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A review and analysis of current computer-aided fixture design approachesIain Boyle, Yiming Rong, David C. BrownKeywords:Computer-aided fixture designFixture designFixture planningFixture verificationSetup planningUnit designABSTRACTA key characteristic of the modern market place is the consumer demand for variety. To respond effectively to this demand, manufacturers need to ensure that their manufacturing practices are sufficiently flexible to allow them to achieve rapid product development. Fixturing, which involves using fixtures to secure work pieces during machining so that they can be transformed into parts that meet required design specifications, is a significant contributing factor towards achieving manufacturing flexibility. To enable flexible fixturing, considerable levels of research effort have been devoted to supporting the process of fixture design through the development of computer-aided fixture design (CAFD) tools and approaches. This paper contains a review of these research efforts. Over seventy-five CAFD tools and approaches are reviewed in terms of the fixture design phases they support and the underlying technology upon which they are based. The primary conclusion of the review is that while significant advances have been made in supporting fixture design, there are primarily two research issues that require further effort. The first of these is that current CAFD research is segmented in nature and there remains a need to provide more cohesive fixture design support. Secondly, a greater focus is required on supporting the detailed design of a fixture’s physical structure.2010 Elsevier Ltd. All rights reserved. Contents1. Introduction (2)2. Fixture design (2)3. Current CAFD approaches (4)3.1 Setup planning (4)3.1.1 Approaches to setup planning (4)3.2 Fixture planning (4)3.2.1 Approaches to defining the fixturing requirement (6)3.2.2 Approaches to non-optimized layout planning (6)3.2.3 Approaches to layout planning optimization (6)3.3 Unit design (7)3.3.1 Approaches to conceptual unit design (7)3.3.2 Approaches to detailed unit design (7)3.4 Verification (8)3.4.1 Approaches to constraining requirements verification (8)3.4.2 Approaches to tolerance requirements verification (8)3.4.3 Approaches to collision detection requirements verification (8)3.4.4 Approaches to usability and affordability requirements verification (9)3.5 Representation of fixturing information (9)4. An analysis of CAFD research (9)4.1 The segmented nature of CAFD research (9)4.2 Effectively supporting unit design (10)4.3 Comprehensively formulating the fixturing requirement (10)4.4 Validating CAFD research outputs (10)5. Conclusion (10)References (10)1. IntroductionA key concern for manufacturing companies is developing the ability to design and produce a variety of high quality products within short timeframes. Quick release of a new product into the market place, ahead of any competitors, is a crucial factor in being able to secure a higher percentage of the market place and increased profit margin. As a result of the consumer desire for variety, batch production of products is now more the norm than mass production, which has resulted in the need for manufacturers to develop flexible manufacturing practices to achieve a rapid turnaround in product development.A number of factors contribute to an organization’s ability to achieve flexible manufacturing, one of which is the use of fixtures during production in which work pieces go through a number of machining operations to produce individual parts which are subsequently assembled into products. Fixtures are used to rapidly, accurately, and securely position work pieces during machining such that all machined parts fall within the design specifications for that part. This accuracy facilitates the interchangeability of parts that is prevalent in much of modern manufacturing where many different products feature common parts.The costs associated with fixturing can account for 10–20% of the total cost of a manufacturing system [1]. These costs relate not only to fixture manufacture, assembly, and operation, but also to their design. Hence there are significant benefits to be reaped by reducing the design costs associated with fixturing and two approaches have been adopted in pursuit of this aim. One has concentrated on developing flexible fixturing systems, such as the use of phase-changing materials to hold work pieces in place [2] and the development of commercial modular fixture systems. However, the significant limitation of the flexible fixturing mantra is that it does not address the difficulty of designing fixtures. To combat this problem, a second research approach has been to develop computer-aided fixture design (CAFD) systems that support and simplify the fixture design process and it is this research that is reviewed within this paper.Section 2 describes the principal phases of and the wide variety of requirements driving the fixture design process. Subsequently in Section 3 an overview of research efforts that havefocused upon the development of techniques and tools for supporting these individual phases of the