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Machining of hard workpieces is one of the most important challenges of the manufacturing industry. Hence, new methods were added to traditional machining. Ultrasonic vibration machining is one of these methods. The advantages of using ultrasonic vibrations compared to traditional machining include reducing machining forces, reducing tool wear and friction, increasing tool life, creating intermittent cutting conditions, increasing surface quality, and so on. To vibrate the tool, a horn with a resonant frequency of 20,633 Hz was analyzed by Abacus software. In this study, the effects of cutting speed, feed rate, conventional machining conditions, and vibration machining conditions at three different hardness of 15, 30, and 45 Rockwell C for the workpiece on surface roughness and tool wear were evaluated. The experiments were designed at full factorial, and a total of 54 experiments were performed. The results showed that at higher workpiece hardness by applying vibration the surface roughness was reduced. The surface roughness (Ra) in machining by means of ultrasonic vibrations is up to about 36% less than conventional machining in various machining parameters. In addition, the temperature in vibration machining is lower about 15% at higher stiffness of the workpiece. Also, with the increase in the hardness of the workpiece, the tool wear was increased, which is less by applying ultrasonic vibrations. Also, by applying vibrations, tool wear was reduced in total, which can be minimized by selecting cermet tools and applying vibrations in 4140 AISI steel machining.

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The ability to predict tool wear during machining is a very important part of diagnosis, which makes it possible to replace the tool at the appropriate time. Therefore, in this research, the artificial neural network approach was used to predict tool wear. First, hardened steel 4140 was turned with uncoated cemented carbide tool TCMW 16T304 H13A and with input parameters including cutting speed, feed rate and machining time in three different levels and with constant cutting depth, and the amount of tool wear was measured. And the experimental test results were used to train and validate the artificial neural network. The optimal neural network architecture was obtained with 3 nodes in the input layer, two hidden layers with 12 and 36 nodes in the first and second hidden layers, and 1 node in the output layer to predict tool wear. The prediction values of the artificial neural network model were compared with the experimental results and the average error percentage of the validation data was calculated as 3.32%.

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When working with hardened materials, it's important to control and optimize the surface roughness and machining force. To achieve this, we can use intelligent methods that are based on prediction and optimization models. In this study, an artificial neural network was used to evaluate the surface roughness and machining force of hardened steel 4140 by analyzing cutting speed, feed rate, and machining time. A full factorial method was used to carry out 27 experiments, and an uncoated cemented carbide tool TCMW 16T304 H13A was used to measure surface roughness and machining force during turning. An artificial neural network model with two hidden layers was selected as the optimal architecture for separately predicting surface roughness and machining force. The predicted values were then compared with the experimental results, and the average error percentage for validation data was calculated as 4.25% for surface roughness and 5.11% for machining force. Finally, the optimal cutting parameters were selected to minimize surface roughness and machining force.