In conclusion, SLM is chosen as the core AM process in the validation case. Two representative machines with different sizes (M290 and M400-4 [47,48]) are selected as reference machines to study the manufacturing cost with SLM.

The MHR is calculated through the division of the overhead cost (Coverhead) for using a machine in one year by the working hours of the year (WH), expressed by:

MHR =

Coverhead WH

vbuld

(3)

(4)

The overhead cost per year (Coverhead) is the sum of machine depreciation per year (DY), maintenance cost per year (MC), room cost per year (RCY), and the capital interest per year (I), and it is given by:

Coverhead = DY + MY + RCY + I

(5)

In the validation case, the purchasing prices of the machines are obtained from [4], and six years of service life are assumed for the application condition of the project partner, see Table 1. Assuming that the machines should work 90% time in a year [49], 7884 h annual working time is calculated by 365 × 24 × 90%. The space requirements of the machines are determined according to [47,48]. The interest rate, unit room cost and maintenance cost coefficient are assumed according to the conditions of the project partner. Hence, the MHR of 200

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For both machines, two types of protective gas can be applied: argon and nitrogen. The price of liquid argon compressed under 200 bar for welding is 2.14 €/l according to [54]. The argon gas consumptions of both machines are obtained as 100 l/min from their data sheets. According to [55], 1 L liquid argon compressed under 200 bar is expand to 0.213 m³ argon in gas form under ambient condition. Hence, the hourly liquid argon consumption rate of both machines is calculated as 28 l/h, see Table 2. By multiplying the liquid argon price with the liquid argon consumption rate, the GPH for argon is calculated as 60 €/h, see Table 2. For nitrogen, both machines have inbuilt the nitrogen generator, which enables the generation of nitrogen from the compressed air. The compressed air supply for both machines are 20m³/h, and the cost for generating 1 m³ compressed air is nearly 0.0114€/h [56], and hence, the GPH for nitrogen of both machines is calculated as 0.228 €/h. The labor cost Clabor considers the number of the employees (n), labor cost per hour of an employee (LCH), and the time for machine preparation (tpre) and post handling (tpos), expressed by Eq. 9. In Germany, the labor cost per hour of an employee is around 39 €/h according to [57].

Table 1 Calculation of MHR of machines (according [4,47,48,52]). Basic machine data Machine Manufacturer Size of the build platform (mm × mm × mm) Space requirement (m²) Purchasing price [€] (PP) Application conditions Service year [year] (SY) Maintenance coefficient [%] (MC) Unit room cost [€/m²] Interest rate [%] (i) Working hour per year [h] (WH) (365 × 24 × 90%) Calculation of machine hour rate Depreciation per year [€/year] (DY) Maintenance per year [€/year] (MY) Room cost per year [€/year] (RCY) Interest per year (I) [€/year] Overhead cost [€/year] (Cover) Machine hour rate [€/h] (MHR)

M290 EOS 250 × 250 × 250

M400-4 EOS 400 × 400 × 400

17 480000

39 1420000

6 10 130 5 7884

6 10 130 5 7884

80000 48000 2210 24000 154210 19.56

236667 142000 5070 71000 454737 57.68

Clabor = n⋅LCH ⋅(tpre + tpro) both machines are calculated as 19.56€/h and 57.68€/h, respectively, see Table 1. The material cost (Cmaterial) for manufacturing a part depends on the material price per kilogram (MP), material density (ρmaterial) and part volume (Vpart), and it is expressed by:

Cmaterial = MP⋅ρmaterial ⋅Vpart

5.2. Cost estimation of the validation case By varying the type of machines (M290 and M4004), type of protective gas (argon and nitrogen), and type of materials (316 L steel and aluminum), 8 different solutions for a build task are obtained. However, if aluminum is applied, it requires argon as the protective gas but not nitrogen because aluminum is active and the performance of argon is more suitable, and hence, two solutions (aluminum with nitrogen for M290 and M400-4) are excluded. Eventually, the remaining 6 solutions are considered in the cost evaluation, see Table 3. For each solution, the build of multiple parts in one task is assumed that 15 pieces can be produced by M290, and 40 pieces by M400-4. The part volume of a single part is 21 cm³ and 10% more volume is assumed for support structures. Hence, the total volume of parts and support structures is 346.5 cm³ for M290, and 924 cm³ for M400-4. The consumed powders are calculated by multiplying the volume with the material density, see Table 3. For cost estimation, time parameters are first determined, and they are described as follows:

(6)