design process is provided. Section 4 critiques these efforts to identify current gaps in CAFD research, and finally the paper concludes by offering some potential directions for future CAFD research. Before proceeding, it is worth noting that there have been previous reviews of fixturing research, most recently Bi and Zhang [1] and Pehlivan and Summers [3]. Bi and Zhang, while providing some details on CAFD research, tend to focus upon the development of flexible fixturing systems, and Pehlivan and Summers focus upon information integration within fixture design. The value of this paper is that it provides an in-depth review and critique of current CAFD techniques and tools and how they provide support across the entire fixture design process.2. Fixture designThis section outlines the main features of fixtures and more pertinently of the fixture design process against which research efforts will be reviewed and critiqued in Sections 3 and 4, respectively. Physically a fixture consists of devices that support and clamp a work piece [4,5]. Fig.1 represents a typical example of a fixture in which the work piece rests on locators that accurately locate it. Clamps hold the work piece against the locators during machining thus securing the work piece’s location. The locating units themselves consist of the locator supporting unit and the locator that contacts the work piece. The clamping units consist of a clamp supporting unit and a clamp that contacts the work piece and exerts a clamping force to restrain it.Typically the design process by which such fixtures are created has four phases: setup planning, fixture planning, unit design, and verification, as illustrated in Fig. 2 , which is adapted from Kang et al. [6]. During setup planning work piece and machining information is analyzed to determine the number of setups required to perform all necessary machining operations and the appropriate locating datums for each setup. A setup represents the combination of processes that can be performed on a work piece without having to alter the position or orientation of the work piece manually. To generate a fixture for each setup the fixture planning, unit design, and verification phases are executed.During fixture planning, the fixturing requirements for a setup are generated and the layout plan, which represents the first step towards a solution to these requirements is generated. This layout plan details the work piece surfaces with which the fixture’s locating and clamping units will establish contact, together with the surface positions of the locating and clamping points. The number and position of locating points must be such that a work piece’s six degrees of freedom (Fig. 3 ) are adequately constrained during machining [7] and there are a variety of conceptual locating point layouts that can facilitate this, such as the 3-2-1 locating principle [4]. In the third phase, suitable unit designs (i.e., the locating and clamping units) are generated and the fixture is subsequently tested during the verification phase to ensure that it satisfies the fixturing requirements driving the design process. It is worth noting that verification of setups and fixture plans can take place as they are generated and prior to unit design.Fixturing requirements, which although not shown in Kang et al.[6] are typically generated during the fixture planning phase, can be grouped into six class es ( Table 1 ). The ‘‘physical’’requirements class is the most basic and relates to ensuring the fixture can physically support the work piece. The ‘‘tolerance’’requirements relate to ensuring that the locating tolerances aresufficient to locate the work piece accurately and similarly the‘‘constraining’’ requirements focus on maintaining this accuracy as the work piece and fixture are subjected to machining forces. The ‘‘affordability’’ requirements relate to ensuring the fixture represents value, for example in terms of material, operating, and assembly/disassembly costs.The ‘‘collision detection’’ requirements focus upon ensuring that the fixture does not collide with the machining path, the work piece, or indeed itself. The ‘‘usability’’ requirements relate to fixture ergonomics and include for example needs related to ensuring that a fixture features error-proofing to prevent incorrect insertion of a work piece, and chip shedding, where the fixture assists in the removal of machined chips from the work piece.As with many design situations, the conflicting nature of these requirements is problematic. For example a heavy fixture can be advantageous in terms of stability but can adversely affect cost (due to increased material costs) and usability (because the increased weight may hinder manual handling). Such conflicts add to the complexity of fixture design and contribute to the need for the CAFD research reviewed in Section 3.Table 1Fixturing requirements.Generic requirement Abstract sub-requirement examplesPhysical ●The fixture must be physically capable of accommodatingthe work piece geometry and weight.