Neural computing-based design of components for cellular manufacturing

Kusiak, Andrew; Lee, H.

doi:10.1080/00207549608904997

Cited by 29 publications

(6 citation statements)

References 5 publications

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“…Neural networks have also been widely used in computer aided process planning (CAPP), group technology (GT) machine-"part family problems, and cell formation and layout problems [16,26]. Posani and Dagli [30] applied the Hopfield network and BSB (brain state in-a-box, which is a one-layer, auto-associative, nearest-neighbor classifier) to process planning.…”

Section: Review Of Related Workmentioning

confidence: 99%

Soft computing in engineering design: a hybrid dual cross-mapping neural network model

Zha

2005

Neural Comput & Applic

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Contemporary design process requires the development of a new computational intelligence or soft computing methodology that involves intelligence integration and hybrid intelligent systems for design, analysis and evaluation, and optimization. This paper first presents a discussion of the need to incorporate ''intelligence'' into an automated design process and the various constraints that designers face when embarking on industrial design projects. Then, it presents the design problem as optimizing the design output against constraints and the use of soft computing and hybrid intelligent systems techniques. In this paper, a softcomputing-integrated intelligent design framework is developed. A hybrid dual cross-mapping neural network (HDCMNN) model is proposed using the hybrid soft computing technique based on ''cross-mapping'' between a back-propagation network (BPNN) and a recurrent Hopfield network (HNN) for supporting modeling, analysis and evaluation, and optimization tasks in the design process. The two networks perform different but complementary tasks-the BPNN ''decides'' if the design problem is a ''type 0'' (rational) or ''type 1'' (non-rational) problem, and the output layer weights are then used as the energy function for the HNN. The BPNN is used for representing design patterns, training classification boundaries, and outputting network weight values to the HNN, and then the HNN uses the calculated network weight values to evaluate and modify or re-design the design patterns. The developed system provides a unified soft-computingintegrated intelligent design framework with both symbolic and computational intelligence. The system has self-modifying and self-learning functions. Within the system, only one network training is needed for accomplishing the evaluation, rectification/modification, and optimization tasks in the design process. Finally, two case studies are provided to illustrate and validate the developed model and system.

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Section: Review Of Related Workmentioning

confidence: 99%

Soft computing in engineering design: a hybrid dual cross-mapping neural network model

Zha

2005

Neural Comput & Applic

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“…ANNs have been widely used in many classification and optimization situations, where historical data are used to 'train' the network, automatically determining the most appropriate configuration of the hidden layers [11]. Owing to their ability in modelling human associative memory, ANNs are well suited to the application for product design [12] and cellular manufacturing systems [13,14]; for process planning [15]; monitoring and diagnosing [16]; for control [17] and scheduling [18]; for machine fault detection and classification [19]; for bankruptcy prediction [20]; and for modelling manufacturing processes [21]. However, the research area into the application of ANNs to manage supplier knowledge is underdeveloped.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Design of an Intelligent Supplier Knowledge Management System - An Integrative Approach

Choy

Tan

Chan

2007

Proceedings of the Institution of Mechanical Engineers, Part B:

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The drive to cut costs continually and focus on core competencies has driven many companies to outsource some or all of their production. Unlike the past, companies can no longer concentrate only on their own internal business operations, but have to work with customers and suppliers effectively and efficiently. The integration of customer demand and supplier capability to facilitate supplier management using data mining and artificial intelligence technologies has become a promising solution for outsourced-type companies in outsourcing manufacturing operations to suitable suppliers. The result is to form a supply network on which they depend on the provision of products and services. In this paper, a supplier knowledge management system (SKMS) is introduced for such a purpose. By using its hybrid on-line analytical processing (OLAP)/artificial neural networks (ANNs)/case-based reasoning (CBR) approach in predicting future customer demands and allocating suitable suppliers during the order fulfilment process, it is found that the overall efficiency in the whole supply chain is greatly enhanced. A case study using the SKMS to integrate the order subcontracting system of Farnell Newark-InOne (Shanghai) Limited is presented. Through the use of the SKMS, the demand of customers is related to the supplier's capabilities both efficiently and effectively while, at the same time, valuable supplier knowledge is also accumulated by the company.

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“…Several repeated solutions with different initial weights and network parameters are used to converge the optimal solution. There is no general framework to select the optimum NN architecture and its parameters (Chung and Kusiak, 1994;Goh, 1995;Kusiak and Lee, 1996;Yoon et al, 1993;Zhang et al, 1996). Although some recent research work has contributed to Use of genetic algorithm determine the number of hidden layers, the number of neurons in each layer and selecting the learning rate parameters, the results are still not at satisfactory level to be accepted as general rules for generating optimal NN architecture.…”

Section: Nn Architecturementioning

confidence: 99%

Use of genetic algorithm to design optimal neural network structure

Öztürk

2003

Engineering Computations

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In this research, neural network (NN) and genetic algorithm (GA) are used together to design optimal NN structure. The proposed approach combines the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications in design and manufacturing. Genetic input selection approach is introduced to obtain optimal NN topology. Experimental results are given to evaluate the performance of the proposed system.

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Neural computing-based design of components for cellular manufacturing

Cited by 29 publications

References 5 publications

Soft computing in engineering design: a hybrid dual cross-mapping neural network model

Soft computing in engineering design: a hybrid dual cross-mapping neural network model

Design of an Intelligent Supplier Knowledge Management System - An Integrative Approach

Use of genetic algorithm to design optimal neural network structure

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