ComputerAided_Design_Engineering_amp_Manufactur.pdf

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has been developed as a neutral interface for the bi-directional communication of data between CAD systems and computer-controlled CMMs in a multi-vendor, computer-integrated environment. DMIS was developed by Computer-Aided Manufacturing Incorporated (CAM-I) and it was approved in 1990 by the American National Standards Institute (ANSI) as an American National Standard. The main advantages of DMIS is that it is APT-like and it allows measurement results to be carried back in the same neutral format. This is particularly important in computer integration to both quality and management information systems in a CIM architecture and is fast evolving as the main industry standard. DMIS carries two main types of statements: process-oriented commands and geometry-oriented statements. Process-oriented commands consist of motion statements, machine parameters statements, part alignment statements, probe calibration statements, and other statements related to the inspection process. Geometry-oriented statements are used to describe the geometrical elements, geometric dimension and tolerances, coordinate systems, and other types of data which may be included in a CAD database. DMIS has been designed to be compatible with complete part model definitions supporting simple geometrical features like point, circle, ellipse, arc, sphere, cylinder, cone, and plane—as well as more complicated ones like the general curve element, general surface element, pattern elements, rectangle element, parallel plane element, and general raw data element. It also supports GD&T specifications according to ANSI Y14.5M which is widely adopted in the industry. Another powerful feature of DMIS is its high level language capability. This allows for variable definitions as well as program control, providing for high flexibility in automated inspection planning. Although it has been reported that there are interfacing problems between the CAD system and the CMM, 44 DMIS will evolve as the main interface standard as further refinements are made. Computer-Aided Inspection Planning System for the Coordinate Measuring Machine (CAIPS-CMM) The development of a computer-aided inspection planning system for the coordinate measuring machine (CAIPS-CMM) is characterized by the generative approach with few reported instances of the variant approach. 45 In the generative approach, applications are broadly divided into: • Inspection of turned parts46 • Inspection of prismatic parts47–49 • Inspection of complex surfaces for molds. 50–52 For turned and prismatic parts, the application of a feature-based technique is very apparent. Expert systems are also mainly used for the reasoning process in planning, coupled with techniques like objectoriented programming. For the inspection of complex surfaces, the focus of researchers is on minimization of inspection points and comparative analysis. Regardless of the applications, a CAIPS-CMM should generally cover the following areas in order to automate the task of CMM part programming in a computer-integrated environment: • An interface to a CAD product model for interpretation of measurement requirements. Inspection requirements for the CMM are driven by the GD&T specifications which usually refer to geometrical surfaces or datum on the product model. Hence a product model should ideally encompass complete geometric information as well as GD&T information. Higher level information like features can be incorporated which could help in the planning process. The structure of the product model representation is outlined in the next section. • A framework for inspection planning based on the knowledge of the operational procedures of the CMM is required. • Knowledge of the machine capabilities in terms of the machine configuration, the available sensor configurations, the accuracy, the operating speed, the supporting evaluation functions, etc., is essential for decisions such as selection of probes for the CMM. © 2001 by CRC Press LLC
FIGURE 2.21 CMM planning requirements and tasks. • A methodology for the assignment of set-ups, part coordinate systems, probe configurations, sampling size, and minimization of probe travel is important for the optimization of CMM inspection in terms of cycle time. • A methodology for collision-free path planning in order to realize feasible inspection programs • An interface to the CMM in the form of a neutral programming language such as DMIS which will carry the final inspection commands for any CMM. To realize the final objectives of a computer-aided inspection planning system for the CMM, there are generally five inspection tasks to be carried out, as outlined in Figure 2.21. 1. The first inspection task is to interpret the measurements required for the part based on the product model representation. Interpretation would include recognizing geometric elements on the part to be measured for the evaluation based on their associated GD&T specifications, and determining the evaluation functions to be used and the output to be produced by the CMM. 2. The second inspection task is to determine the setup of the part on the CMM for inspection. The major consideration is the minimum setup orientation for the part. The task plan for the CMM will include the part coordinate systems to be used for the orientation, the elements to be measured or constructed to create the part reference, and the elements that are to be measured in the setup. 3. The third inspection task is to assign the probe configurations and operation parameters for the measurement elements. Major considerations would include assignment of suitable probes, minimization of the probes assigned, and the sampling points required for the measurement. © 2001 by CRC Press LLC
