Parametric feature constraint modeling and mapping in product development

Advanced Engineering Informatics 26 (2012) 539–552

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Advanced Engineering Informatics journal homepage: www.elsevier.com/locate/aei

Parametric feature constraint modeling and mapping in product development C.-G. Yin a, Y.-S. Ma b,⇑ a b

Department of Mechanical Design and Manufacture, China Agricultural University, Beijing, China Department of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G8

a r t i c l e

i n f o

Article history: Received 1 September 2010 Accepted 20 February 2012 Available online 28 March 2012 Keywords: Feature-based design (FBD) Concurrent engineering (CE) Assembly feature (AF) Product lifecycle management (PLM) Engineering informatics

a b s t r a c t This paper presents the exploration and application of a feature-based design methodology within mechanical product development cycles. Based on a review of the feature technology and previous research work, this paper focuses on the modeling of intricate relations among features of different design aspects. A concept of feature parameter map that leads to a constraint mapping method is proposed. Further, features are classified into different levels; and information management for product lifecycle support is considered. The application of this method is demonstrated with the conceptual design and optimization of a gearbox as the study case. In addition, an extended feature system for product development was explored. With a spreadsheet package and a computer aided design (CAD) tool, the product model generation, change management and final optimization of the case assembly including its bulk shape, have been achieved. Two important information chains were used to address the aspect of ‘‘design for post-manufacture services’’ with concurrent engineering consideration, i.e. a field installation pattern and a set of wrapping dimensions for product transport packaging. In order to demonstrate the feasibility of change management, a different product derivative model was regenerated by adopting and changing the values of a main conceptual feature. The result is promising. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Nowadays, many companies have to speed up their new product development (NPD) to meet the challenge of keen market competition. From management point of view, outsourcing certain components and forming a supply chain are win–win cooperation strategies. From technological point of view, the trends are to apply knowledge driven NPD strategies [1] and to emphasize collaborative and concurrent engineering throughout product life cycles. Information technology solutions are being developed to integrate complex product and process models. The technical aspects involved include product conceptual design, design modeling and automation, manufacturing, delivery, field service, maintenance, and recycling [1–2]. In the past two decades, feature technology has been broadly used in engineering design, semantic modeling, information sharing, process flow control, and system integration. For example, feature technology becomes fundamental to CADCAM, product management and product lifecycle management (PLM) [3–6]. Currently, a major challenge for engineering informatics research is to develop a new feature modeling approach that can support the coexistence of multiple information views of different concurrent engineering aspects. More research is expected on those less-formalized engineering aspects, such as design con⇑ Corresponding author. Tel.: +1 780 492 4443; fax: +1 780 492 2200. E-mail address: [email protected] (Y.-S. Ma). 1474-0346/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.aei.2012.02.010

cept generation and ‘‘design for X’’ in the early stage of product development; hereby ‘‘X’’ represents the different variable aspects of concurrent engineering. A preliminary architecture has been proposed [7] in which a product development information system and the mechanisms for feature modeling and management are connected. It is well known that NPD involves iterative design-and-redesign cycles with a tremendous amount of evolvement of concepts, models and scenarios. Feature technology is believed to be useful to increase the efficiency of tedious design cycles, and effective to facilitate the exploration of different ideas and innovations by reducing and simplifying the updating tasks. This paper proposes a concept of feature parameter map that leads to a constraint mapping method. This concept is illustrated with a case study about a gearbox. Features are classified into levels for supporting product lifecycles, and feature information management is considered for the evolving conceptual design and optimization. The outline of this paper is as follows. After this introduction, Section 2 reviews the research progress of featurebased design. Section 3 describes the research scope of this paper and the approach dealing with design intent, feature management, and design methodology. Section 4 presents the detailed case study of the gear-box. Section 5 further discusses the system issues for the proposed method. Section 6 demonstrates a redesign case to check the system’s feasibility and stability. Finally, Section 7 presents conclusions and future work.

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2. Literature review on feature-based design A type of feature is a class of characteristic and semantic patterns of a set of interrelated domain-application entities involved for understanding, communication, reasoning and technical implementation. For example, the learning feature of a student can be the personality that is recognized by a teacher to apply proper educational techniques. Farmers need to understand the feature of their land, so that the right type of vegetable could be planted and the proper fertilizer can be chosen. Recently, in nano-scale material construction analysis [6], a feature was interpreted as the basic operational unit that has engineering or functional implications, with which the efficiency and convenience of crystal model construction can be increased. In the engineering field, the application of feature technology was traditionally limited to geometry construction and manufacturing processes. More recently, broader definitions and applications of advanced features are developed. However, as highlighted in [2], product engineering design has evolved into a full digital definition era due to product lifecycle management requirements and market expectation. It is expected that future comprehensive product models, which include semantic, analytical and geometrical details with computer interpretable accuracy, will provide the scalability support of modern engineering IT technology. Feature technology is one of the key enabling technologies. Research on feature technology has been greatly expanded due to many recent efforts and achievements around the world, which makes feature technology more applicable and mature. Currently, feature technology is under rapid development to meet the demands of product innovation, collaboration, time-to-market competition and managing complexity. Table 1 presents some representative contributions about feature definitions and applications. Historically, the feature concept was defined for simple and commonly used design and machining geometry elements as described by Shah [8]. In the area of manufacturing process planning, a machining feature is defined as the specification of a geometric region of a part, which includes shape, position, dimensions, tolerance, and surface finishing of that volumetric region [9–11]. This definition is formed from the perspective of manufacturing convenience to minimize the total number of machining set-ups [9]. Another concept of feature is described as a spatial unit (element) representing a region of interest within a product, which includes values and relationships (structure and constraints) [12–13]. Furthermore, complex features have been defined in the scope of specific views of the product description with respect to the classes of

characteristic properties and computing methods that cover different phases of product development [14–16]. Researchers further suggested that feature technology can be extended to system integration [10,17–20] and the management of product life cycles. In engineering informatics field, the potential functions of features could be versatile [3]; the ‘‘feature’’ concept was defined as a class of semantically endowed objects that accompanies product development from customer requests through to product release. In the field of product design and manufacturing, those existing feature definitions can be divided into two categories according to their granularity at different application levels, i.e. part features (PFs) and assembly features (AFs) [13,21–25]. At the level of an individual part, research efforts were focused on geometric or detail features [5,8–11,26]. Currently, most commercial 3D modeling software tools can support a feature-based design process from the geometric elements construction angle [6]. At the level of assemblies, the engineering approach from a perspective of mating relations among components [27–29] has matured and can be found in commercial CAD tools. More advanced assembly features (AFs) were suggested, such as those that have functional importance from the design point of view across components, sub-assemblies and material boundaries [15,21,22]. In such advanced feature modeling approaches, change management requires explicit mapping and validation of the embedded associative relations. Usually, when the designer modifies one of those parts, some other relevant components’ properties such as dimensions, need to be changed correspondingly. Therefore, the definition and management of AFs need to be considered in a broader scope at a time within a product design environment. The definition of object-based assembly features has been reported in [21,22], where a feature-object mechanical assembly library was also developed. In fact, even in the field of industrial manufacturing systems, such as production cells and lines, floor management can benefit from the similar concept, i.e. configurable system of characteristic data models [18,30]. From a computer science point of view, feature technology can be understood as a kind of semantic representation system that can describe, visualize and evaluate the intricate fine grain and invisible links among the different engineering aspects and elements within the whole product lifecycle, i.e. ‘‘from cradle to grave’’ [4,23,31,32]. As to software implementation, features are firstly defined and subsequently used by application engineers and then should be easily expressed in a program. In doing so, computer software can express, create, manipulate and manage engineering semantic entities, carry out reasoning, and resolve problems as a productivity tool for experienced engineers [31].

