Sada S O, Ikpeseni S C
Department of Mechanical & Production Engineering, Faculty of Engineering, Delta State University, Oleh Campus, Nigeria.
Heliyon. 2021 Feb 1;7(2):e06136. doi: 10.1016/j.heliyon.2021.e06136. eCollection 2021 Feb.
In the development of an accurate modeling technique for the design of an efficient machining process, manufacturers must be able to identify the most suitable technique capable of producing a fast and accurate performance. This study evaluates the performance of the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in predicting the machining responses (metal removal rate and tool wear) in an AIS steel turning operation. With data generated from carefully designed machining experimentation, the adequacies of the ANN and ANFIS techniques in modeling and predicting the responses were carefully analyzed and compared. Both techniques displayed excellent abilities in predicting the responses of the machining process. However, a comparison of both techniques indicates that ANN is relatively superior to the ANFIS techniques, considering the accuracy of its results in terms of the prediction errors obtained for the ANN and ANFIS of 6.1% and 11.5% for the MRR and 4.1% and 7.2% for the Tool wear respectively. The coefficient of correlation (R) obtained from the analysis further confirms the preference of the ANN with a maximum value of 92.1% recorded using the ANN compared to that of the ANFIS of 73%. The experiment further reveals that the performance of the ANN technique can yield the most ideal results when the right parameters are employed.
在开发一种用于高效加工工艺设计的精确建模技术时，制造商必须能够识别出能够实现快速且准确性能的最合适技术。本研究评估了人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）模型在预测AIS钢车削加工过程中的加工响应（金属去除率和刀具磨损）方面的性能。利用精心设计的加工实验所生成的数据，对ANN和ANFIS技术在建模和预测响应方面的适用性进行了仔细分析和比较。两种技术在预测加工过程的响应方面均表现出卓越能力。然而，对两种技术的比较表明，考虑到其结果的准确性，就金属去除率（MRR）而言，ANN的预测误差为6.1%，ANFIS为11.5%；就刀具磨损而言，ANN为4.1%，ANFIS为7.2%，ANN相对优于ANFIS技术。分析得出的相关系数（R）进一步证实了对ANN的偏好，使用ANN记录的最大值为92.1%，而ANFIS为73%。实验还表明，当采用正确的参数时，ANN技术的性能能够产生最理想的结果。
