A clustering algorithm for machine cell formation in group technology using minimum spanning trees

1994 ◽  
Vol 32 (9) ◽  
pp. 2149-2158 ◽  
Author(s):  
G. SRINIVASAN
Keyword(s):  
Clustering Algorithm ◽  
Spanning Trees ◽  
Group Technology ◽  
Cell Formation ◽  
Minimum Spanning Trees ◽  
Machine Cell

The application of a knowledge-based clustering algorithm as an aid to probe selection and inspection process planning

1998 ◽  
Vol 212 (4) ◽  
pp. 299-305 ◽  
Author(s):  
A Ajmal ◽  
S G Zhang
Keyword(s):  
Process Planning ◽  
Incidence Matrix ◽  
Clustering Algorithm ◽  
Group Technology ◽  
Cell Formation ◽  
Inspection Planning ◽  
Knowledge Based ◽  
Probe Selection ◽  
Inspection Process ◽  
Probe Calibration

This paper outlines the development of a clustering algorithm used for inspection planning which allows each inspection feature to be inspected at a designated cell. This is achieved by grouping (a) inspection features into feature families and (b) probe orientations into probe cells. This would result in minimal probe calibration errors and part installation errors for the relative tolerance features. This procedure would reduce the time for probe exchange and reinstallation of parts. An incidence matrix representation has been developed to represent the relationship between inspection features and their relative probe orientations. The incidence matrix which is used for grouping feature families and probe cells are similar in function to the concept of group technology (GT) as used in machine cell formation. The knowledge-based clustering algorithm possesses the flexibility for consideration of multiple constraints for grouping probe cells and feature families. The application of the developed clustering algorithm satisfies the requirement of the inspection feature grouping and provides efficiency and effectiveness in probe selection and inspection process planning for coordinate measuring machines (CMMs).

Application of Syntactic Pattern Recognition Approach in Design and Optimisation of Group Machining Systems

2010 ◽  
Vol 165 ◽  
pp. 342-347 ◽  
Author(s):  
Mieczyslaw Siemiatkowski
Keyword(s):  
Pattern Recognition ◽  
Group Process ◽  
Clustering Algorithm ◽  
Group Technology ◽  
Distance Measure ◽  
Cell Formation ◽  
Systems Design ◽  
Syntactic Pattern Recognition ◽  
Job Sequencing ◽  
Syntactic Pattern

The focus of this paper is on planning applications of group technology (GT) and the design of related layouts for multi-assortment cellular manufacturing (CM) of mechanical parts. A methodical approach is developed to optimally solve cell formation (CF) problems with CM systems design, which consists in the identification of machine cells and corresponding part families. The approach involves the use of syntactic pattern recognition concepts from the field of artificial intelligence (AI). It is based on methods of strings matching and clustering, applied extensively in genetics, molecular chemistry and biological sciences. The CF strategy followed implies clustering character strings that denote machine sequences in process routings. Numerical quantification of dissimilarity between part routings by a specific distance measure and the concept of average linkage clustering algorithm (ALCA) are at the core of the clustering procedure. The use of the approach is studied numerically with regard to a real industrial case and diverse layouts of cellular system are considered, including those with machine sharing. Group process alternatives with given system layouts and workflows prototyped by definite job sequencing rules, are simulated using programmed models. Generated design solutions are subjected to further analysis and quantitative evaluation by assumed measures of their operational performance.

scholarly journals CELLULAR MANUFACTURING LAYOUT DESIGN USING HEURISTIC CLUSTERING ALGORITHM AND LPP MODEL

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
S. Ramesh ◽  
N. Arunkumar ◽  
R. Vijayaraj
Keyword(s):  
Clustering Algorithm ◽  
Cell Formation ◽  
Shop Floor ◽  
Benchmark Problems ◽  
Algorithm Performance ◽  
Batch Sizes ◽  
Optimisation Model ◽  
Exceptional Elements ◽  
Heuristic Clustering ◽  
Machine Cell

This mathematical model forms machine cells, optimises the costs of unassigned machines and components, and designs the shop floor cell layout to have minimal movement of materials. The complete similarity measure algorithm forms machine cells and part families in a refined form. Later, exceptional elements are eliminated in the optimisation model by using machine duplication and sub-contracting of parts. Then the shop floor layout is designed to have optimised material movements between and within cells. An evaluation of the cell formation algorithm’ performance is done on the benchmark problems of various batch sizes to reveal the process’s capability compared with other similar methods. The data of machining times are acquired and tabulated in a part incidence matrix, which is used as input for the algorithm. The results from the linear programming optimisation model are that costs are saved, machines are duplicated, parts are sub-contracted, and there are inter- and intra- cellular movements. Finally, the output of the inbound facility design is the floor layout, which has machine cell clusters within the optimised floor area.

Machine cell formation in generalized group technology

2001 ◽  
Vol 41 (2) ◽  
pp. 227-240 ◽  
Author(s):  
Belarmino Adenso-Dı́az ◽  
Sebastián Lozano ◽  
Jesús Racero ◽  
Fernando Guerrero
Keyword(s):  
Group Technology ◽  
Cell Formation ◽  
Machine Cell

scholarly journals A Time- and Message-Optimal Distributed Algorithm for Minimum Spanning Trees

2020 ◽  
Vol 16 (1) ◽  
pp. 1-27
Author(s):  
Gopal Pandurangan ◽  
Peter Robinson ◽  
Michele Scquizzato
Keyword(s):  
Distributed Algorithm ◽  
Spanning Trees ◽  
Minimum Spanning Trees
Sign in / Sign up

Export Citation Format

Share Document