Cubic Boron Nitride (CBN) and TiN Composite Coated CBN combines the thermal stability, super abrasiveness and cost effectiveness for hard machining applications. This paper reports the results of a study addressing wear performance of these CBN and TiN based coated CBN inserts (SNMN090308) for face milling of 61 HRC hardened 90MnCrV8 tool steel. Machining test conditions are obtained after dynamic stability simulation of cutting tools and machine tools. The tool wear and cutting forces are also analyzed and the results are presented.
Additive Manufacturing (AM) methods, in the field of production, are increasing rapidly. In particular, the use of Ti alloys has an important role in AM methods. The major disadvantage of AM methods is low surface quality of the manufactured parts. Therefore, parts produced using AM methods need subsequent surface treatment. Electro Discharge Machining (EDM) is one of the nontraditional machining methods, which can be used to improve the surface quality with appropriate parameters. In this study, EDM was investigated to improve the surface quality of sintered Ti–6Al–4V alloy by using 160 different finish parameters. It was observed that Current (I) has a notable effect on surface roughness showing that best surface quality is achieved with low current values, which is under Ra = 2 µm. To estimate the final result of EDM, roughness values obtained from the experiments were modeled by using the Genetic Expression Programming (GEP), and a mathematical relationship between the obtained roughness values and EDM parameters was proposed. As a result of 830,900 iterations, GEP model created can estimate the final surface roughness of the parts with 84% accuracy.
Hard milling is a very common used machining procedure in the last years. Therefore the prediction of cutting forces is important. The paper deals with this prediction using genetic evolutionary programming (GEP) approach to set mathematical expression for out cutting forces. In this study, face milling was performed using DIN1.2842 (90MnCrV8) cold work tool steel, with a hardness of 61 HRC. Experimental parameters were selected using stability measurements and simulations. In the hard milling experiments, cutting force data in a total of three axes were collected. Feed direction (F x ) and tangential direction (F y ) cutting forces generated using genetic evolutionary programming were modelled. Cutting speed and feed rate values were treated as inputs in the models, and average cutting force values as output. Mathematical expressions were created to predict average F x and F y forces that can be generated in hard material milling.
In this study, the vanadium carbide coating in molten borax bath process is modeled by evolutionary genetic programming (GEP) with bath composition (borax percentage, ferro vanadium (Fe-V) percentage, boric acid percentage), bath temperature, immersion time, and layer thickness data. Five inputs and one output data exist in the model. The percentage of borax, Fe-V, and boric acid, temperature, and immersion time parameters are used as input data and the layer thickness value is used as output data. For selected bath components, immersion time, and temperature variables, the layer thicknesses are derived from the mathematical expression. The results of the mathematical expressions are compared to that of experimental data; it is determined that the derived mathematical expression has an accuracy of 89%.
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