Chapter Literature Review 2.1 Introduction In this research, four topics are addressed, viz., the definition of integrative features, the feature-based approach, the optimization of set-up planning, and the robust fixture layout In this context, feature-based approaches include integrative feature definition, machining feature extraction and fixturing-feature-based approach Integrative features are to be defined in Chapter Research on machining feature extraction has been well addressed, while fixturing-feature-based approach to a much lesser extent Therefore, in this chapter, the previous research on fixturing-feature-based approach, together with set-up planning and fixture layout are reviewed in detail After each review section, a short discussion is made, followed by a summary at the end of this chapter 2.2 Fixturing-feature-based Approaches Fixtures are devices used to locate and hold workpieces in manufacturing operations Fulfilling fixturing requirement during machining is as important as the operations and tools in the manufacture of a part (Ong and Nee, 1994) 14 Chapter Literature Review Feature-based technology is found to be feasible for the integration of CAD/CAM/CAPP segments due to its ability to capture the designer’s intent from one stage to the other of product development (Shah, 1990) It is important for achieving a true integration of design and manufacturing stages during early product development The manufacturing information needed from design models include the retrieval of machining features with technological specifications (dimensions, tolerances, etc.) with respect to the machining operations, and fixturing features which are holes, recesses and facets on a part for providing places for locating and clamping the workpiece during the machining processes Research on machining feature extraction has been well addressed, while fixturingfeature-based approach to a much lesser extent Trappey et al (1990) developed an automatic fixture configuration system using a projective spatial occupancy enumeration (PSOE) approach The PSOE approach uses the 2D projection of a 3D object for the selection of locating and clamping positions for a possible fixture configuration This research emphasized that PSOE could deal with arbitrarily-shaped workpieces, and established several algorithms for searching fixturing positions However, in using PSOE, one may lose the 3D geometric information of the workpiece and lead to unrealistic solutions A feature-based methodology for fixture design was developed to select fixturing faces and elements efficiently by Dong (1991) He investigated the use of features for fixture design, concentrating on the selection of locating elements and the identification of locating surfaces for workpiece positioning 15 Chapter Literature Review A feature-based classification scheme using a 3D solid modeler was presented It uses a feature extractor and an object-oriented system shell (Nee et al, 1992) Its intended use lies in variant fixture design as well as its association with machining operations, machining environment, cutting tools and workpiece features The operations begin with a solid model where machining features are extracted and grouped into set-ups based on machining directions and tolerance factors A knowledge base is used to infer the operations involved and the cutting tools for a giving machining environment Kumar et al (1992) developed a feature recognizer for extracting machining features represented in a CAD model A rule/object-based approach was used to group the machining features into appropriate fixture set-ups, and suitable clamping, locating and supporting points are recommended The fixturing elements are then selected and assembly sequences are planned A knowledge-based approach was used to reason the clamping, locating and supporting faces for a set up Fuh et al (1993) presented an approach to computer-assisted fixture planning, emphasizing the integration of fixture planning with process planning A rule-based approach was presented to determine planar locating and clamping surfaces of a workpiece for a given machining operation on a three-axis vertical milling machine Chou et al (1994) presented a method to identify fixturing features for a given operation plan during the conceptual design stage of fixture planning Based on the surface reasoning method, five common fixturing features are identified and defined 16 Chapter Literature Review They are extreme surfaces, unobstructed surfaces, locating holes, corners and flanges Fixture functions are also assigned to each fixturing feature, such as hole-location, Vblock, and 3-2-1 locations One design strategy is developed for each mentioned fixture other than a general strategy for all types of fixtures The system can be used for prismatic parts consisting of primarily flat and cylindrical surfaces Roy and Sun (1994) presented a fixture configuration method using heuristic algorithms for selecting the locating and clamping positions for a given workpiece in an automatic fixture design (AFD) system (cutting force direction as the main factor to determine the primary locating surfaces) It uses several geometric reasoning mechanisms based on traditional fixture design principles, such as the 3-2-1 locating principle and collision-free assembly Form features have been used besides the planar surfaces in the research reported by