Table 1 Representative contributions to feature technology development. References

Specific application area

Contribution

Shah (1991) [8] Burgett et al. (1995) [35] Chu and Gadh (1996) [9] Brunetti and Golob (2000) [14] Ma and Tong (2003) [34] Ma et al. (2004, 2007) [21,22] Stamati and Fudos (2005) [41] Jin and Li (2007) [13] Igwe et al. (2008) [12] Bohm et al. (2008) [42] Skander et al. (2008) [19]

Fundamental research Design automation Machining process General conceptual design

Original definition of features Definition of application feature Minimizing the set-ups in machining Mapping product functions to concept and assembly features

Injection molding cooling circuit Injection mold design

Object-oriented semantic features Definition of advanced, object-oriented assembly feature

Jewellery pattern design

Parametric feature design in a single part

Concept design generation Virtual reality, physics modeling via CAD Morphological concept grouping for product design Feature relation modeling between design and manufacturing representations Welding form features Collaborative 3D design

Conceptual design at the assembly system level Volumetric self-organizing feature maps Concept feature generation with a repository Definitions of skin and skeleton feature considering manufacturing Application of features for cost estimation Controlling the level of details via geometric feature classification

Chayoukhi et al. (2008) [11] Chu et al. (2009) [5]

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On the other hand, application engineers should be easily able to initiate, control, accept and verify the processes that the computer does, and make the necessary decisions without being concerned with the integrity of those semantic entities, i.e. features. For the application of the feature technology, past research efforts have covered automotive component design [33], mold manufacturing [21,34], machine tool design [35], plant assembly [30], as well as other product development [36,37]. However, feature applications for conceptual design are not well studied because the modeling process is often related to assembly mechanisms. More specifically, no mature mechanism is available to manage design evolution of changes explicitly and systematically. This paper proposes a method of formalizing design constraints among features. To prove the applicability for typical modular products, a gearbox is used in our case study. The proposed parametric feature constraint modeling method allows explicit change propagation and supports systematic tracking. 3. Research approach This research is intended to support the continuous construction of features at different stages of the design process, where features are created by engineers concurrently according to their design patterns and intent from different engineering aspects. Adopting the feature-based design methodology, this research investigates a general design constraint modeling method, mainly focusing on parametric constraints, including their representation and the programming process for solving them. This paper’s innovative contribution is a proposed parametric feature association scheme, i.e. feature parameter maps, which can be used to manage feature dependencies explicitly. Hence, features are always under control by the end user via such maps and CAD feature parameters. Such feature parameter maps are continuously updated while the design model evolves throughout the lifecycle of a product until the product is out of the market and fully terminated.

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This work uses a real and yet traditional product design case, i.e. a power transmission system (gearbox) for a working machine in a food factory. The design methodology for this kind of classical design problem has been mature for many years. Some handbooks about mechanical engineering design provide the descriptions of the whole design process and the required equations [37–40]. Well trained mechanical engineers can readily design a gearbox using the routine method. The key challenge of this research is the knowledge embedment, configuration redesign and verification with different sets of customer requirements, so that the engineers can be relieved from tedious routine design effort and focus on more value-added innovation. 3.1. Design process flow chart This paper illustrates a comprehensive feature-based design process applicable from the stage of problem statement through to the finishing of the geometry synthesis and design assessment, with the emphasis on the level of conceptual design. Fig. 1 shows a design process flow chart. To do so, the hierarchical feature reference relationships are to be modeled. In the proposed feature modeling, two process groups are distinguished, conceptual feature modeling and detailed feature modeling. In more complicated design cases, features can be classified into several categories according to its different phases of the whole design process or the levels of the problem granularity under consideration. In the procedure proposed, concept design feature modeling is a process stage after engineering principle modeling and verification. In this stage, the designer will decide which concept design feature needs to be used and transferred to the downstream into the subsequent design steps. Along with conceptual features, key ‘‘design for X’’ considerations can be modeled and carried throughout the design process [24]. Here, the authors would like to differentiate the proposed feature parameter map from the ‘‘volumetric selforganizing feature maps’’ introduced by Igwe et al. [12] in which

Fig. 1. Feature-oriented design flow chart.

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volumetric feature elements or mass points were labeled into different features. Once these conceptual features are defined, they need to be stabilized over iterative processes of concept detailing which embed more and more constraints into the feature models. Then a question arises, i.e. how such numerous and complicated feature constraints are to be managed? In [21,34], constraints are built into the object class definitions. However, with a large variety of features and a large number of cross-references among features, there is a need for a general solution. It can also be realized soon that the design engineer would like to understand the dependencies visually; and he needs to evaluate explicitly the route of updating when any change is propagated throughout the feature system. With a lot of painstaking exercises, it was realized that the hierarchical relationships among those features can be worked out via their parameters, and such relations are implemented with some mathematical and logical programming, and the programmed functions can be used to update existing feature models and generate more features relevant to downstream component design. With the consideration of information flow, it can be appreciated that those typical feature dependency relations can be largely represented with a mapping mechanism of feature parameters. Such generalized dependency maps among different parameters are named feature parameter maps. This proposed feature parameter map is a conceptual organization scheme for modeling dependencies among parameters. Such feature parameter relations are at a lower level of information granularity than features, but they are intended as a means to manage parametric relations and the consistency of the overall design model during the design evolution process. The form of the representation is to be introduced in Section 4.5. After the stage of concept feature modeling, many conceptual features are then established with associations with the corresponding feature definitions, mathematical models, and management procedures. Ideally, a set of feature parameter maps should be developed to represent those relations that are formally defined between conceptual features and key design parameters. Clearly, features could have many types of constituents other than parameter relations, e.g. dependencies between geometric entities. Due to the limitation of resources and time, parameter relations are investigated first. Hence, feature parameter maps represent only one type of feature relations, i.e. cross-references among parameters. With this scheme, feature parameter maps can be identified and managed during the design process; and some valuable information chains can be formed according to applicable ‘‘design for X’’ rules. Once such parametric relations are implemented into conceptual features, organic product model can be semantically decomposed and synthesized with some common engineering design patterns, like layout pattern, installation pattern, casing dimension space, etc. As shown in Fig. 1, the conceptual modeling process certainly involves many iteration cycles for optimization. 3.2. The study case preview – a gearbox assembly Fig. 2 illustrates a classical two-stage gearbox assembly. Some important components have been labeled and named for explanation purposes. Currently, a number of 3D CAD software tools support parametric feature technology for many engineering applications. Feature based research efforts for gearbox detail design were reported in [33,36,38]. However as to the feature-based conceptual design for gearboxes, no literature can be found. 3.3. Bottom up feature-based design approach As to the design methodology and processes for a gearbox, the bottom-up approach is dominant in practice. The bottom-up

