Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach

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@Article{Garg:2015:JCP,

• author = "Akhil1 Garg and Jasmine Siu Lee Lam and L. Gao",
• title = "Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach",
• journal = "Journal of Cleaner Production",
• volume = "108, Part A",
• pages = "34--45",
• year = "2015",
• ISSN = "0959-6526",
• DOI = "doi:10.1016/j.jclepro.2015.06.043",
• URL = "http://www.sciencedirect.com/science/article/pii/S0959652615007726",
• abstract = "From the perspective of energy conservation, the notion of modelling of energy consumption as a vital element of environmental sustainability in any manufacturing industry remains a current and important focus of study for climate change experts across the globe. Among the manufacturing operations, machining is widely performed. Extensive studies by peer researchers reveal that the focus was on modelling and optimizing the manufacturing aspects (e.g. surface roughness, tool wear rate, dimensional accuracy) of the machining operations by computational intelligence methods such as analysis of variance, grey relational analysis, Taguchi method, and artificial neural network. Alternatively, an evolutionary based multi-gene genetic programming approach can be applied but its effective functioning depends on the complexity measure chosen in its fitness function. This study proposes a new complexity-based multi-gene genetic programming approach based on orthogonal basis functions and compares its performance to that of the standardized multi-gene genetic programming in modelling of energy consumption of the milling process. The hidden relationships between the energy consumption and the input process parameters are unveiled by conducting sensitivity and parametric analysis. From these relationships, an optimum set of input settings can be obtained which will conserve greater amount of energy from these operations. It was found that the cutting speed has the highest impact on the milling process followed by feed rate and depth of cut.",
• keywords = "genetic algorithms, genetic programming, Environmental sustainability, Energy conservation, Energy consumption, Machining, Computational intelligence, Milling process",

}

Genetic Programming entries for Akhil Garg Jasmine Siu Lee Lam Liang Gao

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