Ong and Nee (1994, 1996, 1997, 1998), where a methodology was presented for the quantitative evaluation of the fixturing properties of features (both planar surfaces and form features) on a part such as clamping, location and/or supporting features with the use of fuzzy membership functions The system was designed for machining prismatic parts on a 3-axis vertical machining center Table 2.1 summaries the approaches, the types of fixturing features and workpieces considered by other researchers 17 Chapter Literature Review Table 2.1 Summary of fixturing-feature-based approaches in literature Researchers Trappey et al (1990) Dong (1991) Nee et al (1992) Automatic Feature Extraction Approach Projective spatial occupation enumeration (POSE) Design information reasoning Feature-based classification scheme Kumar et al (1992) Fuh et al (1993) Rule/object-based Rational/rule-based Chou et al (1994) Surface reasoning Roy and Sun (1994) Ong and Nee (1994, 1996, 1997, 1998) Geometric reasoning Fuzzy membership functions Types of Fixturing Feature Planar surfaces Types of Workpiece Arbitrarilyshaped Planar surfaces Prismatic Planar surfaces, Prismatic, Cylindrical surface Rotational, Special Planar surfaces Prismatic Planar surfaces Prismatic Cylindrical surface Planar surface, Prismatic Form features Rotational Planar surface Prismatic Form features, Prismatic Planar surfaces In general, feature-based design can be achieved through three methods:  Automatic feature extraction  Interactive feature definition  Design-by-feature Researchers mostly agree that an ideal feature-based system should provide an environment for the design-by-feature approach, in combination with feature extraction and interactive feature definition Based on previous research, fixturing feature extraction is the popular approach while design-by-fixturing-feature approach is less addressed A CAD model can usually provide sufficient information for fixturing, either as geometric objects or with the associated technical information Therefore, it is logical to have automatic extraction 18 Chapter Literature Review of fixturing features from the CAD model in the first instance If there are insufficient fixturing features for manufacturing purposes from the extraction results, additional fixturing features should be defined or designed to ensure the manufacturing processes are achievable Therefore, it is desirable to apply a hybrid of fixturingfeature extraction with design-by-fixturing-feature and interactive fixturing feature definition approaches In this research, a hybrid fixturing-feature-based approach is adopted to obtain fixturing features for the integration of design and manufacturing, which uses the above-mentioned three approaches 2.3 Set-up Planning Set-up planning is a function of both process planning and fixture design (Ong and Nee, 1994) It should consider both design specifications and manufacturing resources Design specifications include workpiece geometry, dimension, tolerance, and features Manufacturing resources include available production equipment, cutting tools, and fixtures A set-up plan which considers these two factors can ensure the delivery of the product with not only high quality but also high throughput and low cost From literature, various set-up planning approaches have been applied to meet the design specifications of workpieces in terms of tolerance analysis, precedence constraint satisfaction, geometric data analysis, and tool access direction verification 19 Chapter Literature Review Fuzzy sets theory was used by Ong et al (1994, 1996, 1997, 1998, 2000) to present the geometrical, tolerance and fixturing relations, machining requirements, design features, etc., in the set-up planning systems for manufacturability and fixturability evaluation Zhang et al (1995) proposed a hybrid heuristic-based and optimization approach, in which various constraints other than tolerances in set-up planning are identified and discussed Precedence relationships among the features have been analyzed by Ong et al (2002) to generate a precedence relationship matrix This matrix acts as the main constraints for set-up planning optimization Most of the research studies have adopted tolerance analysis as the main criterion in set-up generation and sequencing Boerma and Kals (1988) reported on the development of a computer-aided planning system for the selection of set-ups and the design of fixtures in part manufacturing The automated selection of set-ups is based on the comparison of the tolerance relations between the different shape elements of the part A tolerance factor has been developed to compare the effect of different tolerances The system selects the positioning faces automatically and supports the selection of tools for positioning, clamping and supporting the part 20 Chapter Literature Review Zhang et al (1996) and Huang et al (1997) discussed the importance of set-up planning in relation to tolerance control in process planning A graphical approach was proposed to generate optimal set-up plans based on design tolerance specifications Wu and Chang (1998) described an approach that uses the tolerance specification in a feature-based design system to generate set-up plans with explicit datum elements The focus of this research is an automatic tolerance analysis approach for selecting set-ups and datum for prismatic workpieces in the design system