Evaluating performance of cutting machines during sawing dimension stones

Abstract

The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.

摘要

切割机的能耗和振动性能直接影响到生产成本。本研究的目的是通过系统的实验研究, 使用混 合智能模型来评估切削机床的性能。建立了一个关于碳酸盐岩和花岗岩的数据库, 确定极限抗压强度、 弹性模量、莫氏硬度、Schmiazek 磨耗系数等物理力学参数和切削深度、进给速度等操作参数为输入 参数。将多层感知器人工神经网络与遗传算法(GANN-BP)、支持向量回归法与布谷鸟优化算法 (SCA-SVR)相结合, 建立预测模型。结果表明, 所建立的GANN-BP 模型与COA-SVR 模型性能相近, 与实测值吻合较好。这些结果也表明, 这些模型是评价切削机床性能的合适工具。