Concurrent optimization of machining process parameters and tolerance allocation

• Published in: International journal of advanced manufacturing technology Vol. 51; no. 1-4; pp. 357 - 369
• Main Authors: Janakiraman, V., Saravanan, R.
• Format: Journal Article
• Language: English
• Published: London Springer-Verlag 01-11-2010; Springer Nature B.V
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Cutting force; Cylinders; Design of experiments; Engineering; Genetic algorithms; Industrial and Production Engineering; Machining; Manufacturing; Mathematical models; Mechanical Engineering; Media Management; Model accuracy; Optimization; Original Article; Process parameters; Production costs; Genetic algorithm (GA); Quality loss function; Tolerance allocation; Design of experiments (DoE); Optimization
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-010-2602-x

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power.