●The fixture must allow access to the work piece features tobe machined.Toleranc e ●The fixture locating tolerances should be sufficient to satisfypart design tolerances.Constraining●The fixture shall ensure work piece stability (i.e., ensure thatwork piece force and moment equilibrium are maintained).●The fixture shall ensure that the fixture/work piece stiffness issufficient to prevent deformation from occurring that could resultin design tolerances not being achieved.Affordabilit y ●The fixture cost shall not exceed desired levels.●The fixture assembly/disassembly times shall not exceeddesired levels.●The fixture operation time shall not exceed desired levels. CollisionPrevention●The fixture shall not cause tool path–fixture collisions to occur.●The fixture shall cause work piece–fixture collisions to occur(other than at the designated locating and clamping positions).●The fixture shall not cause fixture–fixture collisions to occur(other than at the designated fixture component connectionpoints).Usabilit y ●The fixture weight shall not exceed desired levels.●The fixture shall not cause surface damage at the workpiece/fixture interface.●The fixture shall provide tool guidance to designated workpiece features.●The fixture shall ensure error-proofing (i.e., the fixture shouldprevent incorrect insertion of the work piece into the fixture).●The fixture shall facilitate chip shedding (i.e., the fixture shouldprovide a means for allowing machined chips to flow awayfrom the work piece and fixture).3. Current CAFD approachesThis section describes current CAFD research efforts, focusing on the manner in which they support the four phases of fixture design. Table 2 provides a summary of research efforts based upon the design phases they support, the fixture requirements they seek to address (boldtext highlights that the requirement is addressed to a significant degree of depth, whilst normal text that the degree of depth is lesser in nature), and the underlying technology upon which they are primarily based. Sections 3.1–3.4 describes different approaches for supporting setup planning, fixture planning, unit design, and verification, respectively. In addition, Section 3.5 discusses CAFD research efforts with regard to representing fixturing information.3.1. Setup planningSetup planning involves the identification of machining setups, where an individual setup defines the features that can be machined on a work piece without having to alter the position or orientation of the work piece manually. Thereafter, the remaining phases of the design process focus on developing individual fixtures for each setup that secure the work piece. From a fixturing viewpoint, the key outputs from the setup planning stage are the identification of each required setup and the locating datums (i.e., the primary surfaces that will be used to locate the work piece in the fixture).The key task within setup planning is the grouping or clustering of features that can be machined within a single setup. Machining features can be defined as the volume swept by a cutting tool, and typical examples include holes, slots, surfaces, and pockets [8]. Clustering of these features into individual setups is dependent upon a number of factors (including the tolerance dependencies between features, the capability of the machine tools that will be used to create the features, the direction of the cutting tool approach, and the feature machining precedence order), and a number of techniques have been developed to support setup planning. Graph theory and heuristic reasoning are the most common techniques used to support setup planning, although matrix based techniques and neural networks have also been employed.3.1.1. Approaches to setup planningThe use of graph theory to determine and represent setups has been a particularly popular approach [9–11]. Graphs consist of two sets of elements: vertices, which represent work piece features, and edges, which represent the relationships that exist between features and drive setup identification. Their nature can vary, for example in Sarma and Wright [9] consideration of feature machining precedence relationships is prominent, whereas Huang and Zhang [10] focus upon thetolerance relationships that exist between features. Given that these edges can be weighted in accordance with the tolerance magnitudes, this graph approach can also facilitate the identification of setups that can minimize tolerance stack up errors between setups through the grouping of tight tolerances. However, this can prove problematic given the difficulty of comparing the magnitude of different tolerance types to each other thus Huang [12] includes the use of tolerance factors [13] as a means of facilitating such comparisons, which are refined and extended by Huang and Liu [14] to cater for a greater variety of tolerance types and the case of multiple tolerance requirements being associated with the same set of features.While some methods use undirected graphs to assist setup identification [11] , Yao et al. [15] , Zhang and Lin [16] , and Zhang et al. [17] use directed graphs that facilitate the determination and explicit representation of which features should be used as locating datums ( Fig. 4 ) in addition to setup identification and sequencing. Also, Yao et al. refine the identified