Robust Tolerance Design With the Integer Programming Approach

Feng, Cheng; Kusiak, Andrew

doi:10.1115/1.2831193

Cited by 59 publications

(27 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study it is referred as the quality loss function (Feng and Kusiak 1997). This loss function is a quadratic expression for measuring the cost in terms of financial loss due to product failure in the eyes of the consumers.…”

Section: Quality Loss Functionmentioning

confidence: 99%

Multiobjective Optimization of Design and Manufacturing Tolerances Using TLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

View full text Add to dashboard Cite

Tolerance design has become a very responsive and key issue in product and process development because of an informal compromise between functionality, quality, and manufacturing cost. The problem formulation becomes more complex with simultaneous selection of design and manufacturing tolerances and the optimization problem is difficult to solve with the traditional optimization techniques. Rao and More (Prod Manuf Res Open Access J 2: 71-94, 2014) used TLBO algorithm for the optimal selection of design and manufacturing tolerances with an alternative manufacturing process to obtain the optimal solution. An example of knuckle joint assembly with three arms is presented in this chapter and it is found that the TLBO algorithm has produced better results when compared to those obtained using NSGA-II and MOPSO algorithms.

show abstract

Section: Quality Loss Functionmentioning

confidence: 99%

Multiobjective Optimization of Design and Manufacturing Tolerances Using TLBO Algorithm

Rao

2015

Teaching Learning Based Optimization Algorithm

View full text Add to dashboard Cite

show abstract

“…Feng and Kusiak [21] considered quality loss in addition to manufacturing cost in a discrete cost function. Jeang [6,22] developed a few general mathematical models to determine product tolerances minimizing the combined manufacturing costs and quality losses without considering the manufacturing processes, in a continuous cost function using quadratic and geometrical decay functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated total cost and Tolerance Optimization with Genetic Algorithm

Kumar¹,

Alagumurthi²

2010

IJCIS

View full text Add to dashboard Cite

The tolerance allocation problem has been studied in the literature for decades, usually using mathematical programming (or) heuristic optimization approaches. Elegant tools for minimum cost tolerance allocation have been developed over several decades but still there is no specified tool to find the total cost with respect to machining cost and asymmetric quality loss functions. Objective of this paper is to find the optimized total cost by considering the machining cost and the asymmetric quality loss of an assembly. The non linear multi variable optimization of total cost problem formulated is solved using genetic algorithm. In this work, Piston and cylinder arrangement example was considered for concurrent allocation of design and machining tolerance, as well as to optimize the total cost. The result shows, optimized tolerance will enhances the quality and reduces the total cost

show abstract

“…The overrunning clutch model was proposed by Feng and Kusiak [13]. The overrunning clutch given in Fig.…”

Section: Optimum Tolerances For Overrunning Clutchmentioning

confidence: 99%

“…The following manufacturing cost functions are found in the literature [13] Manufacturing cost for single side tolerance values for…”

Section: Objective Functionmentioning

confidence: 99%

Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Haq

Karthikeyan

Sivakumar³

et al. 2005

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Nowadays tolerance optimization is increasingly becoming an important tool for manufacturing and mechanical design. This seemingly, arbitrary task of assigning dimension tolerance can have a large effect on the cost and performance of manufactured products. With the increase in competition in today's market place, small savings in cost or small increase in performance may determine the success of a product. In practical applications, tolerances are most often assigned as informal compromises between functional quality and manufacturing cost. Frequently the compromise is obtained interactively by trial and error. A more scientific approach is often desirable for better performance. In this paper particle swarm optimization (PSO) is used for the optimal machining tolerance allocation of over running clutch assembly to obtain the global optimal solution. The objective is to obtain optimum tolerances of the individual components for the minimum cost of manufacturing. The result obtained by PSO is compared with the geometric programming (GP) and genetic algorithm (GA) and the performance of the result are analyzed.

show abstract

Robust Tolerance Design With the Integer Programming Approach

Cited by 59 publications

References 18 publications

Multiobjective Optimization of Design and Manufacturing Tolerances Using TLBO Algorithm

Multiobjective Optimization of Design and Manufacturing Tolerances Using TLBO Algorithm

Integrated total cost and Tolerance Optimization with Genetic Algorithm

Particle swarm optimization (PSO) algorithm for optimal machining allocation of clutch assembly

Contact Info

Product

Resources

About