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COMPUTER-AIDED DESIGN, ENGINEERING,
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Library of Congress Cataloging-in-P
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Editor Cornelius T. Leondes, B.S.,
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Chapter 1 Chapter 2 Chapter 3 Chapt
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the implementation of the IPD syste
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In our IPD system implementations,
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2. Specification of analysis-specif
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FIGURE 1.2 base or the design insta
Page 17 and 18: FIGURE 1.4 System architecture of t
Page 19 and 20: of a beam is considered for FEA. Th
Page 21 and 22: FIGURE 1.7 through the control expe
Page 23 and 24: 2. machining facility (e.g., gang m
Page 25 and 26: from the FBDS. The user also specif
Page 27 and 28: Depending on the type of feature to
Page 29 and 30: TABLE 1.6 Tolerance synthesis is de
Page 31 and 32: FIGURE 1.12 Display of the NC tool
Page 33 and 34: component geometric entities and va
Page 35 and 36: FIGURE 1.14 The system architecture
Page 37 and 38: TABLE 1.10 User Input for the Toler
Page 39 and 40: TABLE 1.11 Tolerance Specifications
Page 41 and 42: 7. Z. Young and I. R. Groose. A rul
Page 43 and 44: FIGURE 2.1 Tool paths generated for
Page 45 and 46: FIGURE 2.2 sented by instances of f
Page 47 and 48: TABLE 2.1 A CAD-Generated Hole Conf
Page 49 and 50: FIGURE 2.6 mounted on the rotary ta
Page 51 and 52: FIGURE 2.8 fixture for the machinin
Page 53 and 54: FIGURE 2.10 or best-fit, or the cur
Page 55 and 56: FIGURE 2.11 Linear approximation of
Page 57 and 58: FIGURE 2.13 Circular approximation
Page 59 and 60: FIGURE 2.14(b) Circular approximati
Page 61 and 62: FIGURE 2.16 Types of biarcs. FIGURE
Page 63 and 64: FIGURE 2.18 Approximation of scanne
Page 65 and 66: FIGURE 2.20 A touch trigger probe c
Page 67: TABLE 2.4 Substitute Elements in CM
Page 71 and 72: Product Model Representation for Co
Page 73 and 74: © 2001 by CRC Press LLC TABLE 2.5
Page 75 and 76: TABLE 2.7 Partial Listing of Dimens
Page 77 and 78: 24. Makinouchi, S., Okamoto, M., an
Page 79 and 80: Bijan Shirinzadeh Monash University
Page 81 and 82: FIGURE 3.1 Trends in flexible manuf
Page 83 and 84: FIGURE 3.3 and constrain different
Page 85 and 86: Other Fixturing Techniques There ar
Page 87 and 88: FIGURE 3.6 contact point. The geome
Page 89 and 90: FIGURE 3.8 The vertical support fix
Page 91 and 92: and moments about the contact point
Page 93 and 94: een identified, the normals are use
Page 95 and 96: one of choosing the variables: such
Page 97 and 98: Fixture Module Location An importan
Page 99 and 100: FIGURE 3.15 Illustration of functio
Page 101 and 102: FIGURE 3.18 Minimum separation test
Page 103 and 104: direction) the face, respectively.
Page 105 and 106: FIGURE 3.21 Software structure for
Page 107 and 108: 3.8 Conclusion A reconfigurable fix
Page 109 and 110: Heui Jae Pahk Seoul National Univer
Page 111 and 112: FIGURE 4.1(b) Conceptual framework
Page 113 and 114: 4.3 Measurement Points Sampling and
Page 115 and 116: Surface S2 Measurement Path FIGURE
Page 117 and 118: FIGURE 4.4 Rough phase alignment ba
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deviation between the nominal CAD d
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FIGURE 4.6(a) Sum of squares distan
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FIGURE 4.7(c) Trailing edge. (c) th
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FIGURE 4.8(a) Profile tolerance of
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The calculated minimum form error i
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FIGURE 4.11(a) A typical mold havin
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FIGURE 4.11(d) Inspection results f
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FIGURE 4.12(c) Maximum deviation (p
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5.1 Introduction Developments in th
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process plan for a part (Figure 5.3
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FIGURE 5.5 leading to the developme
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the previously stored process plan
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FIGURE 5.7 Techniques of defining a
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Feature Recognition and Extraction
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in relation to another feature (e.g
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FIGURE 5.9 Framework for building a
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FIGURE 5.10 Process plan content.