Fig. 2. An example of gearbox layout (1. pinion_1; 2. bearing_1; 3. connecting bolt for bearing cover; 4. high speed shaft; 5. gear_1; 6. bearing cover_1; 7. pinion_2; 8. middle shaft; 9. bearing_2; 10. bearing cover_2; 11. bearing_3; 12. low speed shaft; 13. bearing cover_3; 14. gear_2; 15. housing connecting bolt; 16. guide pin; 17. high-speed stage center distance; 18. low-speed stage center distance).

approach requires the designer to begin with those primary parts, like pinions, gears, and so on. Then other parts follow like shafts and bearings. Finally, the housing and other accessory components are created according to the constraints related to all parts defined. For more advanced users, parameters can be associated with mathematical relations used in different features and hence parametric control for specific features can be achieved. Unfortunately, this bottom-up approach works well for the designers in detail design process such as selecting supporting components, but the approach does not work for the overall layout of the product. This is because that bottom-up approach is too rigid and tedious to support the designer to redesign a product like the gearbox again and again. However, such design iterations are necessary to improve certain aspects related to some intricate technical specifications, e.g. the overall dimensions, or performance of a product. One such specification for a gearbox is the ratio of speed changes. Commonly, with many parts interconnected, the designer obviously faces a great challenge in taking care of those existing constraints as well as the new changes during the iterations of calculation and redesign. Therefore, a great amount of time and effort is required to work out a satisfactory and well-integrated design result, and this may cause some frustration. 3.4. Top-down feature-based design approach It has been suggested in the research community that when modeling a new product or adopting an existing design for a new model, concept design features can be very useful in reflecting the original design intent at a semantic level with interpretable rules and formulas. These features can be modeled in a concise and well-defined manner if a generic and scalable modeling approach is applied [15,34]. In order to reflect the built-in design principles, conceptual features cross the boundary of assembly modules and physical parts. For example, in Fig. 2, the dimensions 17 and 18 represent the center distances of high-speed stage and low-speed stage respectively. They obviously determine the whole bulk shape to a large extent. Such high level dimensions, collectively, form the characteristic and semantic design pattern at the top assembly level of this

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gearbox. Therefore, higher level features are commonly used in conceptual design. They are usually abstracted when initiated. They could be further detailed in downstream design stages or fully expanded according to the availability of information and the requirement of analysis. They also have instrumental influence on the lower level detailed component features. The lowest level features would be the geometrical features constructing the geometry of components. In this paper, any abstracted semantic design pattern is referred to as a conceptual feature (CF); and usually, there is a set of top level CFs. There could also be different levels of CFs; they can be realized via applying the concept of assembly features (AFs) [22] or other associative features from the angle of feature definition and modeling [34]. 3.5. The feature-based design system Fig. 3 gives a design system structure supporting the proposed feature-based design methodology. In contrast to Fig. 1, Fig. 3 shows some detailed information about how to achieve the feature-based design intent. Three types of software tools are needed across different stages of the design process; they are a parameter management tool (MS WORD and EXCEL), a parametric constraint solver (EXCEL and MATLAB), and a 3D parametric CAD software

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tool (NX). Generic programming languages, like C++ and Notepad++, are the interface developing tools. 3.6. Development of a comprehensive feature model The initial design problem can be simply represented in an abstract model as shown in Fig. 4. The first step in this proposed method is to identify all the features and their parameters that are correlated with other features. According to the definitions of unified semantic feature modeling [7], a variety of features can be identified, modeled and implemented according to an objectoriented modeling approach. All features can be further categorized according to their abstracted semantic levels, scope of influence, variation behavior, etc. For example, some of them can be classified as geometric, non-geometric, component level or subassembly level, or associated with ‘‘design for X’’ aspects, etc. It can be appreciated that features in different categories play different roles within the feature-based design progress. Concurrently with the feature generation in the product’s CAD development, all the feature data structures are also constructed into a spread sheet with the parameter definitions and their corresponding values. Theoretically, if the hierarchical feature relations or constraints can be modeled comprehensively, the whole product assembly

Fig. 3. Feature-based design system structure.

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Fig. 4. Design problem description.

could be automatically regenerated according to different options of features at both higher and lower levels. In such a way, a lot of tedious duplicated design iterations can be largely automated and the time required for alternation cycles significantly reduced. In the end, the efficiency of product innovation and new product development (NPD) would be effectively enhanced. It has been recognized by the authors that concept features often contain key parameters, and these parameters are usually associated with the upstream customer requirements and downstream design threads. In other words, feature key parameters are usually related to the design intent, and detailed geometry of parts, machining considerations, assembly, packaging, and so on. Through our case study, the bulk shape of the gearbox housing can be either defined upfront within the gearbox conceptual design, and get enriched or detailed as more required information becomes available, or progressively developed step by step while accumulating the set of conceptual feature entities as the other modules or components are developed via detailed design features. Hence either a ‘‘top-down’’ or a ‘‘bottom-up’’ design approach works well. More conceptual features can be identified and developed for considering other concurrent engineering aspects, such as the convenience of assembly processes and the cost of maintenance. 3.7. Management of features In the feature-based design approach, when a real and specific product is involved, the management of features is even more challenging than feature definition. The quality of the management scheme will greatly affect the overall feature-based design processes from conceptualization through to system maintenance, and further to field service instructions. However, managing features has to have a mechanism that takes care of the embedded associations among features at different levels and updates the related features once a change is introduced. In fact, engineering changes can be initiated from either the upstream or the downstream processes. 3.8. Problem identification and the proposed solution Through the analysis of the design process and the relationships among the parts of the gearbox case, the authors identified a kind of hierarchical feature constraint patterns among feature parameters. If the hierarchical feature constraint relations can be modeled and effectively managed in a data structure, a coherent feature-oriented design system can be achieved. It can be appreciated that in a feature system, majority of those associative feature relations can

