Applications for
Group Technology
and Cellular
Manufacturing
4.1 Introduction4.2 Artificial Neural Networks
4.3 A Taxonomy of Neural Network Application for GT/CM
4.4 Conclusions
4.1 Introduction
Recognizing the potential of artificial neural networks (ANNs) for pattern recognition, researchers first began to apply neural networks for group technology (GT) applications in the late 1980s and early 1990s. After a decade of effort, neural networks have emerged as an important and viable means for pattern classification for the application of GT and design of cellular manufacturing (CM) systems. ANNs also hold considerable promise, in general, for reducing complexity in logistics, and for streamlining and synergistic regrouping of many operations in the supply chain. This chapter provides a summary of neural network applications developed for group technology and cellular manufacturing.
Group technology has been defined to be, in essence, a broad philosophy that is aimed at (1) identi- fication of part families, based on similarities in design and/or manufacturing features, and (2) systematic exploitation of these similarities in every phase of manufacturing operation [Burbidge, 1963; Suresh and Kay, 1998].
Figure 4.1 provides an overview of various elements of group technology and cellular manufacturing. It may be seen that the identification of part families forms the first step in GT/CM. The formation of part families enables the realization of many synergistic benefits in the design stage, process planning stage, integration of design and process planning functions, production stage, and in other stages down- stream.
In the design stage, classifying parts into families and creating a database that is easily accessed during design results in:
• Easy retrieval of existing designs on the basis of needed design attributes • Avoidance of “reinvention of the wheel” when designing new parts
Nallan C. Suresh
State University of New York at Buffalo
• Countering proliferation of new part designs • Reduction in developmental lead times and costs • Better data management, and other important benefits.
Likewise, in the downstream, production stage, part families and their machine requirements form the basis for the creation of manufacturing cells. Each cell is dedicated to manufacturing one or more part families. The potential benefits from (properly designed) cellular manufacturing systems include:
• Reduced manufacturing lead times and work-in-process inventories • Reduced material handling
• Simplified production planning and control • Greater customer orientation
• Reduced setup times due to similarity of tool requirements for parts within each family • Increased capacity and flexibility due to reduction of setup times, etc.
For implementing GT and designing cells, early approaches relied on classification and coding systems, based on the premise that part families with similar designs will eventually lead to identification of cells. Classification and coding systems involve introducing codes for various design and/or manufacturing attributes. A database is created and accessed through these “GT codes.” This offers several advantages, such as design rationalization and variety reduction and better data management, as mentioned above. But the codification activity involves an exhaustive scrutiny of design data, possible errors in coding, and the necessity for frequent recoding. The need for classification and coding systems has also been on the decline due to advances in database technologies, especially the advent of relational databases.
Therefore, in recent years, cell design methods have bypassed the cumbersome codification exercise. They have relied more on a direct analysis of part routings, to identify parts with similar routings and machine requirements. Part families and machine families are identified simultaneously by manipulating part-machine incidence matrices.
FIGURE 4.1 Elements of GT/CM. (From Suresh, N.C. and Kay, J.M. (Eds.), 1998, Group Technology and Cellular
Manufacturing: State-of-the-Art Synthesis of Research and Practice, Kluwer Academic Publishers, Boston. With
permission.) Part Family Identification Engineering Design Process Planning Production: Cellular Manufacturing Production Planning & Control Other Functions GROUP TECHNOLOGY &
The application of neural networks for GT/CM has undergone a similar evolution. As described below, early efforts for utilizing ANNs for GT/CM were devoted to identification of part families based on design and manufacturing process features, while much of the later efforts have been devoted to the use of neural networks for part-machine grouping based on direct analysis of part routings.
The objective of this chapter is to provide a systematic, and state-of-the-art overview of various neural network architectures developed to support group technology applications. A taxonomy of this literature is provided, in addition to a summary of the implementation requirements, pros and cons, computational performance and application domain for various neural network architectures.
4.2 Artificial Neural Networks
Artificial neural networks have emerged in recent years as a major means for pattern recognition, and it is this particular capability that has made ANNs a useful addition to the tools and techniques applicable for group technology and design of cellular manufacturing systems.
ANNs are “massively parallel, interconnected networks of simple processing units (neurons), and their hierarchical organizations and connections which interact with objects in the real world along the lines of biological nervous systems” [Kohonen, 1984]. The basic elements of a neural network are the processing units (neurons), which are the nodes in the network, and their connections and connection weights.
The operation of a neural network is specified by such factors as the propagation rule, activation/trans- fer function, and learning rule. The neurons receive weighted input values, which are combined into a single value. This weighted input is transformed into an output value through a nonlinear activation function. The activation function could be a hard limiter, sigmoidal nonlinearity or a threshold logic limit. This neuro-computing process is illustrated in Figure 4.2.
FIGURE 4.2 Neural computation.
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