Machining is one of the most widely used manufacturing processes in the mold industry and which affects the manufacturing cost significantly. Particularly, the desired surface roughness/quality at a low cost at minimum machining time is the ultimate target. Surface quality depends on many parameters such as cutting speed, feed, depth of cut, vibration, coolant, insert properties/geometry used. In this study, surface roughnesses after turning of hot work tool steel at different parameters are investigated. At the same time, regression, artificial neural network, and fuzzy logic prediction models are developed from the experimental data. Therefore, surface roughness values at the different parameters are determined. The closest estimate with approximately 5% error is obtained by the Sugeno fuzzy logic model when it compared to experimental results.

The tensile test is one of the most basic and simple tests in which the material is pulled in a single axis until it breaks and allow us to recognize the material from the data obtained from it. While recognizing materials, their behavior under different temperatures and strain rates is also important. Especially in the manufacturing industry, there are many different production and shaping methods, and each has its own characteristics. For example, in the hot deep drawing process, the mechanical properties of the material can be determined by hot tensile tests. At the same time, this situation has become more important with the development of finite element analysis programs. Because modeling under the same conditions is very effective on the accuracy of the results. In this study, the effects of temperature and strain rate on tensile properties are investigated in steel, titanium, aluminum and nickel alloys. In the examinations, it is seen that the change of the temperature and strain rate for these materials have a great effect on the stress and ductility.

Machining is one of the most widely used manufacturing processes in the mold industry and which affects the manufacturing cost significantly. Particularly, the desired surface roughness/quality at a low cost at minimum machining time is the ultimate target. Surface quality depends on many parameters such as cutting speed, feed, depth of cut, vibration, coolant, insert properties/geometry used. In this study, surface roughnesses after turning of hot work tool steel at different parameters are investigated. At the same time, regression, artificial neural network, and fuzzy logic prediction models are developed from the experimental data. Therefore, surface roughness values at the different parameters are determined. The closest estimate with approximately 5% error is obtained by the Sugeno fuzzy logic model when it compared to experimental results.