Neural computing-based design of components for cellular manufacturing

In this paper, a neuro computing approach for integrating design and manufacturing engineering is developed. Products and components have been traditionally designed without considering constraints imposed by a manufacturing system. With the introduction of concurrent engineering, design and manufacturing engineering are viewed as an integrated area. A three layer neural network that integrates several manufacturing functions is constructed for designing a cellular manufacturing system. The neuro computing system proposed provides a designer with the desired features that meet the current manufacturing constraints for design of a new component. The proposed methodology overcomes the typical limitations of neutral networks such as the internal representation and training problem, and proves to be appropriate for concurrent engineering.     

Capability-based distributed layout approach for virtual manufacturing cells

It is shown in the literature that in highly volatile manufacturing environments functional job shops and classical cellular manufacturing systems do not perform well. Classical cellular manufacturing systems are very sensitive to changing production requirements due to their limited flexibility. In order to adapt cellular manufacturing systems to volatile manufacturing environments, the virtual cellular manufacturing concept was proposed in the 1980s by the National Bureau of Standards in USA. This concept is similar to group technology where job families are processed in manufacturing cells. The main difference between a virtual cell and the classic cell is in the dynamic nature of the virtual manufacturing cell; whereas the physical location and identity of classic cell is fixed, the virtual cell is not fixed and will vary with changing production requirements. The virtual manufacturing cell concept allows the flexible reconfiguration of shop floors in response to changing requirements. In the literature, the formation and scheduling process of virtual cells are clearly explained and researched in detail. However, the layout issue is not addressed entirely. Virtual cells are generally formed over functionally divided job shops. Forming virtual cells over a functional layout may adversely affect the performance of a virtual cellular manufacturing system. There is a need to search for different layout strategies in order to enhance the performance. The distributed layout approach may be a better alternative for virtual cellular manufacturing applications. In this research paper, a novel capability-based approach is proposed for the design of distributed layouts. A simulated annealing based heuristic algorithm is developed from the distributed layout. The proposed approach is tested with a problem with real data. An example is also shown in order to give an idea about the superiority of a capability-based distributed layout over the functional layouts in forming virtual manufacturing cells.     

A cellular similarity coefficient algorithm for the design of manufacturing cells

The development and implementation of an integrated system for computer-aided process planning and cellular manufacturing design is described. A coding scheme is proposed for classifying parts according to the manufacturing processes required to produce them. The routeing data and the classification codes obtained from computer-aided process planning are then stored in a database. A heuristic algorithm has also been developed for designing manufacturing cells using the classification data stored in the process planning database. This algorithm is based on a new concept developed in this study called cellular similarity coefficient which considers the similarity between machine cells rather than individual machines, as in the case of other similarity coefficients.     

Design of dedicated,shared and remainder cells in a probabilistic demand environment

In this paper, a new layered cellular manufacturing system is proposed to form dedicated, shared and remainder cells to deal with the probabilistic demand, and later its performance is compared with the classical cellular manufacturing system. In the layered cellular design, each family may need more than one cell to cover capacity requirements. The proposed approach for layered cellular design involves five stages: (1) product clustering, (2) identifying number of cells and demand coverage probabilities, (3) determining cell types using the proposed heuristic procedure, (4) performing simulation to determine operating conditions and (5) statistical analysis to pick the best design configuration among layered cellular designs. Simulation and statistical analysis are performed to help identify the best design within and among both layered cellular design and classical cellular design. It was observed that as the number of part families increased, the number of machines needed to process the parts decreased first. Then the number of machines started to increase once again as the number of part families continued to increase. Another observation was that the average flow time and total WIP were not always the lowest when additional machines were used by the system. The last and the most important observation was that the layered cellular system provided much better results than the classical cellular system when high demand fluctuation was observed.     

Cell formation: the need for an integrated solution of the subproblems

Cellular manufacturing is a viable option in many manufacturing systems. There are various subproblems in the design of a cellular manufacturing system. These are machine group and part family formation, machine duplication, intracell layout and intercell layout. The only comprehensive design strategy that attempts to address all of these is production flow analysis. However, this technique is a sequential strategy where the subproblems mentioned above are assumed nested within each other and are solved in a forward pass with no feedback. This is a satisfactory approach only in cases where the part families are relatively disjoint and machine groups are formed without constraints on machine duplication to eliminate intercell flow. The presence of bottleneck machines and parts renders the problem considerably more complex, as the subproblems influence each other substantially. This paper presents an integrated framework for solving these subproblems by generating a limited set of feasible alternative solutions.     

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.     

