1. egekwu.3311 ISAT 331 Module 3: GROUP TECHNOLOGY AND PROCESS PLANNING

2. egekwu.3312 GROUP TECHNOLOGY – [ Chapter 5 of Bedworth ] l Introduction of GT l Development of Part Families l Coding and Classification-basis for GT »coding schemes »examples of coding systems l Cellular Manufacturing l Economic Considerations - production planning, tool analysis.

3. egekwu.3313 Definition of GT l GT is an engineering and manufacturing philosophy that groups parts together based on their similarities in order to achieve economies of scale in a small- scale environment. »economies of scale is associated with large-scale production »economies of scope is also realized

4. egekwu.3314 Production Quantity Product Variety 10010,0001 M Product Variety vs Production Quantity Har d Sof t Low Hig h Job Shop Mass Production Mid Variety Mid Production (Most Difficult) Changeover (set up)Time MH automated

5. egekwu.3315 Mass Production Production Quantity Product Variety 10010,0001 M Types of Production Plant (facilities) and Layout Har d Sof t Low Hig h Fixed Position (Large) Process Product (Flow line) Process (Quantity) Process (Batch) Cellular (GT families) FMS (GT families- automated MH) Job Shop Mid Variety Mid Production (Apply GT) Efficiency Flexibility

6. egekwu.3316 Process Layout – typical of most job shops

7. egekwu.3317 GT layout

8. egekwu.3318 WIP Distribution – Machined Part Fabrication 5% Moving and Waiting - 95% Cutting < 30% Position, loading, gauging, idle, etc. 70 %

9. egekwu.3319 Characteristics of Job Shops - (operations scheduling) l low-volume production, lot sizes small l machining centers organized by manufacturing function l high labor content in product costs l general-purpose machinery l significant changeover time l little automation of material handling l large variety of products

10. egekwu.33110 Need for GT l need to improve productivity in a job shop or batch production. l approx. 75% of all manufactured parts in the US are made on a small lot basis. l need for design retrieval and cell mfg. l grouping similar parts should improve design and manufacturing efforts - HOW?? - (design, mfg., and tool engineers)

11. egekwu.33111 Importance of GT to CAD/CAM Integration l GT facilitates structuring and archiving of product data e.g. design and manufacturing attributes. l provides common language for users. l facilitates integration of different part- related information. l GT is key to CAPP-computer aided process planning.

12. egekwu.33112 Geometric CharacteristicsProduction Process Characteristics Attributes Grouping into Part Families GT Classification SIZESHAPE NO. of OperationsSequence Type of Operation Process Condition Tooling Type Holding Method Process Method based on Successful grouping is key to GT Implementation. determined by

13. egekwu.33113 Design Attributes Grouping parts into families is based on design and/or manufacturing attributes (features)

14. egekwu.33114 Manufacturing Attributes

15. egekwu.33115 Design and Manufacturing Attributes

16. egekwu.33116 Grouping Methods – [ Bedworth Figs. 5.3 and 5.4]

17. egekwu.33117 Methods for Developing Part Families l Three Basic General Methods l (A) Manual Visual Search –low reliability, not used in formal GT application –different knowledge of processes result in different groupings –differences in identification of important attributes –groupings differ ‘cause different tool/machine combination can be used in fabricating a part.

18. egekwu.33118 Part Families Development contd. l (B) Production Flow Analysis (PFA) – analyzes sequence of operation for part fabrication (Route Sheet- show transparency). –parts that go through similar operations are grouped together –machines used for the operations are also grouped together –mach./component chart is formed and sorted - clustering techniques are often used –depends on accurate routing sheets.

19. egekwu.33119 Part Families Dev’t - Fig 5.5

20. egekwu.33120 Part Families Dev’t contd. l (C) Classification and Coding –coding involves the assignment of representative symbols to a part –symbols relate to different part attributes –coding system is unique to a company –expensive but payback is high because it forms basis for design info. retrieval & cell production –for robustness, design and mfg. attributes are coded - E.G. shape, material, size; and tolerances, processes, tool requirement, etc.

21. egekwu.33121 Classification and Coding contd. l There are many coding systems for GT application - no single system is universally accepted l 3 basic types of systems are: »hierarchical (monocode) »attribute (polycode, a chain code, discrete code or fixed-digit code) »hybrid (mixed)

22. egekwu.33122 Hierarchical Code l characters in a code are dependent on the meaning of previous characters l characters “amplify” the information of the previous character l adequate for capturing design specific information (shape, material, size, etc.) l not robust enough for analyzing process-related information.

23. egekwu.33123 Example-Hierarchical (Bedworth fig. 5.6)

24. egekwu.33124 Attribute Code l Characters are independent of others in the code l each part attribute is assigned a specific position in the code l preferred by manufacturing - easy to identify parts that require similar processes l disadvantage - code could be very long.

25. egekwu.33125 Example - Attribute Code (fig. 5.7) How might one use attribute code for retrieving part families that require identical processing?

26. egekwu.33126 Hybrid Code l combines the benefits of an attribute code (ease of identifying specific part features) and the need for a compact code (data base space and management)

27. egekwu.33127 Selecting a Coding System - Factors to Consider l > 100 coding systems to choose from l A) Objective - user needs (engineering, manufacturing or both) »Engineering Objectives - retrieval, part information, mfg capability and producibility analysis. »Mfg. Objectives - info. for part families, process plan retrieval, machine groupings.

28. egekwu.33128 Selecting Coding System contd. l B) Robustness - able to handle current and future parts. l C) Expandability - ease of expansion. l D) Differentiation - balance both similarities and differences in parts. l E) Automation - ascertain degree of automation of coding, data base retrieval and analysis functions.

