Efficiency Optimization for Disassembly Tools via Using NN-GA Approach
Efficiency Optimization for Disassembly Tools via Using NN-GA Approach
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DOI：
10.1155/2013/173736
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发表时间：
2013-11
影响因子：
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通讯作者：
Guangdong Tian;Tian Qiang;Jiangwei Chu;Guan Xu;W. Zhou
中科院分区：
文献类型：
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作者：
Guangdong Tian;Tian Qiang;Jiangwei Chu;Guan Xu;W. Zhou
在当今可持续发展的背景下，拆卸问题受到了广泛的关注。其中之一是拆卸工具的选择和效率比较。针对这一问题，本文以螺栓为移除对象，考虑影响移除过程的因素，设计了不同移除工具的移除实验。在实验数据的基础上，采用神经网络（NN）和遗传算法（GA）相结合的混合算法对不同去除工具的去除效率进行优化。讨论了它们的效率比较。数值算例说明了所提思想和方法的有效性。
Disassembly issues have been widely attracted in today’s sustainable development context. One of them is the selection of disassembly tools and their efficiency comparison. To deal with such issue, taking the bolt as a removal object, this work designs their removal experiments for different removal tools considering some factors influencing its removal process. Moreover, based on the obtained experimental data, the removal efficiency for different removal tools is optimized by a hybrid algorithm integrating neural networks (NN) and genetic algorithm (GA). Their efficiency comparison is discussed. Some numerical examples are given to illustrate the proposed idea and the effectiveness of the proposed methods.