A methodology for designing flexible cellular manufacturing systems

Cell formation in cellular manufacturing deals with the identification of machines that can be grouped to create manufacturing cells and the identification of part families to be processed within each cell. Dynamic and random variations in part demands can negatively impact cell performance by creating unstable machine utilizations. The purpose of this paper is to introduce and illustrate an interactive cell formation method that can be used to design 'flexible' cells. Flexibility in this context refers to routing flexibility (i.e., the ability for the cellular system to process parts within multiple cells) and demand flexibility (i.e., the ability of the cell system to respond quickly to changes in part demand and part mix). Through an experimental analysis using multiple data sets, we also validate the procedure and provide guidelines for parameter settings depending upon the type of flexibility of interest to the user. Finally, trade-offs and interdependences between alternative types of flexibility in the context of cellular systems are illustrated.     

Design for manufacturing: application of collaborative multidisciplinary decision-making methodology

Design for manufacturing is often difficult for mechanical parts, since significant manufacturing knowledge is required to adjust part designs for manufacturability. The traditional trial-and-error approach usually leads to expensive iterations and compromises the quality of the final design. The authors believe the appropriate way to handle product design for manufacturing problems is not to formulate a large design problem that exhaustively incorporates design and manufacturing issues, but to separate the design and manufacturing activities and provide support for collaboration between engineering teams. In this article, the Collaborative Multidisciplinary Decision-making Methodology is used to solve a product design and manufacturing problem. First, the compromise Decision Support Problem is used as a mathematical model of each engineering teams’ design decisions and as a medium for information exchange. Second, game-theoretic principles are employed to resolve couplings or interactions between the teams’ decisions. Third, design-capability indices are used to maintain design freedom at the early stages of product realization in order to accommodate unexpected downstream design changes. A plastic robot-arm design and manufacturing scenario is presented to demonstrate the application of this methodology and its effectiveness for solving a complex design for manufacturing problem in a streamlined manner, with minimal expensive iterations.     

Performance of Fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences

The problem context for this study is one of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems and for streamlining material flows in general. Given this problem context, this study develops an experimental procedure to compare the performance of a fuzzy ART neural network, a relatively recent neural network method, with the performance of traditional hierarchical clustering methods. For large, industry-type data sets, the fuzzy ART network, with the modifications proposed here, is capable of performance levels equal or superior to those of the widely used hierarchical clustering methods. However, like other ART networks, Fuzzy ART also results in category proliferation problems, an aspect that continues to require attention for ART networks. However, low execution times and superior solution quality make fuzzy ART a useful addition to the set of tools and techniques now available for group technology and design of cellular manufacturing systems.     

Current status,enablersand barriers of implementing cellular manufacturing system in Indian industries

The cellular manufacturing (CM) has been proved as a well-known manufacturing strategy that helps to improve manufacturing efficiency and productivity by
utilizing the philosophy of group technology. Large number of papers has been published in the area of design issues of CM system. Unfortunately, the issues related to acceptability of CM in Indian industries are typically not examined rigorously as technical issues. This paper presents the results of a survey carried out to find the status, enabler and barrier of implementing CM system in Indian industries.     

A two-phase procedure for duplicating bottleneck machines in a linear layout,cellular manufacturing system

We discuss a two-phase procedure for duplicating bottleneck machines in a cellular manufacturing system. Given a preliminary solution by a clustering technique, the first phase solves a cellular layout problem in which it assigns machine-cells to locations to minimize the total inter-cell material handling costs that result from the bottleneck parts. The purpose of this phase is to find an optimal linear layout of cells. The second phase finds the bottleneck machines that need to be duplicated to minimize the costs. A binary (integer) linear programming model is developed in this phase to minimize the total duplication costs and material handling costs (if not duplicated). Finally, a decision is made as to whether a solution with bottleneck machines, or duplication of bottleneck machines to avoid the bottleneck problem is to be accepted. An example is demonstrated to show how such a bottleneck problem in cellular manufacturing is solved.     

Fixture design information support for integrated design and manufacturing

Although a vast amount of research has been conducted on developing computer-aided fixture design systems, the need for information exchange between the fixture design domain and other manufacturing domains has not been thoroughly dealt with. This paper addresses this gap in fixture design research through the development of appropriate information models for computer-aided fixture design systems to support integrated design and manufacturing. A fixture design activity model is presented that relates fixture design to other design and manufacturing activities. The implementation of the information models in XML and the exchange of the information models based on an XML messaging approach are also discussed.     

A possibilistic approach for designing hybrid cellular manufacturing systems

In this paper, a hybrid cellular manufacturing (HCM) system is presented in which the main sources of uncertainty, e.g. the demands of parts and unit costs are treated as fuzzy numbers in the form of possibilistic distributions. The basic concept of HCM is that high variation in demand might disturb cell efficiency, so forming cells with only those parts that have stable demand, will profit. Thus, to design stable and robust manufacturing cells, a two-phase method is proposed in which a fuzzy adaptive ranking method is first applied to identify those parts with low and non-repetitive demands (i.e. the special parts) which will then be assigned to a functional cell. Afterwards, an interactive possibilistic programming model is applied to cell formation of remaining regular parts while considering both part sequences and multiple routes. To show the capability and usefulness of the proposed method, an illustrative example is also provided. Finally, concluding remarks are reported.     

