DOI 10.1007/s00170-004-2135-2 ORIGINAL ARTICLE Int J Adv Manuf Technol (2005) 27: 136–144 Manish Kumar · Sunil Rajotia Development of a generative CAPP system for axisymmetric components for a job shop environment Received: 10 November 2003 / Accepted: 9 February 2004 / Published online: 12 January 2005  Springer-Verlag London Limited 2005 Abstract Process planning is a function in a manufacturing organization that selects the manufacturing processes and param- eters to be used to transform a part from its initial state to the final form according to the design specifications. It is a bridge be- tween product design and product manufacturing. The activities of process planning include understanding the part specifications or product design data, selection of job material and tool, setup planning, sequencing the operations within a setup, determin- ation of process parameters for each operation, and generation of process sheets. This paper outlines a method to develop a gen- erative computer-aided process planning system for axisymmet- ric components for a job shop environment. A decision support system is used to perform semi-structured tasks such as setup planning and establishing precedence relationship among vari- ous operations. Keywords Computer-aided process planning · Decision support system · Operations sequencing · Setup planning 1 Introduction Process planning is defined as the preparation of a set of in- structions to manufacture a part or to set up an assembly, which will satisfy the necessary functional and design specifications. A process plan includes any or all of the following informa- tion: operation sequence, material specifications, cutting tools, manufacturing methods, processing time, setup details etc. Since a large number of factors and data need to be considered, process M. Kumar (✉) Department of Production & Industrial Engineering, JNV University, Jodhpur – 342011, India E-mail: mkumargupta@rediffmail.com S. Rajotia Department of Mechanical Engineering, JNV University, Jodhpur – 342011, India planning may be a very complex and time-consuming job. Sev- eral people need to participate in developing a process plan since one may not have the broad expertise required. Workers with vast knowledge and understanding of the manufacturing processes have traditionally done the task of carrying out the detailed pro- cess plans. Computer-aided process planning (CAPP) is the way in which most companies are automating their process planning and overcoming the shortage of skilled process planners. A com- puterized process planning system has essentially the following goals [1]: 1. Reduce clerical load of plan preparation on manufacturing engineers and skilled process planners. 2. Optimize existing plans using the best available information on machines, tools, speeds etc. 3. Standardize what are known to be the “best” process plans for families of components within a company, thereby cap- turing the knowledge of the skilled planners. 4. Standardize production times/costs for particular families of components. CAPP systems are generally developed along two ap- proaches – variant and generative. The variant approach makes use of group technology principles for classifying the parts into part families based on their geometric and manufacturing at- tributes. Standard process plans are prepared for a representative part in each part family and stored in a database. Planning for a new part is done by retrieving a process plan for a similar part and making necessary modifications. ACUDATA/UNIVATION, AUTOPLAN, CAPP, COMCAPPV, EXAPT, GETURN, GEN- PLAN, MIAPP, MIPLAN, MITURN, PI-CAPP, RPO, TURN2, XPS-I, ZCAPPS etc. are some of the variant type systems for rotational parts [2]. In the generative approach, a process plan is created from scratch by mapping the part geometry and technical information on to the manufacturing databases using process planning logic stored in a structured format [3–5]. This requires the use of a de- cision support system (DSS). A DSS is an interactive system that provides the users with easy access to decision models in