In the validation case, two types of powders can be used: 316 L stainless steel and AlSi10Mg aluminum. The price of the powders usually varies according to markets, providers, purchasing quantity and other factors. According to the offering prices in our previous research, the price of 40 €/kg is assumed for 316 L powders, and 75 €/kg for AlSi10Mg powders, see Table 2. It is to mention that the calculation of the part volume (Vpart) should consider the addition volume for support structure. For the validation case, nearly 10% more powders are consumed for producing support structures of the validation part. The electricity cost (Celectricity) is calculated by the multiplication of electricity cost per hour (ECH) with the build time (tbuild), given by:

Celectricity = ECH ⋅tbuild

(7)

• Build time (t

In Germany, the electricity price for the industrial use is around 0.08 €/kWh [53]. By multiplying the electricity price with the average power of each machine, the ECH can be determined, see Table 2. In a similar way, the protective gas cost (Cgas) depends on the gas cost per hour of an AM machine (GCH), and the build time (tbuild), expressed by:

Cgas = GCH ⋅tbuild

•

(8)

Table 2 Price factors for material cost, electricity cost, gas cost and labor cost (according to [47,48,52,53,55,57]). Basic machine data Machine Average power [kW] Price of electricity [€/kWh] Gas supply (argon gas) [l/min] Liquid argon consumption rate (200 bar) [l/h] Price of liquid argon under 200 bar [€/l] Compressed air supply (for nitrogen generator) [m³/h] Cost of compressed air [€/m³] Additional price factors Electricity cost per hour [€/h] (ECH) Gas cost per hour (GPH) [€/h] (Argon) Gas cost per hour (GPH) [€/h] (Nitrogen) Labor cost per hour [€/h] (LCH) Material price (316 L; AlSi10Mg) (MP) [€/kg]

M290 2.4 0.08 100 28 2.14 20 0.0114

M400-4 22 0.08 100 28 2.14 20 0.0114

0.2 60 0.228 39 40; 75

1.76 60 0.228 39 40; 75

(9)

•

build): Different materials have different properties and apply different build rates. The build rates in the solutions are decided according to the recommended parameter sets from the machine manufacturers. The build time is calculated through the division of the total volume by the build rate. Preparation time (tpre): For the machine preparation, powders should be screened. According to [58], the screening speed of powders is 240 kg/h. The mass of minimal required powders that should be loaded into the powder container for M290 is 79 kg, calculated by the multiplication of the area of the build platform, the height of the workpiece, and the material density. The size of the build platform is given in Table 1, the height of the part is shown in Fig. 3, the material density as well as the calculated results are shown in Table 3. Hence, the minimal required time for machine preparation is equal to the powder screening time which is determined by division of the mass of required powders by the screening speed, see Table 3. Post handling time (tpos): For the post handling, the time for removing the support structure per part by single employee is assumed as 3 min, and hence, the post handling time is 0.75 h for M290, and 2 h for M400-4.

After the determination of time parameters, the costs of solutions are calculated. The results of the cost estimation are shown in Table 3, 201

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Table 3 Cost estimation of the validation case. Solutions for a build task Machine Number of parts per build task Total volume (including supports) [cm³] (Vpart) Material Density [g/cm³] (ρ) Powder consumption (kg) Build rate [cm³/h] (vbuild) Build time [h] (tbuild) Min. required powders in container [kg] Screening speed [kg/h] Preparation time [h] (tpre) Post handing time [h] (tpos) Process time [h] (tprocess) Number of employees Protective gas

EOS M290 15 346.5 316 L 7.9 2.7 13.3 26.1 79 240 0.3 0.75 27.1 1 Argon Solution 1 509.6 109.5 5.2 1563.2 42.1 2229.5 148.6 6.4

Nitrogen

AlSi10Mg 2.7 0.9 26.6 13 26.7 240 0.1 0.75 13.9 1 Argon

Nitrogen

EOS M400-4 40 924 316 L 7.9 7.3 42.6 21.7 202.2 240 0.8 2 24.5 1 Argon

Solution 2 509.6 109.5 5.2 5.9 42.1 672.3 44.8 1.9

Solution 3 254.8 69.4 2.6 781.6 33.6 1142 76.1 3.3

– – – – – – – – –

Solution 4 1251.1 292 38.2 1301.4 110.9 2993.5 74.8 3.2

Nitrogen

AlSi10Mg 2,7 2.5 85.1 10.9 68.4 240.0 0.3 2 13.1 1 Argon

Nitrogen

Solution 5 1251.1 292 38.2 4.9 110.9 1697.1 42.4 1.8

Solution 6 626.3 185 19.1 651.5 89.1 1571 39.3 1.7

– – – – – – – – –

Cost estimation Machine cost [€] Material cost [€] Electricity cost [€] Gas cost [€] Labor cost [€] Total cost [€] Cost per piece [€/piece] Cost per volume [€/cm³]

Fig. 5. Share of the cost factors in the validation case.

