车削加工参数研究

1 Pub Date : 2024-05-10 DOI:10.46632/jame/3/1/5
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摘要

制造业主要关注如何最大限度地降低产品成本。工艺计划人员必须根据可用资源的情况制定产品的工艺计划。计划中包含所有信息,包括产品的检验和交付日期。根据制造和检验方法的建议,以可承受的成本生产部件。大多数研究工作都集中在使用优化技术选择加工参数,并证明特定部件/操作的加工时间/成本最小化。在这项工作中,将考虑使用刀具命名法来研究加工参数的选择。工作介绍了车削铝合金时,切削深度、进给量和主轴转速这三个最重要的加工参数对表面粗糙度影响的实验研究。在这项研究中,使用了实验设计这一强大的实验设计工具来优化加工参数,从而有效地加工工件。采用 L9 正交阵列实验设计方法和方差分析(ANOVA)来分析加工参数对 MRR 和加工时间的影响。对两种不同等级的铝合金(即 AL 6063 和 AL 6068)进行加工,输入参数为切削深度和速度。输出参数为 MRR 和加工时间。根据结果形成了经验方程,并对优化结果进行了验证。
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Investigation of Machining Parameters for Turning Process
Manufacturing industries mainly concentrates on how to minimize the cost of the products. The process planers have to prepare the process plan of the product based on the availability of the recourses. All the information is available in the plan including inspection and delivery date of the product. Based on the recommendation of the manufacturing and inspection methods, components are produced at affordable cost. Most of the research work concentrated on selection of machining parameters using optimization techniques and also prove that the machining time / cost were minimized for the particular components / operations. In this work, tool nomenclature is to be considered for the investigation for selection of machining parameters. Work presents an experimental investigation of the influence of the three most important machining parameters of depth of cut, feed rate and spindle speed on surface roughness during turning of aluminium alloy. In this study, the design of experiment which is a powerful tool for experimental design is used to optimize the machining parameters for effective machining of the work piece. L9 orthogonal array experimental design method as well as analysis of variance (ANOVA) is used to analyse the influence of machining parameters on MRR & Machining Time. Two different grade of aluminium alloy i.e. AL 6063 & AL 6068 were machined with input parameters of depth of cut and speed. The output parameters are MRR and machining time. Based on the results empirical equations are formed and optimized results are validation .The optimal results are recommended to manufacturing industries.
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