setups through consideration of available machine tool capability in a two stage setup planning process.Experiential knowledge, in the form of heuristic reasoning, has also been used to assist setup planning. Its popularity stems from the fact that fixture design effectiveness has been considered to be dependent upon the experience of the fixture designer [18] .To support setup planning, such knowledge has typically been held in the form of empirically derived heuristic rules, although object oriented approaches have on occasion been adopted [19] . For example Gologlu [20] uses heuristic rules together with geometric reasoning to support feature clustering, feature machining precedence, and locating datum selection. Within such heuristic approaches, the focus tends to fall upon rules concerning the physical nature of features and machining processes used to create them [21, 22]. Although some techniques do include feature tolerance considerations [23], their depth of analysis can be less than that found within the graph based techniques [24]. Similarly, kinematic approaches [25] have been used to facilitate a deeper analysis of the impact of tool approach directions upon feature clustering than is typically achieved using rule-based approaches. However, it is worth noting that graph based approaches are often augmented with experiential rule-bases to increase their overall effectiveness [16] .Matrix based approaches have also been used to support setup planning, in which a matrix defining feature clusters is generated and subsequently refined. Ong et al. [26] determine a feature precedence matrix outlining the order in which features can be machined, which is then optimized against a number of cost indicators (such as machine tool cost, change over time, etc.) in a hybrid genetic algorithm-simulated annealing approach through consideration of dynamically changing machine tool capabilities. Hebbal and Mehta [27] generate an initial feature grouping matrix based upon the machine tool approach direction for each feature which is subsequently refined through the application of algorithms that consider locating faces and feature tolerances.Alternatively, the use of neural networks to support setup planning has also been investigated. Neural networks are interconnected networks of simple elements, where the interconnections are ‘‘learned’’ from a set of example data. Once educated, these networks can generate solutions for new problems fed into the network. Ming and Mak [28] use a neural network approach in which feature precedence, tool approach direction, and tolerance relationships are fed into a Kohonen self-organizing neural network to group operations for individual features into setups.3.2. Fixture planningFixture planning involves the comprehensive definition of a fixturing requirement in terms ofthe physical, tolerance, constraining, affordability, collision prevention, and usability requirements listed in Table 1 , and the creation of a fixture layout plan. The layout plan represents the first part of the fixture solution to these requirements, and specifies the position of the locating and clamping points on the work piece. Many layout planning approaches feature verification, particularly with regard to the constraining requirements. Typically this verification forms part of a feedback loop that seeks to optimize the layout plan with respect to these requirements. Techniques used to support fixture planning are now discussed with respect to fixture requirement definition, layout planning, and layout optimization.Fig. 4. A work piece (a) and its directed graphs showing the locating datums (b) (adapted from Zhang et al. [17] ).3.2.1. Approaches to defining the fixturing requirementComprehensive fixture requirement definition has received limited attention, primarily focusing upon the definition of individual requirements within the physical, tolerance, and constraining requirements. For example, Zhang et al. [17] under-take tolerance requirement definition through an analysis of work piece feature tolerances to determine the allowed tolerance at each locating point and the decomposition of that tolerance into its sources. The allowed locating point accuracy is composed of a number of factors, such as the locating unit tolerance, the machine tool tolerance, the work piece deformation at the locating point, and so on. These decomposed tolerance requirements can subsequently drive fixture design: e.g., the tolerance of the locating unit developed in the unit design phase cannot exceed the specified locating unit tolerance. In a similar individualistic vein, definition of the clamping force requirements that clamping units must achieve has also received attention [29,30].In a more holistic approach, Boyle et al. [31] facilitate a comprehensive requirement specification through the use of skeleton requirement sets that provide an initial decomposition of the requirements listed in Table 1, and which are subsequently refined through a series of analyses and interaction with the fixture designer. Hunter et al. [32,33] also focus on functional requirement driven fixture design, but restrict their focus primarily to the