Influence factor analysis and prediction models for component removal time in manufacturing

Abstract

Disassembly issues are important in the sustainable manufacturing field. One of them is influence factor analysis and time prediction of product disassembly. To deal with such issue, taking the bolt as a removal object, this work designs its removal experiment considering some factors influencing its removal process. Moreover, factor analysis and ranking on removal time are performed by a grey relational analysis method. In addition, based on the analysed and obtained main factors on removal time, its prediction models are established by multiple linear regressions, artificial neural networks and optimized artificial neural networks based on genetic algorithm methods. A numerical example is given to illustrate the proposed models and the effectiveness of the proposed methods.

A Scatter Search Approach for Multiobjective Selective Disassembly Sequence Problem

“…Disassembly sequence planning is an essential procedure of EOL products recovery which directly affects the effectiveness of maintenance and remanufacturing process in reverse logistics [35,36]. This work addresses an intelligent selective disassembly approach based on scatter search algorithms for the first time.…”

Reliability Modeling and Assessment of Electric Vehicle Motor Using Fault Tree and Fuzzy Petri Nets

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