Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A neural-network approach for an automatic LED inspection system
Expert Systems with Applications: An International Journal
A three-stage integrated approach for assembly sequence planning using neural networks
Expert Systems with Applications: An International Journal
Optimisation of assembly plan through a three-stage integrated approach
International Journal of Computer Applications in Technology
An information criterion for optimal neural network selection
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Using the Taguchi method for effective market segmentation
Expert Systems with Applications: An International Journal
Relationship matrix based automatic assembly sequence generation from a CAD model
Computer-Aided Design
Mechanical assembly planning using ant colony optimization
Computer-Aided Design
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Research in assembly planning can be categorised into three types of approach: graph-based, knowledge-based and artificial intelligence approaches. The main drawbacks of the above approaches are as follows: the first is time-consuming; in the second approach it is difficult to find the optimal solution; and the third approach requires a high computing efficiency. To tackle these problems, this study develops a novel approach integrated with some graph-based heuristic working rules, robust back-propagation neural network (BPNN) engines via Taguchi method and design of experiment (DOE), and a knowledge-based engineering (KBE) system to assist the assembly engineers in promptly predicting a near-optimal assembly sequence. Three real-world examples are dedicated to evaluating the feasibility of the proposed model in terms of the differences in assembly sequences. The results show that the proposed model can efficiently generate BPNN engines, facilitate assembly sequence optimisation and allow the designers to recognise the contact relationships, assembly difficulties and assembly constraints of three-dimensional (3D) components in a virtual environment type.