Part Family Formation Research Articles

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.

Machine cell formation using a mathematical model and a genetic-algorithm-based heuristic

This paper presents a comprehensive mathematical model and a genetic-algorithm-based heuristic for the formation of part families and machine cells in the design of cellular manufacturing systems. The model incorporates dynamic cell configuration, alternative routings, sequence of operations, multiple units of identical machines, machine capacity, workload balancing among cells, operation cost, subcontracting cost, tool consumption cost, set-up cost and other practical constraints. To solve this model efficiently, a two-phase genetic-algorithm-based heuristic was developed. In the first phase, independent cells are formed which are relatively simple to generate. In the second phase, the solution found during the first phase is gradually improved to generate cells optimizing inter-cell movement and other cost terms of the model. A number of numerical examples of different sizes are presented to demonstrate the computational efficiency of the heuristic developed.

A comprehensive mathematical model for the design of cellular manufacturing systems

The design of cellular manufacturing systems (CMS) involves many structural and operational issues. One of the important design steps is the formation of part families and machine cells. In this paper, a comprehensive mathematical model for the design of CMS based on tooling requirements of the parts and tooling available on the machines is proposed. The model incorporates dynamic cell configuration, alternative routings, lot splitting, sequence of operations, multiple units of identical machines, machine capacity, workload balancing among cells, operation cost, cost of subcontracting part processing, tool consumption cost, setup cost, cell size limits, and machine adjacency constraints. Numerical examples are presented to demonstrate the model and its potential benefits.

Framework of a computer-aided short-run SPC planning system

Statistical process control (SPC) is recognized as a technique to achieve cost-effective quality control through continuous manufacturing process improvement. But with growing demand for small-batch and high-variety products in the current dynamic market, the involved manufacturing processes are becoming more complex, variable, and flexible, which are not suitable for implementing SPC in the traditional way. Hence, short-run SPC is applied instead. Planning is a critical phase in the implementation of short-run SPC, which includes the formation of part families and the determination of corresponding data collection. To ensure homogeneity of the family members, this paper addresses preliminary analysis on the characteristics and applications of pertinent factors, and statistical analysis for SPC-based part family formation. To improve the efficiency of SPC planning and the adaptation for computer-integrated manufacturing, a framework for a computer-aided short-run SPC planning system is proposed using group technology classification and coding concepts. This invokes a 29-digit hybrid code appended to the Opitz coding scheme. Further, a supportive database is also proposed to facilitate coding information retrieval and system updating. A case study is shown with data collected from injection-mold manufacturing, which typically involves small-batch processes.

Multi-objectives for incremental cell formation problem

Over the past three decades considerable amount of research work has been reported in the literature of Group Technology (GT). Most of the research work is concerned with formation of machine cells and part families. This is because cell formation is considered to be the most complex and the most important aspect of Cellular Manufacturing System (CMS). Due to NP completeness of cell formation problem, many heuristics have been developed. These heuristics are developed for both single as well as multiple objectives for the comprehensive cell formation. Here all part types and machine types are considered at a time for cell conversion and that all cells are designed at a single point in time. But planning and implementation of most cell conversions in industry are incremental ones, and not comprehensive. This issue has not been addressed in GT literature adequately. In this paper we consider multiple objectives for incremental cell formation and develop, a lexicographic based simulated annealing algorithm. The performance of the algorithm is tested over several data sets by taking different initial feasible solutions generated using different heuristics.

Content-Based Search Techniques for Searching CAD Databases

AbstractThe popularity of 3D CAD systems is resulting in large assembly and part databases being created by product development organizations. Over the last few years several content-based techniques have emerged to search these databases. Ability to perform content-based searches on CAD databases is expected to help the designers and manufacturing engineers in many different ways. Representative examples include part-family formation, redesign suggestion generation, supplier selection, cost estimation, tooling design, machine selection, stock selection, and design reuse. This paper covers the following three topics. First, it presents an overview of different types of part search techniques. Second, it presents an overview of the different aspects of assembly search techniques. Finally, it identifies the open challenges in these areas and outlines future research directions.

An ant-based clustering algorithm for manufacturing cell design

This paper proposes a new part clustering algorithm that uses the concept of ant-based clustering in order to resolve machine cell formation problems. The three-phase algorithm mainly utilizes distributed agents which mimic the way real ants collect similar objects to form meaningful piles. In the first phase, an ant-based clustering model is adopted to form the initial part families. For the purpose of part clustering, a part similarity coefficient is modified and used in the similarity density function of the model. In the second phase, the K-means method is employed in order to achieve a better grouping result. In the third phase, artificial ants are used again to merge the small, refined part families into larger part families in a hierarchical manner. This would increase the flexibility of determining the number of final part families for the factory layout designer. The proposed algorithm has been developed into a software system called the ant-based part clustering system (APCS). In addition to part family formation, APCS performs the tasks of machine assignment and performance evaluation. Finally, performance evaluation of the proposed algorithm was conducted by testing some well-known problems from literature. The evaluation results show that the algorithm is able to solve the cell formation problems effectively.

Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications

Group technology (GT) is a useful way to increase productivity with high quality in flexible manufacturing systems. Cell formation (CF) is a key step in GT. It is used to design a good cellular manufacturing system that uses the similarity measure between parts and machines so that it can identify part families and machine groups. Recently, fuzzy clustering has been applied in GT because the fuzzy clustering algorithm can present partial memberships for part–machine cells so that it is suitably used in cellular manufacturing systems for a variety of real cases. However, these fuzzy clustering algorithms are only used for numeric data of parts and machines. In this paper, we apply a mixed-variable fuzzy clustering algorithm, called mixed-variable fuzzy c-means (MVFCM), to CF in GT. According to real application examples, the MVFCM algorithm gives good results when it is applied to the mixed-variable types of CF data.

Part family formation through fuzzy ART2 neural network

In order to overcome some unavoidable factors, like shift of the part, that influence the crisp neural networks' recognition, the present study is dedicated in developing a novel fuzzy neural network (FNN), which integrates both the fuzzy set theory and adaptive resonance theory 2 (ART2) neural network for grouping the parts into several families based on the image captured from the vision sensor. The proposed network posses the fuzzy inputs as well as the fuzzy weights. The model evaluation results showed that the proposed fuzzy neural network is able to provide more accurate results compared to the fuzzy self-organizing feature maps (SOM) neural network [R.J. Kuo, S.S. Chi, P.W. Teng, Generalized part family formation through fuzzy self-organizing feature map neural network, International Journal of Computers in Industrial Engineering, 40 (2001b) 79–100] and fuzzy c-means algorithm.

Fuzzy part family formation for cellular manufacturing systems

Most of the techniques related to part family formation require precise numerical data for part's features. However, in many situations, the parts features are too ill-defined and are too susceptible to analysis by traditional discrete methods. Fuzzy set theory allows the parts to belong to different groups with different memberships. Moreover, different attributes may not have equal importance. The AHP (analytical hierarchy process) model gives the weightage for different attributes and also checks the consistency of the judgements. This paper suggests two models for part family formation using fuzzy mathematics and the AHP model. One model is based on fuzzy logic and AHP and the second model uses fuzzy equivalence ratios and AHP. Models can be integrated with the existing coding systems used by organizations. Both models are validated using problems from literature.

Formation of machine cells and part families in cellular manufacturing systems using a linear assignment algorithm

This paper presents a linear assignment algorithm for machine-cell and part-family formation for the design of cellular manufacturing systems. The present approach begins with the determination of part-family or machine-cell representatives by means of comparing similarity coefficients between parts or machines and finding a set of the least similar parts or machines. Using the group representatives and associated similarity coefficients, a linear assignment model is formulated for solving the formation problem by allocating the remaining parts or machines and maximizing a similarity index. Based on the formulated linear assignment model, a group formation algorithm is developed. The results of a comparative study based on multiple performance criteria and many existing data sets show that the present approach is very effective and efficient, especially in dealing with large-sized problems.

Formation of machine groups and part families: a modified SLC method and comparative study

This paper introduces a modified single linkage clustering heuristic (MOD‐SC). 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.

The Role of Fuzzy and Genetic Algorithms in Part Family Formation and Sequence Optimisation for Flexible Manufacturing Systems

1. A new similarity coefficient measure has been developed and this coefficient measure is used to form a part-family. 2. A mathematical model that uses this similarity coefficient for solving the part-family formation problems optimally in an FMS is developed. The fuzzy approach has the special advantage of producing more accurate results than conventional clustering and other methods. It not only reveals the specific part family that a part belongs to, but also provides the degree of membership of a part associated with each part family. This will give a balanced work load for the machine. In the second part of this paper, the introduction of the concept of genetic algorithms is proposed to eliminate more job sequences and, finally, the optimum sequence is obtained through the minimum penalty cost. Software is developed and implemented to obtain an optimum sequence and, finally, a numerical example is given as an illustration.

Cell formations in the uni-directional loop material handling environment

The formation of machine cells and part families in a uni-directional loop material handling facility has been considered in this paper. The decision parameters include demand data and routing sequence of parts, processing times, unique and duplicated machine types, machine capacities or workload, and the uni-directional uni-cyclic material handling facilities. In this unique cell formation process, a divide and conquer algorithm is proposed to quickly estimate the intercell material movement cost that includes both forward flow and backward flow, instead of the typical measure that minimizes the number of intercell movements between machine cells. The detail of the proposed three-phase approach and the significance of material flow features are illustrated with two examples. Additional computational studies include 18 instances that are adapted from the literature, with the large data set up to 37 machines and 45 part types. The experimental results indicate that the approach is efficient and effective for performing cell formations in this particular material handling environment. The cell formations, which are developed by the criteria of minimizing the number of intercell movements and minimizing the actual intercell flow cost, are also compared.

