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.

DESIGN OF AN INTERACTIVE VIRTUAL FACTORY USING CELL FORMATION METHODOLOGIES

2003 ◽  
Vol 02 (02) ◽  
pp. 229-246 ◽  
Author(s):  
T. KESAVADAS ◽  
M. ERNZER
Keyword(s):  
Virtual Environment ◽  
Clustering Algorithm ◽  
Flow Analysis ◽  
Cell Formation ◽  
Open Architecture ◽  
Successful Implementation ◽  
Design Environment ◽  
Virtual Factory ◽  
Software Modules ◽  
And Performance

This paper describes an interactive virtual environment for modeling and designing factories and shop floors. The factory building tool is developed as an open architecture in which various modules can be utilized to quickly implement factory design algorithms ranging from plant layout to factory flow analysis. Software modules and utilities have been implemented to allow easy set-up of the visual interface. In this paper, this virtual factory is used to implement cellular manufacturing (CM) system. CM has traditionally been a very complicated system to implement in practice. However successful implementation of the system has improved productivity immersely. Several issues involved in implementing CM within our virtual factory machine modeling and interface designs for defining the cells, are discussed. The mathematical clustering algorithm called Modified Boolean Method was implemented to automatically generate complex virtual environments. The virtual factory makes the process of CM-based factory design a very easy and intuitive process. Though the cell formation problem is NP-complete in 2D space, issues related to human factors and ergonomics can be better perceived in a 3D virtual environment. It also leads to further optimization with respect to maintainability and performance, and thus help get better solutions, which are not visible unless the factory is built. Our virtual factory interface also allows easy reassignment of machines and parts, subcontracting of bottleneck parts and rearranging of machines within the same design environment, making this a productive industrial tool. 3D virtual factory can also be automatically generated from the Part Machine interface called the Virtual Matrix Interface.

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