Zhang and Lin (1999) introduced a systematic approach for automatic set-up planning in CAPP The concept of “hybrid graph”, which can be transferred into directed graph by changing any two-way edge into one-way edge, is introduced Tolerance relations are used as critical constraints for set-up planning Lin et al (1999) developed a variant CAPP system with tolerance charts to automate the generation of operation illustration for aircraft components Zhang et al (2001) employed an extended graph to describe a Feature and Tolerance Relationship Graph (FTG) and a Datum and Machining Feature Relationship Graph (DMG), which could be transferred to an analytical computer model, and a tolerance decomposition model to partition a tolerance into interoperable machining errors These could be used for locating error analysis or for feedback to the design stage for design improvement 21 Chapter Literature Review Tseng and Huang (2007) presented a multi-plant tolerance allocation model to determine the working tolerance of each of the components by considering all the feasible manufacturing operations of the available plants The primary objective is to maximize the cumulative sum of the working tolerances Hebbal and Mehta (2007) focused on the development of a formalized procedure for automatic generation of feasible set-ups and selection of an optimal set-up plan for machining the features of a given prismatic part The proposed work considers simultaneously the basic concepts of set-up planning from both machining and fixturing viewpoints in order to formulate feasible set-up plans A few researchers have considered machine resources during set-up planning Zhang et al (1999) proposed object-oriented manufacturing resources modeling (OOMRM) and agent-based process planning (AAPP) OOMRM describes manufacturing resource capability and capacity in an object-oriented manner, which intends to encapsulate manufacturing system knowledge and the methods of using the knowledge Based on OOMRM, an AAPP prototype is implemented as a manmachine integrated process planning platform It supports an experienced manufacturing engineer in mapping out a more reasonable and flexible machining process Ong et al (2002) presented a hybrid generative algorithm and simulated annealing approach for set-up planning and reset-up planning in a dynamic workshop environment 22 Chapter Literature Review Cai et al (2008) proposed an adaptive set-up planning approach for various multi-axis machine tools, focusing on kinematic analysis of tool accessibility and optimal set-up plan selection Since set-up planning can produce alternate set-up plans due to different considerations between design specification and machine resources, the question of optimization arises Different approaches have applied to deal with this problem Zhang et al (1995) used a numerically exhaustive approach to select the best solution from all the possible alternatives that satisfy the required constraints Zhang et al (1996) and Huang et al (1997) proposed a graph-based theoretical approach to represent the design specifications of a part The problem of identifying the optimal set-up plan is transformed into a graph search problem Zhang et al (1999) applied SA to set-up planning and Zhang (1997) used GA for the optimization Zhang et al (2001) presented seven set-up planning principles to minimize machining error stack-up under a true positioning GD&T scheme assisted with the extended graph approach Ong et al (2002) presented a hybrid generative algorithm and simulated annealing approach for set-up planning and reset-up planning in a dynamic workshop 23 Chapter Literature Review environment Generated precedence relationship matrix acts as the main constraints for the set-up planning optimization Tseng and Huang (2007) presented a mathematical programming model to distribute the components to the suitable plants to achieve the objective of minimizing multiplant manufacturing costs Hebbal and Mehta (2007) focused on the development of a formalized procedure for automatic generation of feasible set-ups and then to select an optimal set-up plan for machining the features of a given prismatic part An optimal tolerance assignment strategy is developed and implemented by Song et al (2007) The optimization criteria are to minimize the manufacturing cost and cycle time while maintaining product quality The cost model considers effective factors at the machine level, part level, and feature level Optimization of tolerance assignment plan with genetic algorithm is formulated The Monte Carlo simulation based tolerance stack up analysis is employed to determine the satisfaction of design tolerance requirements Table 2.2 summaries the published research methodologies on set-up planning considering both tolerances and machine resources 24 Chapter Literature Review Table 2.2 Summary of research on set-up planning Researchers Major Considerations Tolerance Machine Resource Ong et al (1994, 1996, 1997, 1998, 2000) Zhang et al (1995) General Approach Optimization Fuzzy sets theory Heuristic-based and numerical optimization Ong et al (2002) √ Boerma and Kals (1988) Zhang et al (1996) Huang et al (1997) Wu and Chang (1998) Zhang and Lin (1999) Lin et al (1999) Zhang et al (2001) √ Tseng and Huang (2007) Hebbal and Mehta (2007) Zhang et al (1999) Cai et al (2008) Song et al (2007) Zhang (1997) √ Numerically exhaustive approach