and the shares of the respective cost factors are depicted in Fig. 5. The findings of the cost estimation include following aspects:

• Based on the evaluation of total cost, solution 4 is the most ex-

• •

•

•

pensive solution, and solution 2 shows the lowest cost. Besides, the total costs of solutions with M290 are lower than solutions with M400-4, if they apply the same type of gas and material e.g. total cost of solution 1 is lower than solution 4, and total cost of solution 2 is lower than solution 5. Based on the evaluation of cost per piece or per volume, solution 1 is most expensive, and solution 6 is cheapest, but the difference between solutions 2, 5 and 6 is not significant. For solutions with argon gas, the gas cost makes a share around 40% or 70% in the total cost, see solutions 1, 3, 5, and 6 in Fig. 5. For nitrogen, the gas cost is very low. The reason is that the argon should be purchased from suppliers and the generation of argon is usually expensive. For nitrogen, both machines have integrated nitrogen generator, which allows the generation of nitrogen directly from the compressed air at a lower price. For M290, the solution with lowest cost is solution 3 with nitrogen and 316 L material, while for M400-4, the best result is the solution

6 with aluminum and argon. By comparing solution 3 and 6, the total cost of solution 3 is lower, while the cost per piece and per volume of solution 6 are lower. Costs for electricity and labor have less impact on the total cost. For M290, the electricity cost is not significant, and for M400-4, the electricity cost makes a share of nearly 1% to 2%. The share of labor cost for both machines ranges from 1.9% to 6.5%.

In conclusion, solution 6 should be selected as the best solution based on the point of view of the cost per piece and per volume. However, if the users are sensitive to the total investment cost at the first time and the production quantity per day is limited, solution 2 can also be applied. 5.3. Discussion of cost model and estimation For manufacturing companies, the manufacturing cost is an essential criterion for determining whether to adopt a new manufacturing technology. Hence, the estimation and analysis of the manufacturing cost for applying AM technologies has become one of the most concerned topics for companies. By comparing the manufacturing cost of 202

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Hence, the process steps as well the entire hybrid additive-subtractive process chain should be displayed in area 2, interpreting the transformation of the raw material to the final product. In area 4, the information objects like CAD-models are described.

AM with conventional technologies, the companies can intuitively derive the economic advantages or disadvantages of AM, and then make better decisions. The real cost structure in a manufacturing system is complex and difficult to describe accurately in the early planning phase. Therefore, assumptions of parameters and simplifications of the cost structure will result in a part of deviation. Nevertheless, as a meaningful tool for supporting decision-makings, the proposed cost model and the calculation example can help companies of the commercial vehicle industry to conveniently estimate the manufacturing cost and time for a build task with SLM before they start to purchase and to establish systems and processes. In the validation case, the cost per piece with AM is more expensive than the conventional casting process, and the project partners are still in information gathering phase and haven’t adopted the SLM process yet. From the methodological perspective, an important difference between the cost model in this research and other existing cost models is that this paper use time reference to demonstrate the specific cost factors. In this cost model, besides the material cost, the price factors for gas, labor, machine, and electricity are unified with the unit of €/h, as shown in Table 2. In other existing works, the unification of the units of the specific cost factors was not considered, e.g. in [42], the equipment factor is calculated in €/cm³, and energy cost factor is in €/kWh. The main advantage of the unification of the cost factors is the improved feasibility in the cost estimation. In other words, if other users want to apply the cost model in this paper for their own cases, only two types of information are needed: the mass of the part and the time of the build task. This improved feasibility will help users to analyze the cost structure of AM more conveniently and efficiently, as well as to save more time in the decision-making phase.

6.2. Workflow for designing hybrid additive-subtractive process chains The general workflow to design hybrid additive-subtractive process chains consists of five phases: information collection, selection of AM process and system, process chain planning, allocation of resources, and description of process sheets. The overview of the design workflow is depicted in Fig. 7. 6.2.1. Information collection The first phase is to collect the basic information of the product, the manufacturing system, and AM technologies. The information of the product includes its desired geometrical and mechanical properties such as form, surface, strength and fatigue strength. The information about the manufacturing system includes for instance the layout of the factory and the material flow. The information of AM technologies like build speed and minimal processable wall thickness can be collected by AM machine manufacturers, papers or industrial reports. In this work, the information of product and system was provided by the project partner, and the information of AM technologies was already documented in previous internal studies. 6.2.2. Selection of AM process and system The standard ISO 17296-2 defines seven AM process categories and can be referred for evaluating and selecting AM processes and systems [14]. The general criteria of the evaluation include but are not limited to the following:

