Study On Surface Roughness Experiment In The Interrupted Turning Of Hardened Tool Steel Cr12MoV With PCBN Cutting Tools

Surface roughness is one of the important technical indicators to characterize the surface quality. Performance of the products, in particular its reliability and durability, depends largely on the quality of the surface layer of the parts. When the hardened molds or other mechanical products with high hardness and high wear resistance that have intermittent surface, to some extent, its surface roughness is affected by the cutting parameters, tool types, work-piece hardness, processing methods and so on. As surface quality have a direct relationship with the products’character of service, so it becomes a key concern in the area of machining.Currently, national experts and scholars’studying about the surface roughness are focused on continuous cutting process. The studying of the main factors of the surface roughness in the intermittent cutting and the prediction models of surface roughness are still relatively rare. Because the factors that affect the process of intermittent cutting are numerous and complex, and the intermittent cutting is widely used in practical production, to studying on the hardened steel’s surface roughness in intermittent cutting has theoretical and practical significance, for the purpose to establish reasonable process procedures that can improve the surface quality and processing efficiency of parts.In this paper, the polycrystalline cubic boron nitride (PCBN) tool was to machine the hardened tool steel Cr12MoV. Main contents and research results are as follows:(1) It uses the single-factor method to study the main factors affecting the surface roughness in the process of intermittent turning. It discusses in details the work-piece material, tool type, cutting parameters, intermittent intensity, etc. and obtains the impact trends of these factors.(2) Experiments designed with orthogonal test are operated. According to the results, it conducts an analysis of range and variance and a test of significance. In the final, it gains the best combination of cutting parameters and confidence intervals under such conditions, which strongly and intermittently turn the hardened steel Cr12MoV with a hardness of HRC60.Also gets the main factors affecting the surface roughness with its primary and secondary order.(3) Based on the test results obtained by the orthogonal experiment, it establishes a predictive model of surface roughness by using multiple regression analysis and RBF neural network algorithm and the model is verified by the practically experimental result.Studies have shown that the cutting parameters are still the main factors that affect the surface roughness in intermittent cutting of the hardened steel. The prediction model for surface roughness which is built by RBF neural network algorithm is more accurate than established by the multiple regression analysis, which is proven by the real experiment.