physical and constraining requirements.3.2.2. Approaches to non-optimized layout planningLayout planning is concerned with the identification of the locating principle, which defines the number and general arrangement of locating and clamping points, the work piece surfaces they contact, and the surface coordinate positions where contact occurs. For non-optimized layoutplanning, approaches based upon the re-use of experiential knowledge have been used. In addition to rule-based approaches [20,34,35] that are similar in nature to those discussed in Section 3.1, case-based reasoning has also been used. CBR is a general problem solving technique that uses specific knowledge of previous problems to solve new ones. In applying this approach to layout planning, a layout plan for a work piece is obtained by retrieving the plan used for a similar work piece from a case library containing knowledge of previous work pieces and their layout plans [18,36,37]. Work piece similarity is typically characterized through indexing work pieces according to their part family classification, tolerances, features, and so on. Lin and Huang [38] adopt a similar work piece classification approach, but retrieve layout plans using a neural network. Further work has sought to verify layout plans and repair them if necessary. For example Roy and Liao [39] perform a work piece deformation analysis and if deformation is too great employ heuristic rules to relocate and retest locating and clamping positions.3.2.3. Approaches to layout planning optimizationLayout plan optimization is common within CAFD and occurs with respect to work piece stability and deformation, which are both constraining requirements. Stability based optimization typically focuses upon ensuring a layout plan satisfies the kinematic form closure constraint (in which a set of contacts completely constrain infinitesimal part motion) and augmenting this with optimization against some form of stability based requirement, such as minimizing forces at the locating and/or clamping points [40–42] . Wu and Chan [43] focused on optimizing stability (measuring stability is discussed in Section 3.4) using a Genetic Algorithm (GA), which is a technique frequently employed in deformation based optimization.GAs, which are an example of evolutionary algorithms, are often used to solve optimization problems and draw their inspiration from biological evolution. Applying GAs in support of fixture planning, potential layout plan solutions are encoded as binary strings, tested, evaluated, and subjected to ‘‘biological’’ modification through reproduction, mutation, and crossover to generate improved solutions until an optimal state is reached. Typically deformation testing is employed using a finite element analysis in which a work piece is discretized to create a series of nodes that represent potential locating and clamping contact points, as performed for example by Kashyap and DeVries [44] . Sets of contact points are encoded and tested, and the GA used to develop new contact point sets until an optimum is reached that minimizes work piece deformation caused by machining and clamping forces [45,46]. Rather than use nodes, some CAFD approaches use geometric data (such as spatial coordinates) in the GA, which can offer improved accuracy as they account for the physical distance that exists between nodes [47,48].Pseudo gradient techniques [49] have also been employed to achieve optimization [50,51]. Vallapuzha et al. [52] compared the effectiveness of GA and pseudo gradient optimization, concluding that GAs provided higher quality optimizations given their ability to search for global solutions, whereas pseudo gradient techniques tended to converge on local optimums.Rather than concentrating on fixture designs for individual parts, Kong and Ceglarek [53] define a method that identifies the fixture workspace for a family of parts based on the individual configuration of the fixture locating layout for each part. The method uses Procrustes analysis to identify a preliminary workspace layout that is subjected to pairwise optimization of fixture configurations for a given part family to determine the best superposition of locating points for a family of parts that can be assembled on a single reconfigurable assembly fixture. This buildsupon earlier work by Lee et al. [54] through attempting to simplify the computational demands of the optimization algorithm.3.3. Unit designUnit design involves both the conceptual and detailed definition of the locating and clamping units of a fixture, together with the base plate to which they are attached (Fig. 5). These units consist of a locator or clamp that contacts the work piece and is itself attached to a structural support, which in turn connects with the base plate. These structural supports serve multiple functions, for example providing the locating and clamping units with sufficient rigidity such that the fixture can withstand applied machining and clamping forces and thus result in the part feature design tolerances being obtained, and allowing the clamp or locator to contact the work piece at the appropriate position. Unit design has in general received