6. Design of hybrid additive-subtractive process chains

• Feedstock • Manufacturing cost and time • Manufacturing accuracy • Maturity of technology • Available machines in the market • Single- or multi-stage process • Material distribution • Fusion mechanisms

6.1. General structure of a hybrid additive-subtractive process chain In manufacturing systems, a manufacturing process chain describes a combined sequence of interrelated single processes for manufacturing a part [59]. In AM, the subtractive manufacturing is usually used for the post-processing. Therefore, the manufacturing chains combining AM with subtractive manufacturing are also called hybrid additive-subtractive process chains, e.g. [31]. The main properties of a hybrid additive-subtractive process chain consist of following three aspects:

In the validation case, SLM is evaluated and selected as the suitable AM technology for producing the validation part because of its high maturity, manufacturing accuracy, and availability of machines in market. For selecting an appropriate AM system, the manufacturing cost and time are used as criteria. Considering that the production quantity of the validation case varies from 16 to 20 pieces per day, EOS M400-4 with a faster build rate is selected as the AM system for carrying out the SLM process.

1 A hybrid additive-subtractive process chain can be regarded as an entire system, from which different individual closed units can be divided and defined as process steps [60]. Hence, in accordance with a bottom-up approach, the design of an entire hybrid additivesubtractive process chain can be realized through the design and the aggregation of the individual process steps. 2 The process steps can be distinguished between main and peripheral process steps. The main process steps of a hybrid additive-subtractive process chain encompass the activities to process material or data [60], e.g. build process, data preparation and heat treatment. The peripheral process steps include those activities which do not directly participate in material or data processing but support them, e.g. transportation and storage. 3 A hybrid additive-subtractive process chain can be divided into three phases: pre-, in-, and post-processing [2]. Since the in-processing includes the process steps carried by an AM machine, the other process steps before and after the part is manufactured by an AM machine can be allocated to, respectively, the pre-processing and the post-processing.

6.2.3. Process chain planning After the information collection and selection of the AM process and system, the hybrid additive-subtractive process chain can be planned with three steps, see Fig. 8A–C. 1 Description of product, material, and information objects. The aim of this step is that the geometrical and functional features of the part are listed on area 3 of the hybrid additive-subtractive process chain, and the material is described on area 1. Afterward, SLM as the core AM process is placed in the middle of area 2. The information objects of CAD-model and build parameter is placed on area 4, see Fig. 8A. 2 Planning of main process steps. Initially, the main process steps to connect the material and AM core process should be defined and placed in the pre-processing phase. In the validation case (see Fig. 8B), the main process steps for the pre-processing include

The description of a general hybrid additive-subtractive process chain can be summarized in four areas, as numbered from 1 to 4 in Fig. 6. Area 1 refers to the description of raw material used in the manufacturing, and area 3 describes the features of a final product. 203

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Fig. 6. Description of a general hybrid additive-subtractive process chain.

Fig. 7. Overview of the workflow for designing hybrid additive-subtractive process chains in form of a phase/milestone diagram.

support the main process steps. In the validation case (see Fig. 8C), four peripheral process steps are defined. Considering that the inand post-processing are carried out in different departments of the project partner, the first peripheral process step is defined as transportation of the manufactured parts, and it is added between SLM and removal of support structures. Afterward, the quality of the manufactured parts especially the lattice structures should be visually controlled, which leads to the second peripheral process step of a visual inspection. After drilling, two more process steps are defined: Cleaning aims at the removal of scraps on the surface or in the lattice structures and final sampling test aims at inspection of the product quality.

machine preparation and data processing. The machine preparation includes operations like powder screening and loading, machine status inspection and adjustment, and setting of peripheral units, while the data processing involves the creation of the STL-file and the preparation of the parameter set for the build task. In the postprocessing phase, three main process steps are defined. The removal of the support structure needs to be carried out after the SLM process, aiming at disconnection of the manufactured parts from the build platform. The shot peening is then applied to create a compressive residual stress layer on the surface of work pieces in order to improve the mechanical performance of the parts. Finally, drilling aims at machining the holes on the parts and indicates that the manufactured part is already technological feasible for application. 3 Planning of peripheral process steps. While the main process steps aim at the transformation of material and information objects, the planning of the peripheral process steps mainly aims at how to

It is to mention that the planning scope should be defined for designing hybrid additive-subtractive process chains. For example, some manufacturers may want to outsource the post-processing steps to 204

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Fig. 8. Development of a hybrid additive-subtractive process chain in the validation case.

