International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 858 Operation Sequencing and Machining Parameter Selection in CAPP for Cylindrical Part using Hybrid Feature Based Genetic Algorithm and Expert System Abhishek Agrawal 1 , Dr. R.S. Rajput 2 , Dr. Nitin Shrivastava 3 1 Ph.D Scholar, Dept of Mechanical Engg, UIT-RGPV Bhopal, Madhya Pradesh, INDIA 2 Professor, Dept of Mechanical Engg & Director, UIT-RGPV Bhopal, Madhya Pradesh, INDIA 3 Assistant Professor, Dept of Mechanical Engg, UIT-RGPV Bhopal, Madhya Pradesh, INDIA ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Computer-Aided Process Planning (CAPP) is an important interface between Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) in computer- integrated manufacturing environment. Process planning is concerned with the preparation of route sheets that list the sequence of operations and work centers that require producing the product and its components. In any CAPP system, selection of the machining operations sequence is one of the most critical activities for manufacturing a part as per the technical specification and the part drawing. The operation-sequencing problem in process planning is considered to produce a part with the objective of minimizing number of set-ups, maximizing machine utilization and minimizing number of tool changes and a single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives. In general, the problem has combinatorial characteristics and complex precedence relations, it makes the problem more difficult to solve. To overcome these difficulties, by combining Genetic Algorithm (GA) and Expert system (EX) in an appropriate way a new super hybrid genetic algorithms- expert System (S-GENEX) is developed in this research work. The feasible sequences of operations are generated using this hybrid algorithm, based on the inputs precedence cost matrix (PCM). The present work is divided into two phases, the GA and Expert System. During the first phase, GA generates an initial population randomly. Then cross over and mutation operators are imposed for offspring generation based on the initial population. The cross over and mutation sites are selected randomly. This process is repeated for a period of generations for attaining an optimum solution. Expert System is used for selecting the machining parameters for facing, turning and boring operations for three types of materials. A program is developed in C++ based on proposed algorithm, to check its validity and the results are compared with the previous work. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. Key Words: Computer-Aided Process Planning (CAPP); Computer-Aided Manufacturing (CAM); Computer-Aided Design (CAD); Genetic Algorithm; Expert System; Operation Sequencing. 1. INTRODUCTION Today machining process planning has to yield such results that are to give maximum productivity and to ensure economy of manufacturing. Today the market has an ever changing demand for new products, which require shorter development cycle. An important part of the product development cycle is manufacturing process planning. Shorter process planning time can lead to the use of machining parameters that are not optimal and this can lead to the greater cost of production. A human process planner selects proper machining parameters by using not only his own experience and knowledge but also from handbooks of technological requirements, machine tool, cutting tool and selected part material. This manual selection can be slow and does not have to give optimal results. To overcome that problem, machining process planning has gone automated, by the use of Computer-Aided Process Planning (CAPP) system. In addition to operation sequence and machining parameters, the CAPP system should also be able to automatically choose machine and cutting tool while taking in consideration part material. In this paper, the focus is given to cutting parameters optimization. Cutting parameters, such as cutting depth, number of passes, feed rate and machining speed have influence on overall success of machining operation [1,2]. In order to conduct optimization, a mathematical model has to be defined. It is not always easy to define a model that can be expressed by pure analytical functions. Besides, cutting parameters optimization presents a multi-objective optimization problem. So, the classical mathematical methods such as linear programming would not work with such input data. There is also a problem of finding local optimum. In order to overcome these problems, this paper shows the use of Genetic Algorithm (GA) in machining process optimization. GA is a part of the evolutionary algorithms that copy intelligence of nature in order to find global extremities on the given function problem. These algorithms are based on stochastic operations. In nature, only an entity that is able to adapt to its surrounding is going to survive and transfer its qualities to next generations [3,4]. Depending on measuring the quality of entity, the proposed result is kept or deleted. New combined results are then transferred to the