Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool

作     者：Thella Babu Rao A.Gopala Krishna Ramesh Kumar Katta Konjeti Rama Krishna 

作者机构：Department of Mechanical EngineeringGITAM UniversityHyderabad 502329Andhra PradeshIndia Department of Mechanical EngineeringUniversity College of EngineeringJNTUKKakinada 533003Andhra PradeshIndia Productionisation & Technology TransferDefence R&D LaboratoryKanchanbaghHyderabad 500058Andhra PradeshIndia 

出 版 物：《Advances in Manufacturing》 (先进制造进展（英文版）)

年 卷 期：2015年第3卷第1期

页      面：84-95页

核心收录：

学科分类：080503[工学-材料加工工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

主　　题：Self propelled rotary turning ~ Empiricalmodeling ~ Response surface methodology (RSM)   Multi objective formulation   Optimization   Non dominatedsorting genetic algorithm II (NSGA II) 

摘      要：There are many advanced tooling approaches in metal cutting to enhance the cutting tool performance for machining hard-to-cut materials. The self propelled rotary tool （SPRT） is one of the novel approaches to improve the cutting tool performance by providing cutting edge in the form of a disk, which rotates about its principal axis and provides a rest period for the cutting edge to cool and allow engaging a fresh cutting edge with the work piece. This paper aimed to present the cutting performance of SPRT while turning hardened EN24 steel and optimize the machining conditions. Surface roughness （Ra） and metal removal rate （rMMR） are considered as machining perfor- mance parameters to evaluate, while the horizontal incli- nation angle of the SPRT, depth of cut, feed rate and spindle speed are considered as process variables. Initially, design of experiments （DOEs） is employed to minimize the number of experiments. For each set of chosen process variables, the machining experiments are conducted on computer numerical control （CNC） lathe to measure the machining responses. Then, the response surface method- ology （RSM） is used to establish quantitative relationships for the output responses in terms of the input variables. Analysis of variance （ANOVA） is used to check the adequacy of the model. The influence of input variables on the output responses is also determined. Consequently, these models are formulated as a multi-response optimi- zation problem to minimize the Ra and maximize the rMMR simultaneously. Non-dominated sorting genetic algorithm-II （NSGA-II） is used to derive the set of Pareto-optimal solutions. The optimal results obtained through the pro- posed methodology are also compared with the results of validation experimental runs and good correlation is found between them.