A hybrid generative algorithm and simulated annealing Tolerance factor √ Graph theory approach √ √ Automatic tolerance analysis Hybrid graph √ Tolerance chart √ √ Seven set-up planning principles; Extended graph Mathematical programming model Formalized procedure Simulated annealing √ Adaptive approach √ Genetic algorithm √ Genetic algorithm √ √ From literature, it is observed that previous research on set-up planning mainly focuses on the analysis of tolerance specifications of a workpiece, and there are few applications considering machine tools capabilities simultaneously with tolerance 25 Chapter Literature Review analysis When dealing with tolerance analysis, operation sequences are generated based on the dimensions and shapes by checking whether the parts produced are within the designed tolerances If the parts produced are out of the specified tolerances, a more accurate machining centre or operation may need to be considered to meet the requirements However, this could also be due to incorrect locating or fixturing methods Nowadays products are fabricated in a distributed manufacturing environment, and the transportation cost should be considered as well In addition, in most reported research, tolerance charts are input manually and there is no clear extraction of machining and tolerance features from the CAD model This research reports on an approach to extract design information automatically from CAD models through geometric reasoning, integrate machine tools selection with tolerance analysis, and to achieve optimal set-up planning by optimizing the real-time integration process with a cost model The real-time integration is to be achieved by performing set-up planning with the machining resources in real time, which takes into account production schedules and some unexpected events, such as machine tool breakdown, an urgent job which needs to be expedited, etc The cost model is to consider fully the manufacturing cost factors, such as machining cost, fixture cost, transport cost, etc In addition, a tolerance cost factor is to be introduced in the cost model, which will be applied when a more accurate machining centre or operations required due to the effect of tolerance stack-up in a set-up 26 Chapter Literature Review 2.4 Fixture Layout Fixture layout is to determine the locating and clamping positions on the locating and clamping faces Different locating and clamping position will result in different localization errors It is import to evaluate the localization errors during fixture layout planning A number of methods for localization error analysis and reduction have been reported A mathematical representation of the localization error was given by Bourdet and Clement (1988) using the concept of a displacement screw vector Optimization techniques were suggested to minimize the magnitude of the localization error vector or the geometric variation of a critical feature (Bourdet and Clement, 1988; Weill and Dar-El, 1991) Weil and Dar-El (1991) considered the influence of fixture positioning errors on the geometric accuracy of mechanical parts using a screw model An analysis was described by Choudhuri and DeMeter (1999) to relate the locator shape errors to the worst case geometric errors in machined features Geometric deviations of the workpiece datum surfaces were also analyzed for positional, profile, and angular manufacturing tolerance cases and the effects on machined features, such as those by drilling and milling, were illustrated 27 Chapter Literature Review A second order analysis of the localization error was presented by Carlson (2001) A theory for comparing the relative gain in precision using the quadratic sensitivity equation was developed The quadratic sensitivity equation deals with locator contact at non-prismatic surfaces, non-small errors, locator error interaction effects and locator errors in arbitrary directions Recently, the computational difficulties of fixture layout design have been studied with an objective to reduce an overall measure of the localization error for general three dimensional (3D) workpieces such as turbine airfoils (Wang, 2000; Wang and Pelinescu, 2001) They gave a method for optimizing locating errors by minimizing the magnitude of perturbation at the workpiece’s mass center caused by the locators In their method, the determinant of the locator matrix was used as the optimization objective Liu (1999) formalized qualitative tests of 3D frictional form-closure grasps of n robotic fingers as a problem of linear programming (LP) It also addresses a problem of minimizing the L1 norm of the grasp forces balancing an external wrench, which can be transformed to a ray-shooting problem Ding et al (2001) presented an algorithm to automate the generation of optimal fixturing points, which totally restrains the workpiece in the fixture, i.e., satisfies the form-closure condition, as well as minimizes the workpiece positional errors First, a heuristics is proposed for searching a set of fixturing surfaces capable of providing the form-closure Then, the problem of determining optimal fixturing points on the eligible set of fixturing surfaces is formulated as a quadratic programming problem 28 Chapter Literature Review with the workpiece positioning accuracy as the performance index and the robust form-closure requirement as the linear constraints Another distinctive fixture design approach