be mapped explicitly via the related parameters, represented in a feature parameter mapping diagram. Among these parameters, some are directly related to product specifications, performance or certain aspects of the design quality, and their values keep changing during the evolvement of design process. Obviously, the key technology required is to define and manage those feature relations involved in the whole design environment and throughout the product life cycles. Those parameters with associated constraints, either within the same feature, or with other features, can be identified and their relations can be conceptually modeled with a diagram that consists of parameter symbols and dependency links. The simple solution proposed here is a feature parameter map where parameter symbols are connected with arrows to indicate the dependency nature. During the engineering design evolvement, if the design features are changed, their parameters are changed. With the tracking of the feature parameter maps, other associated parameters, and in turn, the related features, can be updated automatically. If those features associated to certain product functions or quality aspects are tagged, represented and managed systematically according to the related specifications, then the product design information chains within the whole lifecycle can be persistently available and synchronously updated. Then eventually a meaningful generic feature system can be developed and managed for different product development and engineering projects. 4. Case study with feature parameter map To introduce the concept of feature parameter map in a more realistic scenario of design, in this section, the gearbox case shown in Fig. 2 is to be studied in details. The design processes are illustrated by applying feature-based methodology. Through this study the gearbox’s key conceptual structure, optimal bulk and some useful post-manufacturing information chains are to be developed. 4.1. Problem statement The gearbox was intended for the power transmission in an industrial belt transferring system of a food factory, as abstracted in Fig. 4. The given requirements are that: the diameter of the belt sheave is given as 0.2 m (D = 0.2 m); the output pulling force along the belt needs to be 1900 N (F = 1900 N); the working velocity of the belt is 1.5 m/s (v = 1.5 m/s); the maximum acceptable tolerance for belt velocity is 5%; and the power source is a electric motor to be selected. 4.2. Functions of the power transmission The following three main functions are identified: (a) To receive power from an electric motor through a coupling; (b) To transmit the power through the gearbox reducing the rotational speed to the desired value; and at the same time, obtaining the required torque; (c) To deliver the power at a lower speed to an output shaft which ultimately drives the machine of the next stage via a belt transfer. 4.3. Primary design parameters (1) Determine the power needed for the belt transfer.

P ¼ x  T ¼ F  v ¼ 1900 N  1:5 m=s ¼ 2:850 kW ð3:819 hpÞ (2) Work out the rotational speed of the belt sheave.

Nr ¼

60  v 60  1000  1:5 ¼ 143:221 rpm ¼ 2:39 r=s ¼ 3:142  200 pD

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(3) Based on the above parameters, an induction AC electric motor with three phases and four poles is then selected as the power source. The authors chose a National Electrical Manufacturers Association (NEMA) Premium™ motor [43]. Practically, in this case, the full-load rotational speed is

Nm ¼ 29:17 r=s ð1750 rpmÞ: 4.4. Selected design requirements Design requirements are collected and logically itemized from the statement of the problem. The input values are selected based on the related product specifications, experience as well as suggestions from collaborating engineers, manufacturers, sales managers, service staff, customers, etc. As to this case, some design requirements are as follows: (1) The power transmitted. Considering the efficiency of the gearbox as well as the motor, the gearbox must be designed to transmit a maximum power from the motor, which can be calculated as approximately:

Pem ¼ 3:819=kgb =kem ¼ 3:819=0:9=0:875 ¼ 4:850 hp ¼ 3:62 kW

(2)

(3) (4)

(5) (6) (7)

(8)

(9)

(10)

where, kgb ; kem refer to the efficiency factors of the gearbox and the electric motor [39] respectively. Finally, for the availability of an existing motor grade, a standard output of a typical electric motor is chosen, P EM ¼ 5:0 hp ¼ 3:73 kW . The input rotating speed is from the motor selected, whose shaft rotates at the full-load with 29.17 r/s (1750 rpm). The selected NEMA frame 184T motor, which has a shaft diameter of 28.58 mm (1.125 in.) and a keyway to accommodate a 6.35 mm  3.175 mm (1/4 in.  1/8 in.) key. The relevant dimensions and values can be found in [37,39]. The rotational speed change ratio of the gearbox can be determined as: VR ¼ N m =N r ¼ 1750=143:2 ¼ 12:2 . The belt sheave’s rotational speed is permitted to vary between 2.27 r/s (136.1 rpm) and 2.51 r/s (150.3 rpm) as the problem statement in Section 4.1. The speed reduction ratio will be correspondingly in the range of 11.6 and 12.9. A mechanical efficiency of greater than 90% is desirable. The belt transfer (working machine) works smoothly, but moderate shock may be encountered. The gearbox will be mounted on a rigid plate that maybe part of the base for the belt transfer. That means the installation pattern of the reducer should be specified. It has been decided that flexible couplings are to be used to connect between the motor shaft and the input shaft of the gearbox, and the output shaft of the gearbox to the belt sheave shaft of the belt transfer. The pairs of the interfacing shafts should have the same diameter. The gearbox should be enclosed, for the sake of a good operating environment. A compact size for the gearbox is desirable. A moderate cost is critical for market success.

After a series of trials of calculation and preliminary parameter selection for the power transmission system considering the primary design criteria, safety, cost, small size, high reliability, convenient maintenance, smooth operation, low noise, as well as low vibration, the final concept chosen was a two-stage spur gearbox. Comparing with other gearbox types, this type uses more outsourced standard parts, like pinions and gears. This advantage can reduce the time and cost for product design and manufacturing. In addition, the design work is also narrowed down to planning the

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gearbox layout; developing non-standard components like bulk shape, shafts and other accessory parts; and satisfying the basic design principles and the considerations for the product lifecycle. 4.5. Selected feature parameter maps As described in Sections 3.2, 4.1, 4.3 and 4.4, following a top-down design approach, more parametric design steps of the gearbox are carried out, through which more and more relevant parameters have been derived. At the same time, the designer needs a clear and comprehensive picture of the parameter dependencies among features, and hence a feature map concept is applied. For example, PEM, the final motor power, was selected based on Pem, the required maximum power. Pem, was in turn calculated based on the belt sheave power needed, P, and efficiency factors, kem and kgb. Fig. 5 shows the connections in the form of a feature parameter map, where the connections among parameters of the top conceptual feature are represented explicitly. In the figure, F is the required belt pulling force, V the belt speed. Further, Nr (nr in Fig. 5), the rotational speed of the belt sheave shaft, is determined by V, the sheave diameter D, and pi (p). Then the output torque, T, can be directly derived. To calculate the gearbox’s expected velocity ratio, VR, the number of speed reduction stages, steps motor’s rotational speed, Nm (nm in Fig. 5), and the required belt sheave rotational speed, Nr, have to be connected. Also, in the figure, it can be observed that velocity reduction distribution ratio between the two stages, KVR, determines the low stage ratio, VR(low), and the high one, VR(high). In turn, the speeds for the pinion and gear of each stage are determined; they are SGL and SPL, and SGH and SPH respectively. In different stages of the whole design process, many feature parameter maps could be used. Here two more feature parameter maps are described to test the general applicability for different design consideration. Fig. 6 shows a feature parameter map for the pinion and gear pair design at the high speed stage. Assume the pressure angle u1 is 20 degrees, after selecting the number of teeth for the pinion, NP1, according to the speed ratio VR(high) which has been worked out from the previous step, the number of teeth for the gear can be calculated, i.e. NG1. When considering the approximation introduced by selecting the available number of teeth, then the actual speed ratio, VR(ah), can be determined. Based on the torque transmitted and the availability, the diametric pitch, Pd1, can be selected out of the standard choices. From the pitch, the teeth characteristic parameters [37], such as addendum, a1, dedendum, b1, and the working depth, t1, become known. Hence, the face width, F1 and the tooth thickness, t1, can be derived. Further, according to the numbers of teeth for the pinion and the gear, their pitch diameters, DP1 and DG1 are available. Taking the pressure angle u1 into consideration, the diameters of the basic circles for the pinion and gear can be obtained, i.e. DBP1 and DBG1. Naturally, based on the pitch radii, RP1 and RG1, the center

Fig. 5. Feature parameter map for top conceptual feature parameter calculations.

