Integrated cellular manufacturing system design : an evolutionary algorithm approach

Cellular manufacturing system design has received much attention for the past three decades. The design process involves decisions on (i) cell formation, (ii) cell layout, and (iii) layout of cells on the shop floor. These decisions should be addressed jointly, if full benefits of cellular manufacturing are to be realised. However, due to the complexity of the problem, most researchers addressed these phases sequentially. In this paper, we propose an enhanced evolutionary algorithm to jointly address cell formation and layout problems, based on sequence data. The approach compares favourably to well-known heuristics and performed well on published data sets, providing improved solutions

Machine-Part cell formation through visual decipherable clustering of Self Organizing Map

Machine-part cell formation is used in cellular manufacturing in order to process a large variety, quality, lower work in process levels, reducing manufacturing lead-time and customer response time while retaining flexibility for new products. This paper presents a new and novel approach for obtaining machine cells and part families. In the cellular manufacturing the fundamental problem is the formation of part families and machine cells. The present paper deals with the Self Organising Map (SOM) method an unsupervised learning algorithm in Artificial Intelligence, and has been used as a visually decipherable clustering tool of machine-part cell formation. The objective of the paper is to cluster the binary machine-part matrix through visually decipherable cluster of SOM color-coding and labelling via the SOM map nodes in such a way that the part families are processed in that machine cells. The Umatrix, component plane, principal component projection, scatter plot and histogram of SOM have been reported in the present work for the successful visualization of the machine-part cell formation. Computational result with the proposed algorithm on a set of group technology problems available in the literature is also presented. The proposed SOM approach produced solutions with a grouping efficacy that is at least as good as any results earlier reported in the literature and improved the grouping efficacy for 70% of the problems and found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure

Formation of machine groups and part families in cellular manufacturing systems using a correlation analysis approach

The important step in the design of a cellular manufacturing (CM) system is to identify the part families and machine groups and consequently to form manufacturing cells. The scope 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 three phases. In the first phase, the correlation matrix is used as 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 simultaneously machine groups and part families while maximizing correlation between elements. In the third stage, an algorithm is improved to assign exceptional machines and exceptional parts using respectively angle measure and Euclidian distance. The proposed approach is also applied to the general Group Technology (GT) problem in which exceptional machines and part are considered. Furthermore, the proposed approach has the flexibility to consider the number of cells as a dependent or independent variable. Two numerical examples for the design of cell structures are provided in order to illustrate the three phases of proposed approach. The results of a comparative study based on multiple performance criteria show that the present approach is very effective, efficient and practical.cellular manufacturing; cell formation; correlation matrix; Principal Component Analysis; exceptional machines and parts

Tracing technological development trajectories: A genetic knowledge persistence-based main path approach

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method overcomes the aforementioned drawbacks defining main paths that are almost 10x less complex while containing more of the relevant important knowledge than the main path networks defined by the existing method.Comment: 20 pages, 7 figure

Drape optimization in woven composites manufacture.

This paper addresses the optimisation of forming in manufacturing of composites. A simplified finite element model of draping is developed and implemented. The model incorporates the non-linear shear response of textiles and wrinkling due to buckling of tows. The model is validated against experimental results and it is concluded that it reproduces successfully the most important features of the process. The simple character of the model results in low computational times that allow its use within an optimisation procedure. A genetic algorithm is used to solve the optimisation problem of minimising the wrinkling in the formed component by selecting a suitable holding force distribution. The effect of regularisation is investigated and the L-curve is used to select a regularisation parameter value. Optimised designs resulting from the inversion procedure have significantly lower wrinkling than uniform holding force profiles, while regularisation allows force gradients to be kept relatively low so that suggested process designs are feasible