less attention than both fixture planning and verification, but a number of techniques have been applied to support both conceptual and detailed unit design.3.3.1. Approaches to conceptual unit designConceptual unit design has focused upon the definition of the types and numbers of elements that an individual unit should comprise, as well as their general layout. There are a wide variety of locators, clamps, and structural support elements, each of which can be more suited to some fixturing problems than others. As with both setup planning and fixture layout planning, rule-based approaches have been adopted to support conceptual unit design, in which heuristic rules are used to select preferred elements from which the units should be constructed in response to considerations such as work piece contact features (surface type, surface texture, etc.) and machining operations within the setup [35,55–58]. In addition to using heuristic rules as a means of generating conceptual designs, Kumar et al.[59] use an inductive reasoning technique to create decision trees from which such fixturing rules can be obtained through examination of each decision tree path.Neural network approaches have also been used to support conceptual unit design. Kumar et al. [60] use a combined GA/neural network approach in which a neural network is trained with a selection of previous design problems and their solutions. A GA generates possible solutionswhich are evaluated using the neural network, which subsequently guides the GA. Lin and Huang[38] also use a neural network in a simplified case-based reasoning (CBR) approach in which fixturing problems are coded in terms of their geometrical structure and a neural network used to find similar work pieces and their unit designs. In contrast, Wang and Rong[37] and Boyle et al.[31] use a conventional CBR approach to retrieve units in which the fixturing functional requirements form the basis of retrieval, which are then subject to refinement and/or modification during detailed unit design.3.3.2. Approaches to detailed unit designMany, but not all systems that perform conceptual design also perform detailed design, where the dominant techniques are rule, geometry, and behavior based. Detailed design involves the definition of the units in terms of their dimensions, material types, and so on. Geometry, in particular the acting height of locating and clamping units, plays a key role in the design of individual units in which the objective is to select and assemble defined unit elements to provide a unit of suitable acting height [61,62]. An et al. [63] developed a geometry based system in which the dimensions of individual elements were generated in relation to the primary dimension of that element (typically its required height) through parametric dimension relationships. This was augmented with a relationship knowledge base of how different elements could be configured to form a single unit. Similarly, Peng et al. [64] use geometric constraint reasoning to assist in the assembly of user selected elements to form individual units in a more interactive approach.Alternatively, rule-based approaches have also been used to define detailed units, in which work piece and fixture layout information (i.e., the locating and clamping positions) is reasoned over using design rules to select and assemble appropriately sized elements [32,55,56] . In contrast, Mervyn et al. [65] adopt an evolutionary algorithm approach to the development of units, in which layout planning and unit design take place concurrently until a satisfactory solution is reached.Typically, rule and geometry based approaches do not explicitly consider the required strength of units during their design. However for a fixture to achieve its function, it must be able to withstand the machining and clamping forces imposed upon it such that part design tolerances can be met. To address this, a number of behaviorally driven approaches to unit design have been developed that focus upon ensuring units have sufficient strength. Cecil [66] performed some preliminary work on dimensioning strap clamps to prevent failure by stress fracture, but does not consider tolerances or the supporting structural unit. Hurtado and Melkote [67] developed a model for the synthesis of fixturing configurations in simple pin-array type flexible machining fixtures, in which the minimum number of pins, their position, and dimensions are determined that can achieve stability and stiffness goals for a work piece through consideration of the fixture/work piece stiffness matrix, and extended this for modular fixtures [68] . Boyle et al. [31] also consider the required stiffness of more complex unit designs within their case-based reasoning method. Having retrieved a conceptual design that offers the correct type of function, this design’s physical structure is then adapted using dynamically selected adaptation strategies until it offers the correct level of stiffness.3.4. VerificationVerification focuses upon ensuring that developed fixture designs (in terms of their setup plans, layout plans, and physical units) satisfy the fixturing requirements. It should be noted from。