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Fig. 6. Feature parameter map for high-speed gear design. Fig. 8. Constraint-driven model (the inner housing cavity).

distance of the pair, C1, is achieved. For manufacturing purpose, the outside diameters of the pinion and gear, i.e. DOP1 and DOG1, are also worked out from their corresponding addendums and pitch diameters. Similarly, their root diameters, DRP1 and DRG1 are available based on the dedendums and their pitch diameters. More detailed parameters, such as clearance, c1, and total tooth depth, ht1, can be known from the difference and the sum of the addendum, a1, and the dedendum, b1. A similar feature parameter map for the low speed stage pair can be developed as well. Note that the feature parameter maps for the higher and lower stage design reflect the parametric dependencies, or constraints, at the gear pair assembly level, and can then be then associated to the corresponding assembly features. The housing design is another important aspect in which the space occupied, stability of the installation in operation, and bearing seats constraints need to be considered. Fig. 7 shows the parametric relations used for housing design. Clearly, it shows how the length, width, and height of the inner envelope box, i.e. LOG, WOG and HOG, are worked out. In addition, since the mounting length, X, and width, Y are used for the mounting hole design as the key parameters of the mounting feature, they are associated as another assembly feature (AF). To explain the relations shown in the figure, the geometry of the inner cavity of the gearbox housing has to be studied. Assume that the center line between the gears of the low stage determines the gearbox length direction; the overall 3D layout of the gearbox can be determined from the first feature; the inner housing cavity envelope is shown in Fig. 8. The inner cavity of the housing was constructed following the principle of constraintdriven design modeling. Here, C1: center distance of high speed stage. C2: center distance of low speed stage.

Fig. 7. Feature parameter map for housing dimension generation.

C3: the third side of the three-axial triangle. RG1: the radius of gear 1. RG2: the radius of gear 2. K1: the clearance, from the gear to the housing inner wall. h  i a1 ¼ arccos C 21 þ C 22  C 23 =2  C 1  C 2 .

a2 ¼ arcsin ½ðRG2  RG1Þ=C 2 . In Fig. 8, the three edges of the triangle are C1; C2; C3. The three vertexes of the triangle are the centers of the three shafts. The outer curve is the skeleton curve of the housing inner cavity. The three curve segments are offset edges, with the offset value K1, from the three gears’ tangential enclosing skeleton of which the three straight segments are the tangent lines between those three gear circles. By doing so, the optimal housing shape, and further, the final optimal gearbox bulk shape are developed. Coming back to the feature parameter map shown in Fig. 7. Based on the gear center distance, C2, the diameter of the install bolt, Dib, is determined such that the required clamping force and the stability across the mounting platform are considered. From Dib, the diameter of the connecting bolts (refer to Table 2 in the next section), Dcb, of the component item No. 15 as shown in Fig. 2, can be determined according to some empirical formulas. The height of the count feet, hmf, is proportionally related to Dib. Note that after considering the clearance between the largest gear to the inner wall of the gearbox, k1, the maximum diameter of the two gears, i.e. Dmax = max(DG1, DG2), and the count feet determine the height for the gearbox inner cavity, HOG. The length of the gearbox inner cavity, LOG, is collectively determined according to HOG, k1, and the width of the flange (also refer to Table 2), Wf, which is set proportional to Dcb. Relatively, the width of the inner cavity, WOG, is straight forward. It is related to the thicknesses of the gears, bearing seat width, Wbs, and the bearing seat factor, k4, for the associated cover, wall thickness, attaching bolt cap height. Obviously, the width of bearing seat, Wbs, is determined by the bearing width, Wb. The wall thickness of the casing, i.e. TW (mm) is calculated with an equivalent dimension, N, which is made empirically related to the average of HOG, WOG, and LOG. Fig. 9 displays the gearbox mount pattern, as the response to design requirement number 7 in Section 4.4. In this case, the gearbox needs four installation bolts, which mount on a rectangle platform with the dimension X  Y. By doing so, qualified project managers can get this information at the first time, which allows them to manage the proper installation preparation on either the working machine platform or a separate piece of space in advance. Similarly, as shown in Fig. 7, the mounting dimensions in length and width directions respectively, i.e. X and Y, can be worked out. Note that in these feature parameter maps, different colors of the parameters indicate different roles or levels within the whole

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C.-G. Yin, Y.-S. Ma / Advanced Engineering Informatics 26 (2012) 539–552 Table 2 Classification of features. Classification

Parameter description

Conceptual feature (CF)

A CF is a set of conceptual relations that are established to satisfy the customer major requirements and the functionality of the design. Parameters involved in such CFs are those primary options within the design process, and to influence downstream design processes. For example, N r ; N m (see Fig. 5 and Section 4.5) are such parameters

Assembly feature (AF)

Parameters used in AFs represent the key assembly characteristics or quality requirements, and greatly influence the assembly process and overall or modular shape and dimensions. For example: C1; C2 (see Figs. 6 and 8)

Component basic feature (CBF)

A CBF represent the main skeleton feature of the part. The characteristic parameters essentially control the skeleton, or the overall layout of the part features. For example: R1; R2; R3 of the inner cavity profile are controlling dimensions for the housing part

Component detailed feature (CDF)

CDFs are those additional features of a part, for the sake of manufacturing, assembly clamping, maintenance, and so forth. For example: Dcb; r; Wf , shown in the picture of the next right cell

Fig. 9. Mount pattern.

product design. Green represents important inputs or primary conceptual features and assembly features; grey means intermediate results; while red items display that they are very important detail feature parameters that result from the calculations.

5. Systematic study for developing the solution After identifying the feature parameter maps and applying them in the case study, it is clear that the concept is useful for explicitly visualizing the feature dependencies. Then, this kind of diagram-based feature parameter maps must be implemented into a computerized model by a parameter constraint management tool that can maintain the concise mathematical and logical relationships. By doing so, such feature diagrams can be concisely constructed. In addition, a solver tool is needed for evaluating all the parametric constraints. The authors achieved this goal by using spreadsheets in Excel, which has enough capabilities and functions to deal with majority of the mathematical and logical calculations and analyses at the

Examples

complex level involved in this research, after comparing with more complex database tools. In a spreadsheet, parameter relations are embedded into formulas which are automatically updated. Note that circular references are checked in Excel automatically by the software as a default function available in the package. Therefore, the Excel can assure that there is no circular reference. In the feature parameter map definition process, consistent parameter definitions must be used such that they can be easily expressed in a feature-based programming system for engineering analysis and easily understood by the designers as well as other downstream technical supporters. A set of parameters designed to be interfaced with CAD parametric models in part or assembly levels is specifically maintained in such a way that those parameters are used to update CAD models automatically by the expression synchronization mechanism that is available in the CAD tool. All the parameters’ owning feature tags are recorded as attributes in the spreadsheet. Feature parameter maps can be constructed horizontally among the features at the same level, e.g. the conceptual level or detailed geometry level, or vertically among features of different levels. The choice is up to the feature management strategy adopted in a design project. Sometimes, the authors find that a common feature can play effective roles in different perspectives. After the feature parameter maps are constructed, the priority level of each parameter is determined according to the influence relationships. These priority level numbers are useful when a parameter updating conflict occurs. Different results can be generated when different change propagation sequences are used. 5.1. The proposed feature parameter map scheme Within the whole design process of a typical product, it can be expected that many parametric relations are identified, implemented, evaluated, modified, edited, transferred and deleted.