Cell formation using a Fuzzy Min-Max neural network

This paper proposes the application of a Fuzzy Min-Max neural network for part family formation in a cellular manufacturing environment. Once part families have been formed, a minimum cost flow model is used to form the corresponding machine cells. For simplicity, the input data are in the form of a binary part- machine incidence matrix, although the algorithm can work with an incidence matrix with continuous values. The application of Fuzzy Min-Max is interpreted in physical terms and compared with a related neural network applied previously for cell formation, the Fuzzy ART network. Both neural networks have similarities and differences that are outlined. The algorithms have been programmed and applied to a large set of problems from the literature. Fuzzy Min-Max generally outperforms Fuzzy ART, and the computational times are small and similar in both algorithms.

A New Approach to Fuzzy Part-Family Formation in Cellular Manufacturing Systems

Group technology (GT) is one of the major issues in successful implementation of cellular manufacturing systems. The success of GT implementation depends only on an effective formation of part-families. The effective formation of part-families depends mainly on an effective formation of a similarity coefficient measure. Many similarity coefficients have been produced over the past three decades, but better similarity coefficient measures are required. The decision-making process in a manufacturing system often involves uncertainties and ambiguities. Under such circumstances, fuzzy methodologies have proved to be effective tools for taking fuzziness into consideration. In this paper, we use fuzzy cluster analysis to form part-families and assign parts to existing part-families. We have established a new approach to convert a fuzzy clustering matrix into a zero-one incidence matrix. We have also developed a new similarity coefficient measure and this coefficient measure is used to form a part-family. A mathematical model that uses this similarity coefficient for solving optimally the part-family formation problems in cellular manufacturing is developed. Finally, it is compared with other models by giving an illustration with a numerical example.

Fuzzy Part Family Formation Based on Grey Relational Analysis

Part family formation is one of the most important techniques in computer-aided process planning (CAPP). This paper presents a new method that uses grey relational analysis and fuzzy clustering to form part families. The main objective of this paper is to identify part families based on a new similarity coefficient which considers processing time, lot size, machine usability, etc., by using grey relational analysis. After the similarity coefficient is calculated, a fuzzy clustering method is used to obtain part families. The main contribution of this paper is to provide a rigorous mathematical approach to forming part families. Part family formation can be controlled, for different applications, by setting a resolution threshold, so the approach is flexible, practical and reliable.

Classification and coding approaches to part family formation under a fuzzy environment

Classification and coding is a major approach to solve the part family formation problem in group-technology applications. Traditionally, all features in a classification and coding system are considered crisp. It has been recognized that more realistically some features should be fuzzy. This paper addresses issues involved in using the classification and coding approach for part family formation under a fuzzy environment. Methods for dealing with major issues such as part coding, computation of similarity between parts, and part family formation are proposed and discussed in detail. Examples were given to show how these methods could be applied.

Generalized part family formation through fuzzy self-organizing feature map neural network

Group technology (GT) addresses the problem of the part family formation. Similar parts, based on a certain similarity of characteristics, are grouped into a family. A design engineer facing the task of developing a new part can use a GT code or an image of the part to determine whether similar parts exist in a computer aided design (CAD) database. The manufacturing engineer can design the cellular manufacturing system based on different families. These can dramatically shorten both the design and the manufacturing life cycle. However, owing to some unavoidable factors, like brightness of light and shift of the part, the crisp network cannot recognize the parts correctly under the above-mentioned conditions. Thus, the present study is dedicated to developing a novel fuzzy neural network (FNN) for clustering the parts into several families based on the image captured from the vision sensor. The proposed network, which possesses the fuzzy inputs as well the fuzzy weights, integrates the self-organizing feature map (SOM) neural network and the fuzzy set theory. The model evaluation results showed that the proposed FNN can provide a more accurate decision compared to the fuzzy c-means algorithm.

CF-GGA: A grouping genetic algorithm for the cell formation problem

In manufacturing, the machine-part cell formation (MPCF) problem addresses the issues surrounding the formation of part families based on the processing requirements of the components, and the identification of machine groups based on their ability to process specific part families. Past research has shown that one key aspect of attaining efficient groupings of parts and machines is the block-diagonalization of the given machine-part (MP) incidence matrix. This paper presents and tests a grouping genetic algorithm (GGA) for solving the MPCF problem and gauges the quality of the GGA's solutions using the measurements of efficiency (Chandrasekharan and Rajagopalan 1986a) and efficacy (Kumar and Chandrasekharan 1990). The GGA in this study, CF-GGA, a grouping genetic algorithm for the cell formation problem, performs very well when applied to a variety of problems from the literature. With a minimal number of parameters and a straightforward encoding, CF-GGA is able to match solutions with several highly complex algorithms and heuristics that were previously employed to solve these problems.