For all machining cutting methods, surface roughness is a parameter that greatly affects the working ability and life of machine elements. Cutting force is a parameter that not only affects the quality of the machining surface but also affects the durability of cutter and the level of energy consumed during machining. Besides, material removal rate (MRR) is a parameter that reflects machining productivity. Workpiece surface machining with small surface roughness, small cutting force and large MRR is desirable of most machining methods. Milling is a popular machining method in the machine building industry. This is considered to be one of the most productive machining methods, capable of machining many different types of surfaces. With the development of the cutting tool and machine tool manufacturing industries, this method is increasingly guaranteed with high precision, sometimes used as the final finishing method. Milling using a face milling cutter is more productive than using a cylindrical cutter because there are multiple cutter s involved at the same time. This article presents a study of multi-objective optimization of milling process using a face milling cutter. The experimental material used in this study is X12M steel. Taguchi method has been applied to design an orthogonal experimental matrix with 27 experiments (L27). In which, five parameters have been selected as the input parameters of the experimental process including insert material, tool nose radius, cutting speed, feed rate and cutting depth. The Reference Ideal Method (RIM) is applied to determine the value of input parameters to ensure minimum surface roughness, minimum cutting force and maximum MRR. Influence of the input parameters on output parameters is also discussed in this study
In machining processes, grinding is often chosen as the final machining method. Grinding is often chosen as the final machining method. This process has many advantages such as high precision and low surface roughness. It depends on many parameters including grinding parameters, dressing parameters and lubrication conditions. In grinding, the surface roughness of a workpiece has a significant influence on quality of the part. This paper presents a study of the grinding surface roughness predictions of workpieces. Based on the previous studies, the study built a relationship between the abrasive grain tip radius and the Standard marking systems of the grinding wheel for conventional and superabrasive grinding wheels (diamond and CBN abrasive). Based on this, the grinding surface roughness was predicted. The proposed model was verified by comparing the predicted and experimental results. Appling the research results, the surface roughness when grinding three types of steel D3, A295M and SAE 420 with Al2O3 and CBN grinding wheels were predicted. The predicted surface roughness values were close to the experimental values, the average deviation between predictive results and experimental results is 15.11 % for the use of Al2O3 grinding wheels and 24.29 % for the case of using CBN grinding wheels. The results of the comparison between the predicted model and the experiment show that the method of surface roughness presented in this study can be used to predict surface roughness in each specific case. The proposed model was verified by comparing the predicted and measured results of surface hardness. This model can be used to predict the surface hardness when surface grinding
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