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Group Technology GT Job shop production System Batch production System Mass production System.

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Presentation on theme: "Group Technology GT Job shop production System Batch production System Mass production System."— Presentation transcript:

1 Group Technology GT Job shop production System Batch production System Mass production System

2 Group Technology Group technology (GT) is a manufacturing philosophy that seeks to improve productivity by grouping parts and products with similar characteristics into families and forming production cells with a group of dissimilar machines and processes. The group of similar parts is known as part family and the group of machineries used to process an individual part family is known as machine cell. It is not necessary for each part of a part family to be processed by every machine of corresponding machine cell.

3 Group technology begun by grouping parts into families, based on their attributes (Geometry, manufacturing process ). Geometric classification of families is normally based on size and shape, while production process classification is based on the type, sequence, and number of operations. The type of operation is determined by such things as the method of processing, the method of holding the part, the tooling. There are three methods that can be used to form part families: –Manual visual inspection –Production flow analysis –Classification and coding Manual visual inspection involves arranging a set of parts into groups by visually inspecting the physical characteristics of the parts. Group Technology

4 Manual visual inspection Part Family 1 Part Family 2

5 Production flow analysis: Parts that go through common operations are grouped into part families. The machines used to perform these common operations may be grouped as a cell, consequently this technique can be used in facility layout (factory layout)

6 Production flow analysis

7 Rank Order Clustering Algorithm: Rank Order Clustering Algorithm is a simple algorithm used to form machine-part groups. Step 1: Assign binary weight and calculate a decimal weight for each row. Step 2: Rank the rows in order of decreasing decimal weight values. Step 3: Repeat steps 1 and 2 for each column. Step 4: Continue preceding steps until there is no change in the position of each element in the row and the column.

8 Part Number Machine ID A 11 B 11 C 11 D 111 E 111 Example #1 Consider a problem of 5 machines and 6 parts. Try to group them by using Rank Order Clustering Algorithm.

9 Step 1: Part Numbers Decimal equivalent Rank Machine ID B. Wt: A = 105 B = 244 C =362 D = 26 3 E =371 Step 2: Must Reorder!

10 Step 2: Part Number Machine ID E111 C11 D111 B11 A11

11 Step 3: Part Number B. WT Machine ID E C D2 111 B A Decimal equivalent = = = = = =16 Rank Step 4: Must Reorder

12 Back at Step 1: Part NumberD. EqvRank B Wt: E =56 1 C = 48 2 D = 7 3 B =6 4 A =5 5 Order stays the same: STOP!

13 Example #2 Part Number Machine ID A111 B11 C1111 D111 E1111

14 Example #2 Part Number Equivalent decimal value Rank Machine ID Binary wt A B11205 C D E Step 1: Assign binary weight and calculate a decimal weight for each row

15 Example #2 Part Number Machine ID C1111 D111 A111 E1111 B11 Step 3: Reorder the matrix according to rank

16 Example #2 Part Number Machine ID Binary wt C D8111 A4111 E21111 B111 Equ. Decimal Value Rank Step 4: Assign binary weight and calculate a decimal weight for each Column

17 Example #2 Part Number Machine ID C1111 D111 A111 E1111 B11 Step 5: Reorder the matrix according to rank

18 Example #2 Part Number Equivalent decimal value Rank Machine ID Binary wt C D A E B1155 Repeat Step 1&2 : Assign binary weight and calculate a decimal weight for each row Order stays the same:

19 Example #2 Part Number Machine ID Binary wt C D8111 A4111 E21111 B111 Equ. Decimal Value Rank Repeat Step 4 & 5 Order stays the same: STOP!