was the homogenous transformation technique, as was first used by Asada and By (1985) to study the kinematic problems, including deterministic locating, total restraining, accessibility and detachability Rodrigo and Placid (2003) addressed the problem of characterizing the acceptable level of inaccuracy in the location scheme so that the features machined on the part would comply with the limits associated with its geometric tolerances Qin et al (2006) presented a mathematical approach to the analysis and optimal design of a fixture locating scheme It characterizes the effects of the localization source errors based on the position and orientation of the workpiece A fixture model is formulated by taking into account the overall errors among the system consisting of the workpiece and the fixture in the design of the fixture locating scheme The locating principle and a criterion of the robust optimal design are proposed to improve the localization quality of the fixture While minimizing localization error is an objective of fixture layout, optimizing this alone does not guarantee a fixture layout solution to be the least sensitive to the variation of localization errors Therefore, robust design approach has been conducted in fixture layout to consider both performance and robustness Robust design is an efficient and systematic methodology that applies statistical experimental design to improve product design The main idea of robust design is to reduce the output 29 Chapter Literature Review variation from the target by making the fixture layout performance insensitive to the disturbances of noise factors There are a few research works addressing robust fixture design Li et al (2003) provided a quality design of the fixture configuration for laser welding of sheet metal A generic robust design methodology, labeled the two-stage response surface methodology, was developed in the robust design model Under the assumption of deterministic location, Cai et al (1997) and Wang (2000) formulated fixture modeling and optimized fixture layout design for machining processes Cai et al (1997) developed a simulation software called RFixDesign for robust fixture configuration design In order to minimize the resulting errors (position and orientation errors), they have only considered surface errors and fixture set-up errors (source errors) A non-linear programming technique was employed in this work However, non-linear programming is sensitive to initial values for reaching the optimal solution Wang (2000) developed a sequential optimization approach for the fixture layout problem with a point set on the workpiece surface This approach focuses on increasing the locating accuracy by maximizing the determinant of the critical configuration matrix which defines the relation between the directional point-wise localization errors with the source errors However, this approach is computationally expensive and impractical with a large number of candidate points Moreover, the 30 Chapter Literature Review measurement of the product quality is the positional error of the workpiece rather than the features to be machined on the workpiece GA and ACO have been proven to be useful techniques in solving optimization problems in engineering They have also been used for fixture layout and configuration design by searching in a large solution space Besides the mentioned research by Li (2003), the following research has also used GA and ACO to solve the fixture design problems Wu and Chan (1996) first applied genetic algorithms to fixture configuration optimization Based on the information provided by the verification system, a genetic algorithm approach was carried to evaluate and determine the most statically stable fixture configuration among a large number of candidate points Kumar et al (1999) combined complementary strengths of genetic algorithms and neural networks for the development of a fixture design system Results obtained using this combined multi-agent approach for the design of fixtures show that the approach is promising Krishnakumar and Melkote (2000) presented the use of genetic algorithms in arriving at optimal 2D fixture layouts A finite element approach was used to evaluate the generated fixture layouts Vallapuzha et al (2002) used spatial coordinates to encode in the GA-based optimization of fixture layout They also presented the methodology and results of an 31 Chapter Literature Review extensive investigation into the relative effectiveness of the main competing fixture optimization methods, which shows the continuous GA, i.e., with optimization functions depending on continuous variables, yields the best solutions Kaya (2006) used the GA and FEM to find the optimal locators and clamping positions of 2D workpiece and took chip removal effects into account Fathianathan et al (2005) developed an automatic fixture design system for modular fixture layout and configuration design using evolutionary search algorithms The algorithm can explore the large solution space using a flexible and generic representation and it considers fixture layout and fixture configuration constraints concurrently in arriving at satisfactory solutions Choubey et al (2005) proposed genetic algorithm with learning automata (GALA) algorithm to solve a fixture configuration problem by minimizing the norm of all the passive contact forces during the entire cutting