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Therefore, a unified feature parameter map representation, modeling and management system is required. Feature parameter maps can be abstracted as a class that consists of a set of related engineering parameters that are used in different features at different stages of the design process and in different aspects; their dependencies are represented in a graph where each individual dependency relation is represented by an arrowed line and parameters as the nodes. Such arrowed lines are indexed and cross-linked corresponding to those related constraints that are modeled and indexed in spreadsheets separately. In a real application, each parameter map is initiated based on designers’ engineering formulas, procedures, and semantic patterns. The application of feature parameter maps has been demonstrated in Section 4.5 with the concept, mechanisms, and possible scenarios. Note that this paper reports the proposed general scheme framework only; a formal software development project is necessary in order to support the proposed approach robustly in a real product development environment. Actually, one important application of feature parameter maps is to manage each ‘‘design for X’ aspect coherently in a comprehensive and unified feature-based model for concurrent engineering. Feature parameter maps are the micro information links that connect and dynamically support the information sharing function such that product model consistency is maintained via this explicit modeling mechanism. For example, a set of feature parameter maps can collectively form useful information chains for the analysis and management of a product’s post-manufacture services. Such an information support capability serves the goal of extending feature technology to product lifecycle management as well as embodying the engineering principles at the conceptual design level. In this work, two information chains for post-manufacturing aspects, i.e. field installation and product packaging, are considered. As a part of the proposed methodology, it is suggested that all features involved in the product design are constructed into an organic and synchronous information flow network where feature parameter maps are a common type of connecting cells. In addition, different feature parameter maps help to create multiple information views for designers, managers, and collaborative companies, which support a product’s different stages of lifecycle.

5.2. The proposed feature-based design procedure The first stage of the design process is problem analysis. All important design parameters have to be carefully collected and managed. Here, the semantic representations and the values of the design parameters are associated with concept design features. With such conceptual features being specified by suitable parameters and values, the degree of uncertainty in the design problem will be reduced progressively in iterations. Finally, a well-defined product concept model, together with its important and influential features as well as potential options and configurations [44], is established and transferred to the next step of design. Next, along the stages of evolvement of detailing parametrically in the feature-based design approach, conceptual features are gradually enriched; and detailed features in individual components are constructed. Among these features, a hierarchical system of feature parameter maps can then be worked out. Further, a mathematical and logical engineering analysis programming effort, with the support of a spreadsheet or a database, can be carried out. The feature mapping and engineering analysis programming are the two sides of a coin; they provide necessary verification and information flow to each other. Feature parameter maps can be represented in any graphical representation tool. In this work, MS PowerPoint was used for feature parameter maps, while

engineering analysis programming is developed in an MS Excel spreadsheet. Potentially, a unified software tool can be developed. After the previous two steps of analysis and calculation, the design output parameters are consolidated into another spreadsheet, and these parameters are explicitly associated to important product modeling aspects and their relevant implementation features in a parametric CAD environment. In such a feature-enabled design environment, the product’s physical geometry modeling makes use of those parameters of features calculated in the constraint solver as the input. In addition, at the CAD modeling process, physical geometric constraints are imposed because component features are often affiliated to part bodies while other advanced features, like conceptual features and assembly features are associated to the product design patterns and assembly modules. Eventually, the final product is developed with all the detailed model features. In addition to engineering design equations and solutions, CAD models, software programs, data sheets and documents, the useful information chains defined as per product lifecycle support requirement, will be also recorded and generated as an important design output in a document format. Essentially, feature parameter maps represent the constraint relations at a finer granularity level than features in a consistent and explicit manner. Within the design process, a constraint-driven design method can be applied, and at the same time, design can make full use of the advantages of feature-based technology. The potential goal is to achieve the information synchronization automatically by capturing design rules at different design stages such as concept design, detail design, redesign, manufacturing, distribution, delivering, field service, and maintenance. 5.3. Features classification in the case study Just as the feature definition in Section 2 indicates, any semantically self-contained engineering design information pattern (or an information carrier) can be defined as a feature. In a broad picture of engineering reasoning, all those sets of well-constrained design elements, including specifications, parameters, referencing datum, construction elements of design, and geometrical entities, are engineering features. Their relations represented involve design elements from the initial requirements of the problem to the detail element dimensions across different design stages. Although the general definition of feature has become more mature through the last two decades as reviewed in Sections 1 and 2, there is a knowledge gap between the formal definition of features at different levels and the real application of a product. Within a product, there could be hundreds of features at a number of levels. This paper classifies them into the following categories: (a) conceptual features (CFs), (b) assembly features (AFs), (c) component basic features (CBFs), and (d) component detailed features (CDFs). More importantly, the question of how the parameters are embedded in features needs to be answered. This section tries to bridge the gap by listing some examples in terms of the levels; they are defined by the authors supporting feature-based gearbox design process. Table 2 gives four levels of features and some examples of their parameters used in the specific gearbox design case. Conceptual features (CFs) refer to those features that are created at the top of the hierarchical feature system. Most CFs come from the abstract solution models corresponding to the requirements of the problem or major aspects of design, or the initial choices of the design frame work. The parameters involved in CFs are critical for the design as inputs or specifications. For example, the electric motor’s speed Nm and the gearbox output rotational speed, Nr (see Fig. 5) are two major specifications involved in the top conceptual feature of the gearbox. In this case, the two parameters Nm and Nr are 29.16 r/s (1750 rpm) and 2.387 r/s (143.221 rpm) respectively,

