Abstract
Wear loss prediction is still essential in various industrial applications particularly the cutting tools. This process is quite sophisticated due to the relation between the interrelated variables. In this work, a genetic programming optimization model for predicting and optimizing the quantities of adhesive wear in low and medium carbon steel was generated. Carbon steel material was subjected to dry sliding wear experiments using a pin-on-disc module. Several parameters including the applied load, sliding speed and time were involved in the model. The proposed model was capable of predicting and optimizing the wear loss in carbon steel and was evaluated and tested using different performance criteria to ensure its reliability. The generated model can be utilized to monitor wear in mechanical components without requiring any human efforts to enhance the monitoring efficiency and reduce human errors.
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Faris, R., Almasri, B., Faris, H., AL-Oqla, F.M., Dalalah, D. (2020). Evolving Genetic Programming Models for Predicting Quantities of Adhesive Wear in Low and Medium Carbon Steel. In: Mirjalili, S., Faris, H., Aljarah, I. (eds) Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-32-9990-0_7
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