29. egekwu.33129 Selecting Coding System contd. l F) Efficiency - number of digits required to code a part. l D) Cost - initial, maintenance and modification costs. l H) Simplicity - ease of use.

30. egekwu.33130 DCLASS Coding System l is an 8-digits system that is partitioned into 5 code segments l based on some basic premises (5 total) »completely characterize parts on the basis of: 1. basic shape 2. features 3. size 4. precision and 5. material type, form, and condition. 124 1 B3A 1 BASIC SHAPE FormSize Precision Materials

31. egekwu.33131 DCLASS Code - fig. 5.8

32. egekwu.33132 DCLASS - fig. 5.14

33. egekwu.33133 DCLASS - fig. 5.15

34. egekwu.33134 DCLASS - fig. 5.16

35. egekwu.33135 DCLASS - Tables 5.3, 5.4, and 5.5

36. egekwu.33136 Coding Systems contd. l MICLASS Coding System: »MICLASS = Metal Institute Classification System »consists of two major sections (segments) »first segment is mandatory-total of 12 digits »first 4 digits describe main shape and their elements »second 4 digits describe dimensions...

37. egekwu.33137 MICLASS contd. - fig. 5.17

38. egekwu.33138 MICLASS contd. l second segment is optional l can contain up to 18 characters; reserved for company specific info. l typical info include: vendors, lot sizes, costs, producibility tips l MICLASS uses an interactive computer program for coding and classifying info. in data base - see fig. 5.19.

39. egekwu.33139 Other Coding Systems ----- figs. 5.10 and 5.12 Examples: 1.CODE eight-digit hybrid code 2.OPITZ nine-digit hybrid code

40. egekwu.33140 Clustering Techniques: Single- Linkage Clustering Algorithm (SLCA) l algorithm utilizes similarity coefficient to group parts requiring similar process l similarity coefficient is calculated for each pair of machines to ascertain: »how alike the 2 machines are based on number of parts that “visit” both machines and »number of parts that “visit” each machine only (and doesn’t visit the second machine).

41. egekwu.33141 SLCA - Similarity Coeff. l S ij = a/(a+b+c) »where, »s = similarity coefficient between mach i and j »a = # of parts common to both machines »b = # of parts that visit only machine i »c = # of parts that visit only machine j l Determine similarity coefficient between machines A and D - fig. 5.21.

42. egekwu.33142 SLCA contd. (fig. 5.21)

43. egekwu.33143 SLCA Steps l calculate pair-wise similarity coefficient for each machine - coefficients will form a symmetric matrix l identify largest coefficient. - the associated machines form initial cluster l identify largest remaining coefficient - associated machines are also grouped

44. egekwu.33144 SLCA contd. l repeat steps 2 and 3 above until all machines are clustered into one group - or until a threshold is reached. »threshold level is used to control number of clusters formed. l see fig. 5.23, Table 5.6 and fig. 5.24

45. egekwu.33145 Fig 5.23 and Table 5.6

46. egekwu.33146 SLCA dendrogram

47. egekwu.33147 Advantages/Disadvantages of SLCA l An Advantage of SLCA is that it provides a powerful systematic way of grouping machines for GT mfg. l Disadvantages: »no clear direction on how to achieve ideal machine-groups. To decide, one need info. on a) no. of inter-group/intra-group movements b) machine utilization c) planning and control and d) bottleneck machines. »Chaining can occur – page 209 of Bedworth.

48. egekwu.33148 Enhancements to SLCA l Anderberg’s Algorithm: »S ij = 2a/(2a + b + c) » this gives more weight to similar machines and thus limits or controls Chaining.

49. egekwu.33149 Average - Linkage Clustering Algorithm (ALCA) l S ij = s ij / (N i x N j ) »where: »s ij = sum of similarity coefficient between all machines of the two groups »N i, N j is no. of machines in group i and j, respectively. l Example: machines A and B belong to group i and machines C, D, and E to group j; Calculate S AB, CDE

50. egekwu.33150 ALCA Steps l calculate pair-wise similarity coefficient for all machines l locate largest coefficient - the 2 machines form initial cluster l calculate average similarity coefficient between new cell and remaining cells - revise similarity matrix l repeat steps 2 and 3 above. Examples..

51. egekwu.33151 ALCA - fig. 5.25

52. egekwu.33152 Facility Design with GT l facility layout is critical to many manufacturing performance measures l 3 major types of machine (process) layout - line, functional, group/cell layout »Discuss in terms of: work balancing, operational costs, material handling, setup, throughput, production control - capacity planning, job scheduling.

53. egekwu.33153 GT and Mfg Cells l used to show logical implementation steps for GT l benefits include: »reduction in number of perishable tools »lower setup times »lower tooling costs - tools can be “kited” »improvement in efficiency of new equipment.

54. egekwu.33154 Economic Modeling of GT l components of product mfg cost »direct material »direct labor »overhead (materials and labor) l involves minimization of total production costs over a planning horizon (ISAT 330) »subject to constraints of labor, equipment, demand, etc.

55. egekwu.33155 Group Tooling Economic Analysis l there is a marked improvement in group tooling cost (total and unit cost) over conventional tooling cost. l see fig. 5.28.

56. egekwu.33156 Tooling Costs fig. 5.28

57. egekwu.33157 Typical Savings Realized from Successful GT program-p. 221 l Benefits to Design function l Benefits to mfg l benefits to Management l see advantages and disadvantages on pages 226 – 227 of Bedworth. »under utilization of some machines in a group - plant-wide benefit vs. sub- optimization of individual machines.

58. egekwu.33158 Types of Layout - fig. 5.20