Virtual cell formation

The concept of a virtual cellular manufacturing system (VCMS), which operates very much like a traditional cellular manufacturing system (CMS), has attracted considerable attention in recent years. Unlike traditional cellular manufacturing systems, virtual cellular manufacturing systems are most suitable in production environments that experience frequent product mix changes. Whereas, in traditional CMS, the shop floor configuration is fixed, in VCMS the shop floor configuration changes in response to changes in product mix over time. Therefore, the life of a given shop floor configuration continues as long as the product mix remains relatively unchanged. Furthermore, whereas in traditional cellular manufacturing systems, a machine cell occupies a contiguous region of the shop floor, the same is not necessarily true with virtual cells. Virtual manufacturing cells are simply logical cells in which the machines belonging to the same cell need not occupy the same contiguous area. Because cell members are logical instead of physical, machines in a cell can be at any location on the floor. In this paper, we present a methodology for designing virtual manufacturing cells.     

Graph-neural network approach in cellular manufacturing on the basis of a binary system

In the past several years, many studies have been carried out on cellular manufacturing. Group technology is a manufacturing philosophy in which similar parts are identified and grouped together to take advantage of their similarities in manufacturing and design. The main problem in the development of cellular manufacturing is that of cell formation. In this paper, a graph-neural network approach is given for cell formation problems in group technology. Effort has been made to develop an algorithm that is more reliable than conventional methods. A graph-neural network has the advantages of fast computation and the ability to handle large scale industrial problems without the assumption of any parameter and the least exceptional elements in the presence of bottleneck machines and/or bottleneck parts. Two examples from the literature have been solved to demonstrate the advantages of the algorithm.     

Manufacturing cell design: an integer programming model employing genetic algorithms

The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.     

A framework for the design of cellular manufacturing systems

A two-stage procedure for the design of a cellular manufacturing system is proposed. The first stage forms the part families. The use of clustering techniques with a new proximity measure is advocated for this stage. The proximity measure uses the manufacturing operations and the operations' sequences. The second stage forms the machine cells. An integer programming model is proposed for this stage. The solution of this model will specify the type and the number of machines in each cell and the assignment of the part families to the cells. The relevance of this approach in the design of flexible manufacturing systems is discussed.     

Framework for designing a flexible cellular assembly system

Modern manufacturing companies have to face competition in a turbulent world market that requires them to improve their operating performances according to increasingly differentiated products with shorter life cycles, low volumes and reduced customer delivery times. The aim of the present paper is to develop a conceptual framework for the simultaneous design and control of a flexible, agile reconfigurable and robust assembly system in conjunction with the analysis and optimization of product, process, system structure, material-handling devices and plant layout. An effective solution for reconfigurability and agility in assembly to order and batch-type systems is a modular semi-automatic approach based on cellular flexible facilities: modularity, automation and human skills are combined to gain the advantages of mass production. Families of parts are produced in flexible cells, i.e. groups of various machines and resources that are physically close together and process one or more families of similar parts. The proposed approach is based on a multistage iterative procedure that integrates different supporting decision techniques and tools such as design for assembly, group technologies and cellular manufacturing. An application to the optimization of a semi-automatic flexible assembly system is illustrated: the system performances of example alternative solutions are presented and compared.     

GT cell formation for minimizing the intercell parts flow

A methodology is proposed to design a GT cell by considering the intercell parts flow in GT cellular manufacturing systems. The problem of GT cell formation is described in a graph using the quantities to be produced in the specified time period and the process routes for producing the products. The objective of this paper is to minimize the total number of parts produced in more than one cell. The problem, formulated as a quadratic assignment problem (QAP), is solved using both Lagrangean relaxation technique and the optimality conditions of quadratic program. Furthermore, in order to obtain the giobal optimal solution rather than the local optimal solution, a branch-and-bound algorithm is employed. Finally, numerical examples are used to show the effectiveness of the solution techniques and GT cell formation procedure. Moreover, a computer simulation is presented, showing the effectiveness of cellular manufacturing systems     

Design for additive manufacturing (DfAM) methodologies: a proposal to foster the design of microwave waveguide components

ABSTRACT

Additive manufacturing offers many advantages, especially in terms of creativity and design freedom. However, this emerging technology is disrupting the way design is carried out and creativity is often limited by the cognitive barriers installed through years of traditional manufacturing processes. Likewise, as this manufacturing process is relatively recent and quite unknown to designers, its specificities are not always considered during the design phase, which leads to manufactured parts happening to differ from CAD models in terms of sizing or surface quality. Consequently, microwave components nowadays manufactured layer-by-layer do not exhibit operational electromagnetic performances. In this way, it is necessary to guide designers throughout the development of a product by drawing their attention to the different steps they must consider in order to design an additive manufactured optimised part.