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chains. The essential meaning for proposing such a process sheet is to link the phases of production planning and execution. The process sheets summarize the results of the process planning and can be used in the phase of process execution. In the current literature, the production planning and execution with AM are usually regarded separately, and this paper has integrated the awareness of the execution in the process design phase. As a benefit, the applicability and feasibility of the designed process chain can be improved. Since the proposed design method has been validated, it can be further applied or modified by companies for their own cases.

manufacturing service providers, and hence, those process steps can be ignored during the planning of the post-processing. Nevertheless, in the validation case, the project partners assume that they want to finish the post-processing in-house and hence take those process steps into account. 6.2.4. Allocation of resources The result of the process chain planning only represents the conceptual structure of a hybrid additive-subtractive process chain. In order to convert the conceptual solution into a detailed feasible solution, the resources used to carry out the entire hybrid additive-subtractive process chain need to be defined and allocated to corresponding process steps. First, the operative units that will carry out the process steps should be defined. Second, technical resources like machines, materials and tools for each process step are defined and allocated. Third, the responsible employee for operating the machines, materials, and tools should be appointed. In validation case, the persons and machines in charge to execute those process steps are defined according to the szenario of the project partners.

7. QM control model with AM 7.1. Indicator system According to the standard ISO 9000:2015, the term of ‘quality’ is defined as the degree to which a set of inherent characteristics of a component fulfils requirements [61]. The QM describes a set of operational relevancies with respect to the objectives for the quality improvement [61]. In order to interpret the performance of the QM and to establish operational base, an indicator system is essential. This work defines the indicators from two perspectives: the quality indicators and the influencing factor indicators. This is based on the assumption that the different arrangement of materials, machines, methods, operations and other factors related to the hybrid additive-subtractive process chain will result in a different quality level of the final part [62,63]. Hence, the quantifiable parameters to indicate the quality features of a final part are defined as the quality indicators, and the measurable parameters of the influencing factors of the hybrid additive-subtractive process chain are defined as the influencing factor indicators.

6.2.5. Description of process sheets The last phase for designing a hybrid additive-subtractive process chain is to summarize the information of all process steps in a systematic document. For each process step, a process sheet is described based on three information types: the organizational information, the process description, and the operation/object table. Fig. 8D provides an example for the process step data processing, in which the process name, the responsible person, the department, and the objective of the process step are first summarized as the organizational information. Second, the operations, the duration, the critical spots, and the note are summarized in the process description. Third, the material and data objects including resources allocated to this process step are listed in rows, while the operations from the process description are listed in columns. The cross symbol in the matrix area indicates that in order to execute a selected operation, the corresponding objects are involved. Since the process sheets outline the most important information of the process chain, they can be used as working instruction in the application phase.

7.2. Closed-loop based quality control The QM activities with AM can be summarized in a closed-loop control model, see Fig. 9. The closed-loop QM control model encompasses four phases, and two branching points lead to one small and one big closed-loops. The small closed-loop encompasses the phases of indicator monitoring and problem solving, and it is considered as the operative control loop, which means that a problem is reported by the indicator system during monitoring and the problem is solved. The big closed-loop is defined as strategic control loop and includes the phases of product and process analysis, indicator planning, and indicator monitoring, which implies the route for changing operative objectives as well as the indicator system. The four phases are described as follows.

6.3. Discussion of design of hybrid additive-subtractive process chains This subsection introduces a design method to support the companies from the commercial vehicle industry to design their individual hybrid additive-subtractive process chains. Based on the classic bottomup approach, the conceptional solution of a hybrid additive-subtractive process chain is first planned and then successive detailed into a feasible solution. Finally, the information of all process steps is summarized in individual process data sheets. The features to distinguish the proposed design method from other existing methods for planning hybrid additive-subtractive process chains include two parts. First, this method suggests the description of hybrid additive-subtractive process chains according to four basic dimensions: materials, process steps, products, and information objects. In current literature, other works mainly emphasize that the design of hybrid additive-subtractive process chain should be subject to the geometrical properties of the product, especially in the remanufacturing context e.g. [28,29]. This research extends this understanding and considers the material and information objects as another two boundary conditions for designing a hybrid additive-subtractive process chain. In other words, the hybrid additivesubtractive process chain is not only considered as the means to realize a specific product design or redesign, but a means to bridge the gap between the material, final product, and information objects in the AM context. Second, this method emphasizes the importance of the summary and documentation of process information, and this paper also proposes a design of a process data sheet. The previous works mainly aim at proposing design rules and framework, and they pay less attention to the systematic construction of information bases for process

