Modeling and impact factors analyzing of energy consumption in CNC face milling using GRASP gene expression programming.

Currently, sustainable manufacturing (SM) attracts more and more attentions due to the increasing environmental pollution and energy shortage threat. Energy consumption is a fundamental element of SM for its valuable effect in the environmental impacts and business opportunities. Analyzing the relationship between process parameters and energy consumption is helpful to reduce production costs, eliminate negative environmental impacts, and increase business opportunities. Since energy consumption is impacted by the inherent uncertainties in the machining process, how to model energy consumption presents a significant challenge. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm and Genetic Programming, and has been successfully applied in formula finding. In this paper, a Greedy Randomized Adaptive Search Procedure (GRASP)-based Gene Expression Programming, named GGEP, is proposed to predict the face milling energy consumption. In this proposed GGEP approach, a GRASP-based learning mechanism and an iterative re-start mechanism have been introduced into the basic GEP. At the basis of defining a GGEP environment for the energy consumption prediction, an explicit model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The analysis of experiment results reveals that the proposed approach models and predicts the energy consumption with high accuracy and high-speed convergence. Moreover, in order to better study the mechanism of machining, the influence and contribution of different impact factors on energy consumption in face milling are analyzed. [ABSTRACT FROM AUTHOR]