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FIGURE 5.12 Schematic sketch of the
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Several techniques of defining a fe
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TABLE 5.1 Data Structure for Repres
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FIGURE 5.16 Classification of the t
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system to ensure that the part bein
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FIGURE 5.19 Graphical model of the
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TABLE 5.4 Data Structure for Repres
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FIGURE 5.20 Mapping between machini
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algorithms. (Some details of the pr
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FIGURE 5.24 An example rotational p
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FIGURE 5.26 Down_face-turn-up_face
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FIGURE 5.27 Procedure for machine t
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FIGURE 5.28 Determining the number
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DL is needed to be set only if the
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FIGURE 5.31 Operation sequencing co
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Cutting Tool Selection Selection of
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1. A tool is searched for in the da
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FIGURE 5.32 Inputs to optimization
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When these values are substituted,
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Usually, maximum and minimum speed
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FIGURE 5.33 Solution methodology.
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TABLE 5.6 Process Plan Internal Rep
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In a strict theoretical perspective
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18. Domazet, D. S. and Lu, S. C. Y,
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63. Prasad, A. V. S. R. K., Rao, P.
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CAD systems. The sample consists of
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learn to master the new system. An
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Although researchers appear to agre
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Link and Zmud28 found that organic
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Informal Training Informal CAD trai
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informal training programs, felt th
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6. C. A. Beatty, Tall Tales and Rea
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A.Y.C. Nee National University of S
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FIGURE 7.1 Planning, design, and ma
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A metal stamping can have the follo
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FIGURE 7.3 Strips used to notch out
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A Skeletal Approach for the Recogni
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These findings can be used to devel
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Semi-Direct Piloting In cases where
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a larger value, the die operations
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FIGURE 7.9 Symbolic relationship be
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FIGURE 7.11 The shape of the envelo
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TABLE 7.1 Schema for the Generation
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strong reasons to support a move to
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FIGURE 7.16 3-D CAD model of a part
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References Cheok, B.T. et al. (1994
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include the once forbidden generati
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A temporal matrix (T-Matrix) is pro
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FIGURE 8.1 A basic process. (From R
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FIGURE 8.4(a) Generate a new IG usi
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Note if the pair of nodes are both
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TABLE 8.1 Summary of the Rules Cond
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The correctness of these rules is e
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needs to show that after each full
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ule is violated, a warning should b
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Π1 Π3 Π4 FIGURE 8.8(a) After add
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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A ik � ‘U’ and is in a cycle
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FIGURE 8.12(a) Add [p2 t7 p7 t8 p4]
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FIGURE 8.12(c) Add a token to p8 vi
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FIGURE 8.12(e) Add [p8 t9 p14 t10 p
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FIGURE 8.13 An example of rule TT.0
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FIGURE 8.14 A GPN model of a machin
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FIGURE 8.15(b) The first exclusive
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FIGURE 8.15(d) The last exclusive T
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FIGURE 8.15(f) After entering the a
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FIGURE 8.15(h) Completion of Compon
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FIGURE 8.15(j) Two PP-generations:
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FIGURE 8.15(l) The last exclusive T
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t1 t5 p1 p5 t6 (a) t2 t3 t4 2 2 p2
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FIGURE 8.17 An example of partial f
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Theorem 10: If pi → pk in a synth
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Note that control transitions are o
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t j than the lower at , it indicate
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• Programming logic and VLSI arra
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7. Chao, D. Y. and D. T. Wang, Appl
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53. Villaroel, J. L., J. Martinez,
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q u : the numerator of the least ra
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geometric modeling, engineering ana
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In dimension driven or parametric d
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FIGURE 9.3 configuration of bodies
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FIGURE 9.6 FIGURE 9.7 Geometric Int
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FIGURE 9.8 Intersection of degrees
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will introduce a way to propagate p
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Completeness of Dimensions Complete
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FIGURE 9.12 3-D constraints. © 200
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The distance constraint from pt �
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therefore: Now merge the lists and
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Quantity analysis methods have the
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y a revolute joint. By selecting al
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