Fig. 9. The closed-loop QM control model. 206

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subtractive process chain are collected, and a cause-effect analysis is carried out in order to explore the relationship between the product features and process features. In accordance with [64], this work also adapted Ishikawa diagram to identify the features of the process steps and to derive the relationship between product and process features. 7.2.2. Indicator planning In the second phase, the quality indicators and the influencing factor indicators are defined. First, in accordance with the product feature evaluation in the former phase, the quality indicators are derived from the important product features. In the validation case, four types of quality features for AM on the final product level are used for deriving the quality indicators: surface quality, geometrical accuracy, material density, and mechanical performance [63]. Fig. 10 shows the defined quality indicators in the validation case that can be used for monitoring the final product quality. Since the holes are connected with the gear systems, their surface finishing, diameters, and positions should be measured and controlled. The residual stress, hardness, and material density can be used to interpret the material performance and the porosity. Second, the influencing factor indicators are defined from the process features that have strong relationship with the important product features. Fig. 11 shows the defined indicators for the validation case. In the process step of machine preparation, the powder is prepared and the related parameters like particle size and humidity can be controlled and used as influencing factor indicators. For data processing, scanning strategy, speed, and build orientation have significant impact on material performance and the porosity of the final product. The influencing factor indicators of the SLM can be the laser power, oxygen content in the build room, type of inert gas, as well as the gas supply speed. For

Fig. 10. Definition of quality indicators for the validation case.

7.2.1. Product and process analysis The first phase is process and product analysis in which the basic information about the final product and the underlying hybrid additivesubtractive process chain are specified. First, the customer and functional requirements for the final product should be defined, according to which the product features are assessed and prioritized, e.g. desired optical features and material properties. Afterward, the most important features for satisfying those requirements are selected. In the validation case, the lattice structures and the holes are evaluated as the most important product features because the holes are interfaces for connecting the gear system, and the lattice structure provides the physical supporting functions. Second, the features of the hybrid additive-

Fig. 11. Definition of influencing factor indicators for the validation case. 207

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Fig. 12. Decision logic of the QM control loop.

B If the quality indicators are under control and the influencing factor indicators are not stable, it means that some influencing factors are unnecessary because their change shows no impact on the quality indicators, and then the strategic control loop should be carried out to remove those unnecessary influencing factor indicators. C If the quality indicators are not under control but the influencing factor indicators are still stable, it means that products are not qualified or there is a high potential for quality problems, and the process chain is not reliable and requires adjustment. Nevertheless, if influencing factor indicators are stable, it means that the origin of the problem cannot be identified. In this case, the strategic control loop should be carried out and new influencing factor indicators should be added, and afterward, the problems should be located and solved. D If the quality indicators are not under control and the influencing factor indicators are not stable, it means that a quality problem is reported or the potential for a quality problem has been increased, and their reasons can be addressed by influencing factor indicators. In this case, the operative control loop needs to be carried out and the problems should be solved.

the shot peening, the diameters of the balls and the peening speed are considered, and for the drilling, the feed rate and the status of the drilling tools should be considered. 7.2.3. Indicator monitoring The objective of this phase is to check whether the product quality is still qualified and the hybrid additive-subtractive process chain is still functional and reliable. For this purpose, the indicators of both types are measured and evaluated regularly. The evaluation of the indicators is based on the classic statistical process control method [65]. In the validation case, the quality control charts are used to analyze the quality indicators. A quality control chart includes the upper and lower warning limit (UWL, LWL), and the upper and lower control limit (UCL, LCL), and the mean value of a quality indicator for a qualified sample should stay between the UWL and LCL [65], see Fig. 12. Initiating from the indicator monitoring in which the quality and the influencing factor indicators are constantly measured and traced, there are four cases by considering whether the quality indicators are under control and whether the values of the influencing factor indicators are still stable at the same time, as marked from A to D in Fig. 12.

7.2.4. Problem solving If the quality indicators report quality problems, then the influencing factor indicators should be checked to see whether one or more indicators show unusual values. The indicators with outliers imply the problems that cause the quality problem and should be corrected in this phase. According to the shape of the curve on the quality control chart,

A If the quality indicators are under control and the influencing factor indicators are stable at the same time, it means that the product is qualified and the hybrid additive-subtractive process chain is reliable. The next action is to repeat the indicator monitoring for the next manufacturing task. 208

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Fig. 13. Curve shapes on the quality control charts, possible reasons and recommended measures for the problem solving of the validation case (adapted according to [61]).

different status of the process chain can be addressed. In accordance with [61], this work summarized five possible unusual curve shapes on the quality control chart, and analyzed the possible reasons, and proposed measures, see Fig. 13.