20 Example #2 Part Number Machine ID C1111 D111 A111 E1111 B11 Part family 1: Part Nos. 1, 4, 2 & 7 Machine Cell 1: C, D & A Part family 2: Part Nos. 3, 5, and 5 Machine Cell 2: E & B Voids Exceptional parts No. of exceptional Parts: 3 No. of Voids: 5 No. of bottleneck machines: 2(Machines D & E) Solutions for overcoming this problem? Duplicate machines Alternate process plans Subcontract these operations

21 Duplicate machines Part Number Machine ID C1111 D111 A111 E1111 B11 Part Number Machine ID C1111 D1 A111 E1 E111 B11 D11 No. of exceptional Parts: 0 No. of Voids: 9 No. of bottleneck machines: 0 No. of duplicate machine: 2(Machines D & E No. of exceptional Parts: 3 No. of Voids: 5 No. of bottleneck machines: 2(Machines D & E)

22 Alternate process plans Part Number Machine ID C1111 D111 A111 E1111 B11 Part Number Machine ID C1111 A111 E1111 B11 D111 No. of exceptional Parts: 2 No. of Voids: 3 No. of bottleneck machines: 2(Machines D & E No. of exceptional Parts: 3 No. of Voids: 5 No. of bottleneck machines: 2(Machines D & E)

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24 Step 1: Part Numbers Decimal equivalent Rank Machine ID B. Wt: A = 105 B = 244 C =362 D = 26 3 E =371

25 Step 1: Part Numbers Decimal equivalent Rank Machine ID B. Wt: A = 104 B = 243 C =55 D = 262 E =371 Step 2: Must Reorder!

26 Step 3: Part Number B. WT Machine ID E D B2 11 A C Decimal equivalent 2 4 = = = = = =17 Rank Step 4: Must Reorder

27 Back at Step 1: Part Number D. EqvRank B. Wt: Machine ID E =56 1 D = 7 3 B = 6 4 A = 5 5 C = 24 2 Step 2: Must Reorder

28 Back at Step 1: Part Number B. WT Machine ID E C D2 111 B A Decimal equivalent 2 4 = = = = =5 Rank Step 2: Must Reorder

29 Part Number D. EqvRank B. Wt: Machine ID E =56 1 C = 48 2 D = 7 3 B = 6 4 A = 5 5 Order stays the same: STOP!

30 Clustering Methods Using Process Similarity methods: –Create Machine – Part Matrices –Compute machine pair-wise similarity Coefficient comparisons:

31 Part Number Machine ID A 11 B 11 C 11 D 111 E 111 Example:

32 Computing Similarity Coefficients: Total number of coefficient is: [(N-1)N]/2 = [(5-1)5]/2 = 10 For 25 machines (typical number in a small Job Shop): 300 S ij s Part Number Machine ID A 11 B 11 C 11 D 111 E 111

33 Continuing: Part Number Machine ID A 11 B 11 C 11 D 111 E 111

34 Grouping of parts using Clustering Methods Similarity coefficients of machine pairs Machine pairs ABACADAEBCBDBECDCEDE SC ADCE Dendrogram B

35 Coding refers to the process of assigning symbols to the parts The symbols represent design attributes of parts or manufacturing features of part families Although well over 100 classification and coding systems have been developed for group technology applications, all of them can be grouped into three basic types: –Monocode or hierarchical code –Polycode or attribute –Hybrid or mixed code Classification and Coding

36 MONOCODE (HIERARCHICAL CODE) This coding system was originally developed for biological classification in 18th century. In this type of code, the meaning of each character is dependent on the meaning of the previous character; that is, each character amplifies the information of the previous character. Such a coding system can be depicted using a tree structure Monocode of Fig. 1 : A-1-1-B-2 Fig. 1 Spur gear

37 hierarchical code for the spur gear (Fig. 1)

38 POLYCODE (ATTRIBUTE CODE): The code symbols are independent of each other Each digit in specific location of the code describes a unique property of the workpiece –it is easy to learn and useful in manufacturing situations where the manufacturing process have to be described –the length of a polycode may become excessive because of its unlimited combinational features

39 POLYCODE (ATTRIBUTE CODE): Polycode for the spur gear (Fig. 1): 22213

40 MIXED CODE (HYBRID CODE): In reality, most coding systems use a hybrid (mixed) code so that the advantages of each type of system can be utilized. The first digit for example, might be used to denote the type of part, such as gear. The next five position might be reserved for a short attribute code that would describe the attribute of the gear. The next digit (7th digit) might be used to designate another subgroup, such as material, followed by another attribute code that would describe the attributes.

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