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as mentioned in Section 4.3. The gearbox must serve the purpose of speed conversion and power transmission. It can be appreciated that one of the most important CFs identified is the total rotational ratio, VR. It influences the whole design process; i.e. to reduce the speed from N m to N r , and the key parameter, VR ¼ N m =N b ¼ 1750143:221 ¼ 12:219. This feature is defined by pairs of spur gears. Considering the VR value of 12.219, a two-step gearbox was suggested, which has a rotational ratio range from 5 to 25 roughly. Assembly features (AFs) are those features that reflect the relations among design elements that cross different physical parts or subassembly modules. The theoretical aspects have been addressed in [22]. The constraints modeled in such features, typically, key parameters or dimensions, greatly influence the assembly quality, assembly processes, the total bulk properties, such as the shape and the overall dimensions. For example, Fig. 8 represents a typical assembly feature while the center distances of the two stages in this gearbox, C1, C2, are two of the feature parameters included. The associative AF could be defined by the spatial axial arrangement associated with the pairs of the engaging gears. The constraints shown in Fig. 8, especially the triangle relations indicate some key parameters. As we all know, the two center distances will influence the relevant locations of the three shafts and the four gears within the gearbox. Furthermore, a series of important features of those accessory parts, like casing, bearings, bearing covers, will be defined, to a great content, by this AF. As an example, the low-speed stage of center distance C2, as one of the most influential AF parameters in this case, influences a lot of down-stream features (see Figs. 9–12). It firstly decides the diameter value of all the install bolts (Dib = 0.1  C2) which, in turn, has an influence on the diameter of the connecting bolts (Dcb = 0.6  Dib = 0.6  0.1  C2). In addition, the width of the flange of the housing case, Wf, is associated with the center distance C2, with the relationship given by Wf = 4  Dcb = 4(0.6  Dib) = 4(0.6(0.1  C2)). Specific and detailed explanation of the calculation foundation can be found in reference [40]. Component basic features (CBFs) are the main skeleton features of a part. They are the most primary features within an individual part. The three radii, R1, R2, R3, shown in the figure in CBF row of Table 2, are an example of those key parameters in such CBFs. On one hand, they are influenced by the higher level features, like the spatial axial layout of the shafts, via their parameters, C1 and C2. On the other hand, they decide the whole shape of the housing component. Furthermore, they have influence on the down-stream CDFs, for instance, the layouts of connecting bolts and the installation bolts.

Fig. 10. Constraint-driven gear-box shape.

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Fig. 11. Gearbox wrapping dimensions (HOGw, LOGw, and WOGw are the height, length and width of the wrapping box respectively).

Fig. 12. Redesign gearbox bulk (partially loaded).

Component detail features (CDFs) refer to those additional features within a part, like the edge blend, face blend and connecting bolt pattern as well. At this level, the feature definition, in this paper, is very similar to those commonly used in commercial 3D CAD software tools like Siemens NX. The main purpose of such CDFs is to make the manufacturing and assembly process smooth or to support the functioning design elements. For example, the width of flange (Wf) should be sufficient to support the whole gearbox, in both static strength and dynamic behavior; especially if it has the sufficient vibration absorption when the machine is working. Most of the available 3D CAD software tools can easily support the generation and parametric calculations of CBFs and CDFs. In this work, the authors built the solid models in the Siemens NX6.0 environment, which has the required expression management and calculation capability as well as the functions to generate the detailed features of components via programming. It also has a convenient design environment for importing external expressions via Excel spreadsheets. Using this function, we can input the important outcomes of spreadsheet into the CAD modeling environment. At the detailing stage of the design, all the CBFs and CDFs involved in the whole gearbox are realized in the NX package. via Excel and CAD integration, CBFs and CDFs are further associated with higher level feature parameters in the rest of product development iterations. In terms of the CFs [14,26,41] and AFs [22,25], which are more advanced and complex at a higher abstract level, the authors implemented them in an Excel spreadsheet with some enhancement via programming. The feature parameters were calculated and optimized. Excel has the primary functions like an entry level

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database. As shown in Fig. 3, by using a Microsoft Office automation tool, Notepad++, the majority of calculations and logical judgments among those feature parameters have been done. The final outcome parameters were exported to another Excel spreadsheet, which was used as the feature input data source into NX CAD software for modeling the solid components and assembly; and then some more detail features and gearbox bulk features were further constructed and led to a completed product solid model. Through the embedded calculations within the spreadsheet, different features at all levels involved in the process of the gearbox design have been constrained and associated together organically. At the same time, specifically selected output features at lower levels are directly synchronized with NX expression parameters. Most of such parameters correspond to CBFs, CDFs, and some modular AFs. It can be appreciated that for the purpose of visibility in future redesign with some automatic and parametric change propagation, all detailed features can be associated if required via feature parameter maps; they are supposed to express relations explicitly among constraint equations expressed either in the Excel or NX software among the existing features, namely, CFs, AFs, CBFs, and CDFs. The proposed design system structure as shown in Fig. 3 has been proven working, but by no means the only nor the best configuration. In fact, many 3D modeling software tools have been capable to support feature-based design. Users can set up the feature relationships represented in the form of feature parameter maps with any usable database tools; and users can manage the levels of features as per the specific application complexity and configuration possibilities within the whole design process in order to achieve an optimal implementation output.

project manager at an early time to arrange packaging, storage, shipping, distribution, installation, etc. The three major dimensions of the enclosing wrapping box for the designed gearbox can be calculated as follows. HOGw ¼ 4:2  TW þ 2  WF þ RG1 þ RP1 þ 2  k1 þ C1  sinða1 þ a2 Þ LOGw ¼ C2  cosða2 Þ þ RG1 þ RG2 þ 2  k1 þ 2TW þ 2  WF WOGw ¼ F1 þ F2 þ 2  k2 þ k3 þ 2  6:5  TW The above equations among the relevant parameters and expressions are implemented in a spreadsheet. Throughout the gearbox case, all of the useful parameter constraints of features are modeled, referred, tracked and updated across the product modeling and design stages either interactively or automatically with a system. It has also been shown in practice that certain parameter analysis and optimization can be carried out by incorporating programmed calculations. Eventually, a completed featurebased gearbox model with fully associated design constraints among parameters was developed. In any real application in the industry, such a fully developed and knowledge driven design case can be a valuable asset for engineering know-how capture and reuse by the company. 6. Redesign cycle demonstration To evaluate the capability of redesign based on the existing prototype model, the authors changed some values of CF, subsequently, a different layout of the three shafts is achieved with little human intervention; and so is the housing shape as shown in Fig. 12, comparable with Fig. 9. Due to space limitations, this section is purposely brief.

5.4. Constraint-driven gear-box model 7. Conclusions and future work Considering the 9th and 10th design requirements introduced in Section 4.4, a small and compact size for the gearbox is expected. At the same time, the gearbox should be enclosed for a good operating environment. In designing the casing profile, a constraint-driven design method is applied in order to minimize the bulk shape. Fig. 10 gives a picture of the gearbox bulk shape. After designing the bulk shape, some more components are added and assembled, inheriting the layout of the inner cavity profile as shown in Fig. 8 which serves as a common CBF for the both pieces of the housing. In addition, more component detailed features (CDFs) were added to complete the whole gearbox model. 5.5. Other useful information chains To embody the principle of product lifecycle management and concurrent engineering within the iteration of feature-based design methodology (see Fig. 3), some associated and valuable information chains about the gearbox are generated at the same time, which will make product-relevant information available to collaborative team members in the right time, avoiding unnecessary waiting time within the collaborative environment and throughout the whole lifecycle. Similar efforts were made by Oliver who tried to work out gearbox weight as a managed parameter [42] and by Song et al. [20] in field pipeline system transportation. In this paper, two important information chains, i.e. the field installation mounting pattern and the package dimensions, are demonstrated. The mounting pattern, represented by X and Y as shown in Fig. 9, can be always maintained as the key installation dimensions and made available to all sales, delivery staff and customers. For the packaging dimensions, Fig. 11 demonstrates the whole product wrapping or packaging geometry and the key dimensions in the early process of design, which makes them available to the

