Applying a multi-sensor system to predict and simulate the tool wear using artificial neural networks

Cutting tool wear in machining processes reduces the product surface quality, affects the dimensional and geometrical tolerances, and causes tool breakage during the metal cutting. therefore, online tool wear monitoring is needed to prevent reduction in machining quality. an artificial neural network (ann) model was developed in this study to predict and simulate the tool flank wear. to achieve this aim, an experiment array was provided using full factorial method, and the tests were conducted on a cnc lathe machine tool. vibration amplitude of the cutting tool and cutting forces were considered as criterion variables in monitoring the tool flank wear. for designing the model, the cutting parameters, cutting forces, and vibration amplitude were defined as model inputs, and tool flank wear was selected as an output. the model was also introduced as a simulation block diagram to be used as a useful model in online and automated manufacturing systems. the estimated and measured results were then compared with each other. based on the comparison results, maximum squared error values are under 6 x 10^-14 mm, and r^2 is 1, meaning that the designed model can predict the results with high and reliable accuracy.