operation However, these research works mentioned above did not consider robustness with respect to workpiece surface error, fixture element error and fixture set-up error Padmanaban and Prabhaharan (2008) applied ACO to optimize 2D fixture layout for minimizing the dynamic response of the workpiece (elastic deformation of the workpiece under dynamic conditions) They compared ACO results with GA results and demonstrated that the performance of ACO is better than GA 32 Chapter Literature Review Table 2.3 summaries published literature on optimal fixture layout, the objectives considered and the optimization method used Table 2.3 Summary of research on optimal fixture layout Researchers Wang (2000); Wang and Pelinescu (2001) Liu (1999) Ding et al (2001) Qin et al (2006) Li et al (2003) Cai et al (1997) Wu and Chan (1996) Kumar et al (1999) Krishnakumar and Melkote (2000) Vallapuzha et al (2002) Kaya (2006) Fathianathan et al (2005) Choubey et al (2005) Padmanaban and Prabhaharan (2008) Optimization Objectives To minimize the magnitude of perturbation at the workpiece’s mass center caused by the locators To minimize the norm of the grasp forces To minimize the workpiece positional errors To minimize the fluctuation of the position error of the workpiece To perform the fixture configuration for laser welding of sheet metal To minimize the localization error To determine the most statically stable fixture configuration To design fixture layout Method Maximizing the determinant of the critical configuration matrix Linear programming; ray-shooting Quadratic programming Mathematical approach Two-stage response surface methodology Non-linear programming technique GA GA and neural networks To achieve the optimal 2D fixture layout To achieve optimal fixture layout GA, FEM To find the optimal locators and clamping positions of 2D workpiece To develop an automatic fixture design system for modular fixture layout and configuration design To minimize the norm of all the passive contact forces To minimize the elastic deformation of the workpiece under dynamic conditions GA and FEM GA Evolutionary search algorithms GA with learning automata algorithm GA and ACO 33 Chapter Literature Review The previous review accounts only for the geometrical errors and their impact on part localization There are many papers on contact mechanics-based fixture layout and optimization Shawki and Abdel-Aal (1996) investigated the deformations taking place at contact surfaces between workpiece and fixture locating/clamping elements under the action of steady loads Shawki (1997) presented an analytical study of the behaviour of the fixture/workpiece system under the action of an eccentrically applied clamping load Anand et al (2004) modeled the effect of clamping sequence on workpiece location error analytically for a fixture–workpiece system where all major compliance sources and fixture geometrical error were considered Wu et al (1998) presented an analysis on fixturing accuracy, clamp planning, fixturing accessibility, and clamping stability When a fixture planning determined, the analysis results can be applied to verify the performance of the fixture design Ravi et al (1991) presented a methodology for dynamic modeling and simulation of a fixture-workpiece system Pham et al (1990) presented a finite element (FE) study to determine the elastic behavior of a workpiece in a fixture and to locate the points of maximum deflection Menassa and DeVries (1990) presented a design synthesis and optimization method for fixtures with compliant elements De Meter (1993) presented an algorithm for selection of locating/clamping positions providing maximum mechanical leverage Amaral (2001) developed a finite-element analysis tool for fixture design and optimization De Meter (1998) presented a finite-element-based support layout optimization, using a nonlinear optimization algorithm King and Hutter (1993) developed a rigid-body model of the workpiece but accounting for the contact stiffness, and optimized the fixture layout using a nonlinear optimization algorithm 34 Chapter Literature Review In this research, fixture layout optimization is used to minimize the mean and variation of the localization errors for a set of machining features in a set-up The feasibility of the location layout is also considered by maximizing the distance between the locating points on the locating faces The non-dominated multi-objective optimization method and ACO and GA are applied to achieve the above goals Their performances are compared and reported in the subsequent chapters 2.5 Summary In this chapter, the previous research on fixturing-feature-based approach, set-up planning and fixture layout are reviewed in detail Based on the review, the approaches addressed in this research are discussed 35 ... integration of design and manufacturing, which uses the above-mentioned three approaches 2. 3 Set-up Planning Set-up planning is a function of both process planning and fixture design (Ong and Nee,... Heuristic-based and numerical optimization Ong et al (20 02) √ Boerma and Kals (1988) Zhang et al (1996) Huang et al (1997) Wu and Chang (1998) Zhang and Lin (1999) Lin et al (1999) Zhang et al (20 01) √... Kumar et al (1999) Krishnakumar and Melkote (20 00) Vallapuzha et al (20 02) Kaya (20 06) Fathianathan et al (20 05) Choubey et al (20 05) Padmanaban and Prabhaharan (20 08) Optimization Objectives To