Jiang Li, Jiutao Zhao, Qinhui Liu^*, Laizheng Zhu, Jinyi Guo, Weijiu Zhang

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 223-244, 2023, DOI:10.32604/cmc.2023.042429

Abstract Cutting parameters have a significant impact on the machining effect. In order to reduce the machining time and improve the machining quality, this paper proposes an optimization algorithm based on Bp neural network-Improved Multi-Objective Particle Swarm (Bp-DWMOPSO). Firstly, this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm. Secondly, the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established. Finally, the Bp-DWMOPSO algorithm is designed based on the established models. In order to verify the effectiveness of the algorithm, this paper obtains the required data through equal probability orthogonal experiments on… More >

Abdul Wahab Hashmi¹, Harlal Singh Mali¹, Anoj Meena¹, Mohammad Farukh Hashmi², Neeraj Dhanraj Bokde^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 917-1005, 2023, DOI:10.32604/cmes.2023.021223

Abstract Computer vision provides image-based solutions to inspect and investigate the quality of the surface to be measured. For any components to execute their intended functions and operations, surface quality is considered equally significant to dimensional quality. Surface Roughness (Ra) is a widely recognized measure to evaluate and investigate the surface quality of machined parts. Various conventional methods and approaches to measure the surface roughness are not feasible and appropriate in industries claiming 100% inspection and examination because of the time and efforts involved in performing the measurement. However, Machine vision has emerged as the innovative approach to executing the surface… More >

Yang Yang^{1, *}

CMES-Computer Modeling in Engineering & Sciences, Vol.116, No.3, pp. 365-389, 2018, DOI: 10.31614/cmes.2018.03847

Abstract The selection of machining parameters directly affects the production time, quality, cost, and other process performance measures for multi-pass milling. Optimization of machining parameters is of great significance. However, it is a nonlinear constrained optimization problem, which is very difficult to obtain satisfactory solutions by traditional optimization methods. A new optimization technique combined chaotic operator and imperialist competitive algorithm (ICA) is proposed to solve this problem. The ICA simulates the competition between the empires. It is a population-based meta-heuristic algorithm for unconstrained optimization problems. Imperialist development operator based on chaotic sequence is introduced to improve the local search of ICA,… More >