Machine-Part Family Formation by Using ART-1 Simulator and FLEXY

2011

Cellular manufacturing system is the formation of optimised machine cells, which forms part families with similar processing requirements and associates machines into machine cells in order to optimise the overall production process. To effectively solve this problem a hybrid novel approach is demonstrated in this paper, which is based on weighted average linkage clustering algorithm and fuzzy-ART neural network technique. The proposed technique is tested on published datasets of past literature and the experimental results presented in this study indicate that the method is very effective for small to medium size problems. Testing large size problems and inclusion of ratio level data could be the prospective scope of future for this research work. This work is a significant addition in the on-going research in cellular manufacturing and could be helpful to the future researchers and practitioners in the aforementioned domain of study.

Arxiv preprint arXiv:0803.3343, 2008

In this paper, we consider the problem of forming machine cell in cellular manufacturing (CM). The major problem in the design of a CM system is to identify the part families and machine groups and consequently to form manufacturing cells. The aim of this article is to formulate a multivariate approach based on a correlation analysis for solving cell formation problem. The proposed approach is carried out in two phases. In the first phase, the correlation matrix is used as an original similarity coefficient matrix. In the second phase, Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make machine groups while maximizing correlation between elements. A numerical example for the design of cell structures is provided in order to illustrate the proposed approach. The results of a comparative study based on multiple performance criteria show that the present approach is very effective, efficient and practical

Sakarya University Journal of Science, 2021

Group technology's basic logic is grouping and producing products of the same type together. An important reason behind Group Technology becoming such an important topic is that nowadays companies have quite an extensive range and workshop type production has increased. Both fuzzy clustering and rank order clustering methods use for grouping parts and machines based on a part-machine matrix created from the production flow technique in order to increase productivity and reduce cost and workmanship required. In this study, Group Technology techniques such as the rank order clustering and fuzzy clustering methods were applied in order to increase the efficiency of the production line, reduce transportation between machines, and form a machine-parts groups in the wood cutting department of a furniture company producing modular furniture in Istanbul. The TOPSIS method was used to determine which products to take into account. According to results of the study, it is shown that fuzzy clustering method has overperformed rank order clustering method based on the evaluation criteria which are group productivity with 21,36%, group efficiency with 43,21% and grouping measure with 82,33%.

International Journal of Computer Applications, 2012

Cellular Manufacturing is an important application of group technology principles and is suitable in a medium variety medium volume production environment. It is concerned with the production of part types in a flow line manner by dividing the production system into manufacturing cells. In cellular manufacturing system (CMS) design, cell formation is one of the most important steps which contain identification of machine cells and part families. Usually, minimization of intercell movements is the criteria for CMS design. In this paper, we introduce a heterogeneity concept which indicates diversity of machines in a cell and it is measured based on the machine assigned to a cell and machines required for processing of parts visiting the cell. A non-linear integer programming model for the design of manufacturing cells is proposed in this paper to minimize the heterogeneity of cells formed for the given part-machine incidence matrix. The solution is found through a heuristic procedure based on a genetic algorithm coded in MATLAB. The approach produced solution with a grouping efficacy equal to or better than some of the previous approaches based on seven problems.

International Journal of Intelligent Systems Technologies and Applications, 2011

The design of Cellular Manufacturing Systems (CMS) has attained the significant interest of academicians, researchers and practitioners over the last three decades. CMS is regarded as an efficient production strategy for batch type of production. Literature suggests that since the last two decades neural network based methods have been intensively used in cell formation problems while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART-Centroid Linkage Clustering Technique (FACLCT), to solve the part machine grouping problems in cellular manufacturing systems considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared with the existing clustering models such as simple C-Linkage, K-Means, modified ART1 and genetic algorithm and achieved better performance. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.

2008

Part family formation using fuzzy cluster analysis in Group Technology (GT) and feature recognition for tool access direction using relationship matrix in Computer Aided Process Planning (CAPP) have been presented in this paper. In the process of identifying part-families and machine groups, Fuzzy Cluster Analysis to form machine-part fuzzy relative matrix which converted into a zero-one conventional matrix. A new similarity coefficient, which involves all the entries of the machine-part fuzzy relative matrix resulting in a more realistic part family formation was suggested. The feature extraction system presented in this paper is designed to extract features from a STEP file in Boundary Representation (B-Rep) and Attributed Adjacency matrix. The system is able to automatically extract alternate tool axis directions (TADs). There is a potential applications in setup change cost optimisation and fixture selection.

Journal of Operations Management, 1986

Integrated Manufacturing …, 2003

This paper introduces a modified single linkage clustering heuristic (MOD-SLC). The proposed MOD-SLC objective is to test the application of Baroni-Urban and Buser (BUB) similarity coefficient to the manufacturing cell formation (MCF) problem instead of Jaccard's similarity coefficient. The MOD-SLC has been compared and evaluated against three cluster formation-based heuristics for MCF. The three heuristics are: the single linkage clustering, enhanced rank order clustering, and direct clustering algorithm. The MCF methods considered in this comparative and evaluative study belong to the cluster formation approach of solving the MCF problem. The comparison and evaluation are performed using four published performance measures. A total of 25 published and ten hypothetical and randomly generated problem data sets are used in the proposed evaluative study. Results analysis is carried out to test and validate the proposed BUB based MOD-SLC. Finally the pros and cons of each method are stated and discussed.

Applied Soft Computing, 2012

Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate of flow lines. Although CM provides many benefits in reducing throughput times, setup times, workin-process inventories but the design of CM is complex and NP complete problem. The cell formation problem based on operation sequence (ordinal data) is rarely reported in the literature. The objective of the present paper is to propose a visual clustering approach for machine-part cell formation using Self Organizing Map (SOM) algorithm an unsupervised neural network to achieve better group technology efficiency measure of cell formation as well as measure of SOM quality. The work also has established the criteria of choosing an optimum SOM map size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology. To evaluate the performance of the proposed algorithm, we tested the several benchmark problems available in the literature. The results show that the proposed approach not only generates the best and accurate solution as any of the results reported, so far, in literature but also, in some instances the results produced are even better than the previously reported results. The effectiveness of the proposed approach is also statistically verified.

TECHNICAL SCIENCES, 2009

The paper considers the machine-part grouping problem, as equivalent to partitioning the set of machines and operations into subsets, corresponding to block diagonalisation with constraints. The attempts to solve the problem with clustering methods are outlined. The difficulties encountered are presented, related to (i) ambiguity of formulations; (ii) selection of criteria; and (iii) lack of effective algorithms. These are illustrated in more detail with a limited survey of similarity and distance definitions, and of criteria used, constituting the main body of the paper. The return is proposed to the basic paradigm of cluster analysis, as providing simple and fast algorithms, which, even if not yielding optimal solutions, can be controlled in a simple manner, and their solutions improved.