夹具设计英文文献

1、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 dem

2、and 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 tr

3、ansformed 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

4、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 concl

5、usion 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 d

6、esign support. Secondly, a greater focus is required on supporting the detailed design of a fixtures physical structure. 2010 Elsevier Ltd. All rights reserved.Contents1. Introduction22. Fixture design.23. Current CAFD approaches.43.1 Setup planning.4 Approaches to setup planning.43.2 Fixture planni

7、ng.4 Approaches to defining the fixturing requirement6 Approaches to non-optimized layout planning.6 Approaches to layout planning optimization63.3 Unit design.7 Approaches to conceptual unit design.7 Approaches to detailed unit design.73.4 Verification.8 3.4.1 Approaches to constraining requirement

8、s verification.8 Approaches to tolerance requirements verification.8 Approaches to collision detection requirements verification8 Approaches to usability and affordability requirements verification93.5 Representation of fixturing information.94. An analysis of CAFD research.9 4.1 The segmented natur

9、e of CAFD research .9 4.2 Effectively supporting unit design 104.3 Comprehensively formulating the xturing requirement.104.4 Validating CAFD research outputs.105. Conclusion.10References101. IntroductionA key concern for manufacturing companies is developing the ability to design and produce a varie

10、ty 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

11、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 organizations ability to achieve flexible manufactu

12、ring, 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 t

13、hat 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 1020% of the

14、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 t

15、his 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 addre

16、ss 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 princip

17、al phases of and the wide variety of requirements driving the fixture design process. Subsequently in Section 3 an overview of research efforts that have focused upon the development of techniques and tools for supporting these individual phases of the design process is provided. Section 4 critiques

18、 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 S

19、ummers 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 c

20、urrent 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 a

21、nd 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 t

22、he work pieces 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 p

23、rocess 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 require

24、d 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 set

25、up 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

26、 surfaces with which the fixtures 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 pieces six degrees of freedom (Fig. 3 ) are adequately constrained during

27、 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 p

28、hase 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 genera

29、ted during the fixture planning phase, can be grouped into six classes ( 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 are sufficient t

30、o locate the work piece accurately and similarly theconstraining 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, operatin

31、g, 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 fix

32、ture 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

33、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

34、.Table 1Fixturing requirements.Generic requirement Abstract sub-requirement examplesPhysical The fixture must be physically capable of accommodating the work piece geometry and weight. The fixture must allow access to the work piece features tobe machined.Tolerance The fixture locating tolerances sh

35、ould be sufficient to satisfy part 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 is sufficient to prevent deformation from occurrin

36、g that could result in design tolerances not being achieved.Affordability The fixture cost shall not exceed desired levels. The fixture assembly/disassembly times shall not exceed desired levels. The fixture operation time shall not exceed desired levels.CollisionPrevention The fixture shall not cau

37、se tool pathfixture collisions to occur. The fixture shall cause work piecefixture collisions to occur(other than at the designated locating and clamping positions). The fixture shall not cause fixturefixture collisions to occur(other than at the designated fixture component connection points).Usabi

38、lity The fixture weight shall not exceed desired levels.The fixture shall not cause surface damage at the work piece/fixture interface. The fixture shall provide tool guidance to designated work piece features. The fixture shall ensure error-proofing (i.e., the fixture should prevent incorrect inser

39、tion of the work piece into the fixture). The fixture shall facilitate chip shedding (i.e., the fixture should provide a means for allowing machined chips to flow away from the work piece and fixture).3. Current CAFD approachesThis section describes current CAFD research efforts, focusing on the man

40、ner 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

41、text that the degree of depth is lesser in nature), and the underlying technology upon which they are primarily based. Sections 3.13.4 describes different approaches for supporting setup planning, fixture planning, unit design, and verification, respectively. In addition, Section 3.5 discusses CAFD

42、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 pi

43、ece 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

44、., 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 in

45、clude 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 to

46、ol 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

47、employed. Approaches to setup planningThe use of graph theory to determine and represent setups has been a particularly popular approach 911. Graphs consist of two sets of elements: vertices, which represent work piece features, and edges, which represent the relationships that exist between feature

48、s 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 the tolerance relationships that exist between features. Given that these edges can be weighted i

49、n 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

50、 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 asso

51、ciated 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 datu

52、ms ( 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 se

53、tup 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

54、 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 concer

55、ning 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 use

56、d 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