In the framework of feature-based design methodology, this research suggests an explicit method for modeling parametric constraints among different features. Following a general constraint relation representation, which we call feature parameter maps, with an implemented prototype system, lots of repetitive constraint calculations are systematically managed with good automation support. Constraint dependencies are then implemented in MS Excel spreadsheets which, in turn, are integrated with a 3D CAD system via feature expressions. In addition, this paper classifies features into four categories corresponding to different semantic abstract levels in design processes, definitions and functions. They are conceptual features (CFs), assembly features (AFs), component basic features (CBFs), and component detail features (CDFs). This classification can be useful for the developers or the end-users of a future software tool, who can apply the scheme to conceptualize their design tasks, develop an effective feature system and manage numerous feature relations. In summary, feature parameter maps provide a form of general semantic representation that can embody the hierarchical and coherent relationships among features of different levels in the product design and analysis process. As for implementation, a case study with a typical gearbox design is presented. Feature dependencies in the form of parametric relations are represented with feature parameter maps and they can be systematically modeled in a spreadsheet data structure and work well with the NX CAD software tool. In the case study, a set of hierarchical feature parameter maps were constructed and a logical program using the MS Excel spreadsheet tool was developed. It has been shown that the feature parameter map concept works well with the constraint-driven feature-based design method in a CAD environment. It has been demonstrated that

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the proposed method can also provide timely product-related information through information chains to collaborating departments or companies. The paper has shown that, once the optimal gearbox bulk shape is defined, then via information chains, the field-installation pattern and product delivery dimensions can be made available instantly; this scenario supports the principles of PLM and concurrent engineering. Through limited automation of theoretical analysis and CAD modeling, with the authors’ experience of case studies as shown in this paper, it is understood that the proposed feature parameter mapping method can manage feature dependencies more effectively and enable highly efficient product redesign. This advantage helps NPD and product post-manufacture services. There might be more effort put in for the initial set-up than the traditional approach due to the conversion process from human knowledge into feature representations. However, the method shows a significant advantage that the designer can generate different acceptable design alternatives within a very short regeneration time in the product redesign or innovation stage. Following the general and explicit constraint relation representation, i.e. feature parameter map, with the implemented prototype system, lots of repetitive constraint calculations are systematically managed with a high level of design automation, and a product’s time-to-market (TTM) can be reduced by saving product design and redesign cost as well as the downstream information support for product-to-market priority and optimal supply chain collaboration. The proposed method is believed to have potential economic benefits for the industry. From application point of view, we assume that the designer knows the status of the design process and makes the calls for starting and ending a design stage or a project. The proposed method could assist the designer to ensure the constraints and feature parameter maps to be tally and consistent. Managing and automating such checking functions is a future research task. Currently, the algorithm for verifying the feature parameter maps has not yet been developed. However, the authors believe the procedure is feasible. Acknowledgements The research work reflected in this paper was mainly carried out at University of Alberta (U of A) during the period from June 2009 to June 2010, where Mr. Yin was attached at U of A under the supervision of Dr. Yongsheng Ma. It was jointly funded by the China Scholarship Council (CSC) grant (No. 2008635512) and Canada NSERC discovery grant (No. 355454-09). The gearbox assembly drawing shown in Fig. 2 was created in an undergraduate course project supervised by Mr. Yin in 2007 at China Agricultural University. References [1] W.O. Schotborgh, Knowledge engineering for design automation, Ph.D. thesis, University of Twente, Enschede, 2009. [2] B.J.M. Jauregui, From how much to how many: managing complexity in routine design automation, Ph.D. thesis, University of Twente, Enschede, 2010. [3] A. Saaksvuori, A. Immonen, Product Lifecycle Management, Springer, 2008. [4] M. Grieves, Product Lifecycle Management – Driving the Next Generation of Lean Thinking, McGraw-Hill, Toronto, 2006. [5] C.H. Chu, P.H. Wu, Y.C. Hsu, Multi-agent collaborative 3D design with geometric model at different levels of detail, Robotics and ComputerIntegrated Manufacturing 25 (2) (2009) 334–347. [6] C. Qi, Y. Wang, Feature-based crystal construction in computer-aided nanodesign, Computer-Aided Design 41 (11) (2009) 792–800. [7] Y.-S. Ma, G. Chen, G. Thimm, Fine grain feature associations in collaborative design and manufacturing – a new modelling approach, in: L.H. Wang, A.Y.C. Nee (Eds.), Collaborative Design and Planning for Digital Manufacturing, Springer, 2009, pp. 71–97. [8] J. Shah, Assessment of feature technology, Computer Aided Design 23 (5) (1991) 331–343.

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[38] J.A. Collins, Mechanical Design of Machine Elements and Machines, John Wiley, 2010. [39] H.W. Beaty, J.L. Kirley Jr., Electric Motor Handbook, Chapter 4, Table 4.12, p108, Figure 4.13 p54, Table 4.16, p112, 1998. [40] B.M. Klebanov, D.M. Barlam, F.E. Nystrom, Machine Elements: Life and Design, CRC Press, 2007. [41] V. Stamati, I. Fudos, A parametric feature-based CAD system for reproducing traditional pierced jewellery, Computer-Aided Design 37 (4) (2005) 431–449. [42] M.R. Bohm, J.P. Vucovich, R.B. Stone, Using a design repository to drive concept generation, Journal of Computing and Information Science in Engineering 8 (1) (2008) 0145021–0145028. [43] NEMA Premium Motors, . [44] A.V. Olver, The effect of configuration in the design of geared transmission systems, Journal of Mechanical Design 131 (7) (2009) 074504-1–074504-5. C.-G. Yin is currently a senior lecturer with the Department of Mechanical Design and Manufacture, School of Engineering, China Agricultural University, PR China. He received his B.Eng. in 2000 from Shenyang Ligong University, and obtained his M.Eng. in 2003 from Beijing Institute of Technology. He had been a visiting scholar

at University of Alberta, as a scholarship award recipient, funded by China Scholarship Council (CSC), from June 2009 to June 2010. His research interests include feature-based design and modeling, new product development process, industrial process modeling and simulation, and product lifecycle management. Y.-S. Ma is currently a tenured Associate Professor and registered Professional Engineer with the Dept. of Mechanical Engineering, University of Alberta, Canada. He teaches capstone design projects, engineering economics and manufacturing processes. His main research areas include feature-based product and process modeling, CADCAM, and product lifecycle management. Dr. Ma received his B.Eng. degree from Tsinghua University, Beijing in 1986, M.Sc. and Ph.D. degrees from UMIST, UK in 1990 and 1994 respectively. Before joining U of A, from 2000 to 2007, he had been an Associate Professor in the school of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore. He is currently an associate editor of IEEE Transaction of Automation Science and Engineering, and actively serves the engineering informatics research community. He started his career as a Lecturer from Ngee Ann Polytechnic, Singapore in 1993, and then from 1996 to 2000, he was a Senior Research Fellow and Group Manager at Singapore Institute of Manufacturing Technology (SIMTech).