Component tolerance design for minimum quality loss and manufacturing cost

Wu, Chao; Chen, Zhuoning; Tang, Geo-Ry

doi:10.1016/s0166-3615(97)00087-0

Cited by 53 publications

(22 citation statements)

References 7 publications

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“…The design variables are tolerances for individual components, which are subject to the constraints from machine tools' capabilities and functional requirements, and the objective function is the total cost per produced unit [9]. As such, two key issues are (a) specifying the constraint conditions and (b) developing the cost model.…”

Section: Model-based Optimum Methodsmentioning

confidence: 99%

Design Techniques for Quality

Jiang

2015

Springer Series in Reliability Engineering

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Section: Model-based Optimum Methodsmentioning

confidence: 99%

Design Techniques for Quality

Jiang

2015

Springer Series in Reliability Engineering

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“…Lu et al [20] suggest an approach to attain least manufacturing cost and proper assimilability product to obtain an optimal solution. Cheng and Maghsoodloo [21], Wu et al [22] and Huang et al [23] used nonlinear programming method to design the tolerance. Cheng and Tsai [24,25] solved allocation of statistical tolerances with exponential cost function and obtained closed-form solution of optimal tolerances.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Tolerance Allocation in a Complex Assembly Using Evolutionary Algorithms

Kumar¹,

Padmanaban²,

Balamurugan³

2016

Int. j. simul. model.

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Tolerance design is a vital factor which influences product and process development. Further, it determines the manufacturing cost, the functionality and quality of a product. It is evident that optimal tolerance normally leads to produce ample parts, better operation of mechanical systems and excellent assembling. In contrast, tight tolerance leads to increase in manufacturing cost for an assembly. An ideal relationship exists among production cost and operation, while determining the optimum tolerance. Based on this relation a new approach by implementing the Non-traditional techniques: Genetic Algorithm (GA), Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Differential Evolution (DE) for determining the optimum tolerance, zero percentage rejection and manufacturing cost considering the varying quality loss constants for an assembly namely overrunning clutch assembly, is discussed in this paper. From the result obtained, it is evident that, the proposed approach is best suitable for solving problems involving complex assemblies.

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“…Several algebraic functions have been proposed to calculate fixture precision costs, such as reciprocal, reciprocal power, negative exponential and reciprocal squared functions [2]. For tolerance allocation, the reciprocal function and negative exponential function are widely accepted [25]. In this paper, we assume a fixture layout at each station based on the common 3-2-1 locating principle applied in machining processes [26].…”

Section: Definition Of Manufacturing Cost Functions 331 Fixture Prmentioning

confidence: 99%

Process-oriented tolerancing using the extended stream of variation model

Abellán-Nebot

Liu

Romero

2013

Computers in Industry

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Current works on process-oriented tolerancing for multi-station manufacturing processes (MMPs) have been mainly focused on allocating fixture tolerances to ensure part quality specifications at a minimum manufacturing cost. Some works have also included fixture maintenance policies into the tolerance allocation problem since they are related to both manufacturing cost and final part quality. However, there is a lack of incorporation of other factors that lead to increase of manufacturing cost and degrade of product quality, such as cutting-tool wear and machine-tool thermal state. The allocation of the admissible values of these process variables may be critical due to their impact on cutting-tool replacement and quality loss costs. In this paper, the process-oriented tolerancing is expanded based on the recently developed, extended stream of variation (SoV) model, which explicitly represents the influence of machining process variables in the variation propagation along MMPs. In addition, the probability distribution functions (pdf) for some machining process variables are analyzed, and a procedure to derive part quality constraints according to GD&T specifications is also shown. With this modeling capability extension, a complete process-oriented tolerancing can be conducted, reaching a real minimum manufacturing cost. In order to demonstrate the advantage of the proposed methodology over a conventional method, a case study is analyzed in detail.

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Component tolerance design for minimum quality loss and manufacturing cost

Cited by 53 publications

References 7 publications

Design Techniques for Quality

Design Techniques for Quality

Optimal Tolerance Allocation in a Complex Assembly Using Evolutionary Algorithms

Process-oriented tolerancing using the extended stream of variation model

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