•

• Type I: Excess of warning limit: If the mean value of a sample

•

exceeds the UWL or LWL, it implies an increased risk of a quality problem. The reasons may be some unknown impact factors from the environment, or inaccurate test equipment, or wears of components of machines or tools. The influencing factor indicators of SLM and drilling may be changed, and hence, the recommended actions include the increase of test frequency and number of samples, paying more attention to the environment, and control of drilling tools, SLM machine, and test equipments. Type II: Excess of control limit: If the mean value of a sample exceeds the UCL or LCL, it means that a quality problem occurs and the production should be stopped. The possible reasons can be unqualified powder and wear of drilling tools, or some components of machines are approaching their service life, or inappropriate shot peening. Hence, the influencing factor indicators of machine preparation, drilling, shot peening should be checked. The recommended measures are resetting of the drilling process, change of the drilling tool, and control of the powder quality. Besides, since

•

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the mean value of the sample exceeds the control limit, the corresponding production batch should be controlled with a larger sample test again. Type III: Run: If seven mean values stay on one side between the middle line and the UWL or LWL, it implies a high risk of quality problems and the production should be stopped. The reasons may be inappropriate parameter set of SLM, wear of the drilling tool, powders in the same batch are not qualified, or the device for screening powders is not qualified. The influencing factor indicators in machine preparation, data processing, SLM, or drilling may be changed. The possible solutions are resetting of the parameter set of SLM, change of the drilling tool, and control of powders and the screening device. Type IV: Trend: If seven mean values are rising or falling in a row and approaching the UWL or LWL, the risk of a quality problem is significant and the production should be stopped. The reasons can be that machine components, drilling tools, or the test equipment are approaching their service life due to the reason like wears and material fatigue. Besides, an inappropriate process parameter set is also possible. Hence, the influencing factor indicators of data processing, SLM, and drilling may be changed. The recommended measures are the resetting of the SLM process, control and change of machine components, drilling tools, or the test equipment.

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• Type V: Periodic: If a periodic wave is observed, it means that there

solving.

may be some periodic impact factors from the environment like periodic temperature changings. Moreover, the wear of clamping tools or unstable powder quality may also result in this type of curve shape. Hence, the influencing factor indicators of machine preparation and drilling should be controlled. The solutions are to find the impact factors in the environment or to control the clamping tools or powders.

Initiating from this paper, future work needs to focus on:

• Identification of more barriers for AM applications from other areas • • •

7.3. Discussion of QM control model with AM For a long-term AM application, a suitable QM strategy is necessary. Based on classic statistical QM methods, this work proposes an AM specific indicator system to describe the status of the final product quality and the hybrid additive-subtractive process chain. By monitoring the indicators, the product quality can be evaluated and the process chain can be controlled. The QM activities are then summarized in a closed-loop control model, which also implies a continuous improvement process (CIP). The QM with AM is an important research topic, and this work addresses the quality control from a management perspective. Current research mainly focuses on a single quality perspective of either the material, machines, or processes. This paper sets the perspective to the level of management and proposed a unified conceptional architecture for a long-term analysis, assessment, and improvement of the product quality in AM. The proposed closed-loop QM model can support users and companies to build their own unique QM system, and the defined four phases and two control mechanisms of the model can help the users to derive and arrange their detailed QM activities. Nevertheless, QM in AM is a comprehensive research topic, and operative methods and tools to evaluate, monitor, and improve the product and process quality of AM should be still addressed in the future work.

