Implementation of Machine Learning Approaches for the Modeling and Predictive Turning Maintenance Operations Incorporating Lubrication and Cooling in Systems of Manufacturing
DOI:
https://doi.org/10.52783/jns.v14.4016Keywords:
Turning, Multi Objectives Optimization, SVR, GPR, polynomial regression, ANNAbstract
The cutting force is a vital parameter in the metal cutting process, which serves as a cornerstone in the production and manufacturing industries for the creation of high-quality products. It is imperative for all Activities related to production and manufacturing to establish A technological advancement, such as a The system for lubrication or cooling is located at the area of cutting during The process of cutting metal. This research Emphasizes the importance of the development of a machine learning algorithm that employs A trio of distinct variations regression techniques: Gaussian process regression (GPR), polynomial regression (PR), and support vector regression (SVR). These methods are intended to forecast reduction in cutting pressure, force, and power by regulating essential Elements such as cutting speed, depth of cut, and feed rate. Furthermore, The process of cooling or lubricating plays a significant role Within the machining phases. Maintaining minimum qualifications for effective operation High-pressure coolant (HPC) and minimum quality lubrication (MQL) are essential. The artificial neural network (ANN) algorithm was utilized to evaluate various parameters, which were optimized for cutting force.
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