Optimization of Conveyor Mounting Plate Design Using Machine Learning and Genetic Algorithm

In this paper, a procedure of building a conveyor mounting plate using the machine learning (ML) and genetic algorithms is explained. A dataset has been created to come up with predictive models that take into account material properties, geometric parameters and performance measures including weight, stress and deflection. Using the assistance of Random Forest Regression, design of experiments is used to train the models of pre and post input indicators of the performance of the design. A Genetic Algorithm(GA) is used to provide this compromise whereby it tries to reduce weight, stress and deflection all of which are contradictory. The outcomes are illustrated in a Pareto front format which presents the optimal designs.