Optimization of fixture locating layout design using comprehensive optimized machine learning

Fixtures are commonly employed in production as work holding devices that keep the workpiece immobilized while machined. The workpiece’s deformation, which affects machining precision, is greatly influenced by the positioning of fixture elements around the workpiece. By positioning the locators and clamps appropriately, the workpiece’s deformation might be decreased. Therefore, it is required to model the fixture–workpiece system’s complicated behavioral relationship. In this study, long short-term memory (LSTM), multilayer perception (MLP), and adaptive neuro-fuzzy inference system (ANFIS) are three machine-learning approaches employed to model the connection between locator and clamp positions and maximum workpiece deformation throughout end milling. The hyperparameters of the developed ANFIS, MLP, and LSTM are chosen using the evolutionary algorithms, including genetic algorithm (GA), particle swarm optimization (PSO), butterfly optimization algorithm (BOA), grey wolf optimization (GWO), and wolf optimization algorithm (WOA). Among developed methods, MLP optimized using BOA (BOA-MLP) reached the highest accuracy among all developed models in predicting the response surface. The developed model had a lower computational load than the final element model in calculating the response surface during the machining process. At the final step, the prementioned five evolutionary algorithms were implemented in the developed BOA-MLP to extract the optimal parameters of the fixture to decrease the deflection of the workpiece throughout the machining. The proposed method was modeled in MATLAB. The outcomes showed that the mentioned model was efficient enough compared with the previous method, such as optimized response surface methodology in the point view of 0.0441 μm lower workpiece deflection.