Acknowledgement This work was supported by the European Union’s Regional Development Fund (ERDF) and the Commercial Vehicle Cluster (CVC) Südwest. References [1] ISO. ISO/ASTM 52900:2015 (ASTM F2792) Additive manufacturing – general principles – terminology. Geneva: ISO; 2015. [2] VDI e.V. VDI 3405: Additive Fertigungsverfahren Grundlagen, Begriffe, Verfahrensbeschreibungen;25.020. Beuth Verlag; 2014. [3] Thompson MK, Moroni G, Vaneker T, Fadel G, Campbell RI, Gibson I, et al. Design for additive manufacturing. CIRP Ann Manuf Technol 2016;65(2):737–60. https:// doi.org/10.1016/j.cirp.2016.05.004. [4] Wohlers T, Campbell RI. Wohlers report 2017: 3D printing and additive manufacturing state of the industry annual worldwide progress report. Fort Collins, Colo: Wohlers Associates; 2017. [5] Levy GN, Schindel R, Kruth JP. Rapid manufacturing and rapid tooling with layer manufacturing (LM) technologies, state of the art and future perspectives. CIRP Ann Manuf Technol 2003;52(2):589–609. https://doi.org/10.1016/S0007-8506(07) 60206-6. [6] Attaran M. The rise of 3-D printing. Bus Horiz 2017;60(5):677–88. https://doi.org/ 10.1016/j.bushor.2017.05.011. [7] Teutsch R. Der 3-D-Druck kommt in der Nutzfahrzeugindustrie an. Atz - Automob Z 2018;120(11):76–9. https://doi.org/10.1007/s35148-018-0187-0. [8] Hoepke E, Breuer S. Nutzfahrzeugtechnik. Wiesbaden: Springer Fachmedien Wiesbaden; 2016. [9] Lehmann FH, Grzegorski A. Anlaufmanagement in der Nutzfahrzeugindustrie am Beispiel Daimler Trucks. In: Schuh G, Stölzle W, Straube F, editors. Anlaufmanagement in der Automobilindustrie erfolgreich umsetzen: Ein Leitfaden für die Praxis. Berlin: Springer; 2008. p. 81–90. [10] Huang Y, Leu MC, Mazumder J, Donmez A. Additive manufacturing. J Manuf Sci Eng 2015;137(1):14001. https://doi.org/10.1115/1.4028725. [11] Hague R, Campbell I, Dickens P. Implications on design of rapid manufacturing. Proc Inst Mech Eng, Part C 2003;217(1):25–30. https://doi.org/10.1243/ 095440603762554587. [12] Merkt S, Hinke C, Schleifenbaum H, Voswinckel H. Geometric complexity analysis in an integrative technology evaluation model (ITEM) for selective laser melting (SLM). S Afr J Ind Eng 2012;23(2). https://doi.org/10.7166/23-2-333. [13] Kerbrat O, Mognol P, Hascoet J-Y. Manufacturing complexity evaluation at the design stage for both machining and layered manufacturing. CIRP J Manuf Sci Technol 2010;2(3):208–15. https://doi.org/10.1016/j.cirpj.2010.03.007. [14] ISO. ISO 17296-2:2015. Additive manufacturing — general principles — part 2: overview of process categories and feedstock. ISO; 2015. [15] Gebhardt A, Hötter J-S. Additive manufacturing 3D printing for prototyping and manufacturing. 1st ed. s.l.: Carl Hanser Fachbuchverlag; 2016. [16] Ponfoort O. Successfull business models for 3D printing: seizing opportunities with a game changing technology [Utrecht]: [Berenschot]. 2014. [17] Kruth J-P, Leu MC, Nakagawa T. Progress in additive manufacturing and rapid prototyping. CIRP Ann Manuf Technol 1998;47(2):525–40. https://doi.org/10. 1016/S0007-8506(07)63240-5. [18] Burkhart M, Aurich JC. Framework to predict the environmental impact of additive manufacturing in the life cycle of a commercial vehicle. Procedia Cirp 2015;29:408–13. https://doi.org/10.1016/j.procir.2015.02.194. [19] Yi L, Gläßner C, Aurich JC, et al. Concept for identifying potentials of additive manufacturing in commercial vehicle production. In: Berns K, Dressler K, Fleischmann P, Görges D, Kalmar R, Sauer B, editors. Commercial vehicle technology 2018. Wiesbaden: Springer Fachmedien Wiesbaden; 2018. p. 246–60. [20] Ley M, Buschhorn N, Stephan N, Teutsch R, Deschner C, Bleckmann M, et al. Hybrid-optimized manufacturing of load-bearing components by combining of conventional and additive manufacturing processes. In: Berns K, Dressler K,

8. Conclusion and outlook This paper provides a holistic discussion for integrating AM technologies in manufacturing systems. Starting point of view is the commercial vehicle industry, but other industries with similar interests also benefit from this work to overcome the challenges in integrating AM technologies. A successful integration and application of AM technologies usually involves many issues on both technological and economic dimensions, and this paper addressed four of them. Following conclusions can be made:

• The lack of know-how, high investment cost, organizational trans•

•

•

and the side of providers. Development of methods, concepts, guidelines, and tools to help companies to overcome the barriers mentioned in this paper. Exploration of other factors for supporting the early decision-makings of companies (e.g. product piracy and risk). Standardization of the method for designing hybrid additive-subtractive process chains. Development of measures and solutions on the technological dimension to monitor, control, and improve the product and process quality of AM.

formation, and unpredictable value and risk are four types of common barriers for AM applications of companies in the commercial vehicle industry. Cost estimation of AM application is a powerful means to analyze the economic amplifications of AM technologies in the early decision-making phase. The cost structure of the SLM process can be summarized in five cost factors: machine cost, material cost, gas cost, electricity cost, and labor cost. The cost model with cost factors specified using the time reference unit improves the practical feasibility in the cost estimation. The integration of AM technologies requires the design of a hybrid additive-subtractive process chain. Four basic dimensions to describe a hybrid additive-subtractive process chain are: materials, process steps, products, and information objects. For designing a hybrid additive-subtractive process chain, the summary of the process information in process sheets will guarantee the applicability of the process chain in the execution phase. Classic statistic QM methods and CIP thinking can be adapted for developing individual QM strategies with AM. The QM activities with AM can be summarized in four phases: product and process analysis, indicator planning, indicator monitoring, and problem 210

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[21] [22]

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