Simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection

Computer-Aided Design (Impact Factor: 1.8). 02/2011; 43(2):207-218. DOI: 10.1016/j.cad.2010.10.001
Source: DBLP


Tolerance specification is an important part of mechanical design. Design tolerances strongly influence the functional performance and manufacturing cost of a mechanical product. Tighter tolerances normally produce superior components, better performing mechanical systems and good assemblability with assured exchangeability at the assembly line. However, unnecessarily tight tolerances lead to excessive manufacturing costs for a given application. The balancing of performance and manufacturing cost through identification of optimal design tolerances is a major concern in modern design. Traditionally, design tolerances are specified based on the designer’s experience. Computer-aided (or software-based) tolerance synthesis and alternative manufacturing process selection programs allow a designer to verify the relations between all design tolerances to produce a consistent and feasible design. In this paper, a general new methodology using intelligent algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPSO) for simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection is presented. The problem has a multi-criterion character in which 3 objective functions, 3 constraints and 5 variables are considered. The average fitness factor method and normalized weighted objective functions method are separately used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find the computational effort of NSGA-II and MOPSO algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed.

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    • "More than 90% of multi-objective optimization approaches have been based on Pareto-optimality using meta-heuristic techniques [3]. Most meta-heuristic methods have adopted evolutionary algorithms of which population-based properties strengthen a multi-points search toward an optimal solution domain called a Pareto-optimal front (POF), i.e. a set of Pareto-optimal solutions [4] [5] [6]. Pareto-optimality is a state of optimal allocation of resources in which no response can be improved without deteriorating other responses [7]. "
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    ABSTRACT: This study investigates the coupling effects of objective-reduction and preference-ordering schemes on the search efficiency in the evolutionary process of multi-objective optimization. The difficulty in solving a many-objective problem increases with the number of conflicting objectives. Degenerated objective space can enhance the multi-directional search toward the multi-dimensional Pareto-optimal front by eliminating redundant objectives, but it is difficult to capture the true Pareto-relation among objectives in the non-optimal solution domain. Successive linear objective-reduction for the dimensionality-reduction and dynamic goal programming for preference-ordering are developed individually and combined with a multi-objective genetic algorithm in order to reflect the aspiration levels for the essential objectives adaptively during optimization. The performance of the proposed framework is demonstrated in redundant and non-redundant benchmark test problems. The preference-ordering approach induces the non-dominated solutions near the front despite enduring a small loss in diversity of the solutions. The induced solutions facilitate a degeneration of the Pareto-optimal front using successive linear objective-reduction, which updates the set of essential objectives by excluding non-conflicting objectives from the set of total objectives based on a principal component analysis. Salient issues related to real-world problems are discussed based on the results of an oil-field application.
    Applied Soft Computing 06/2015; 35:75-112. DOI:10.1016/j.asoc.2015.06.007 · 2.81 Impact Factor
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    • "Requirements of design sizes, geometrical tolerances (both form and position) and machining allowances are expressed mathematically as constraints for the optimization. Shivkumar et al. [14] presented a general new methodology using intelligent algorithms for simultaneous optimal selection of design and manufacturing tolerances with alternative manufacturing process selection. Mansuy et al. [15] presented an original method that enables to solve problems for the case of serial assembly (stacking) without clearances. "
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    ABSTRACT: Geometric dimensioning and Tolerancing (GDT) constitutes the dominant approach for design and manufacture of mechanical parts that control inevitable dimensional and geometrical deviations within appropriate limits. The stack up of tolerances and their redistribution without hampering the functionality is very important for cost optimization. This paper presents a methodology that aims towards the systematic solution of tolerance stack up problem involving geometric characteristics.Conventional tolerance stack up analysis is usually difficult as it involves numerous rule and conditions. The methodology presented i.e. generic capsule method is straightforward and easy to use for stack up of geometrical tolerances of components and their assembly using graphical approach. In the work presented in this paper, angularity tolerance has been considered for illustration of the methodology. Two approaches viz. Worst Case (WC) and Root Sum Square (RSS) have been used. An example of dovetail mounting mechanism has been taken for purpose of stack up of angularity. This assembly consists of two parts i.e. dovetail male and dovetail female. Tolerance stack up has been done both for the components and their assembly. Need for computerisation of methodology for geometrical tolerance stack up of large assemblies has emerged out as the limitation of the proposed method.
    Procedia Engineering 12/2014; 69. DOI:10.1016/j.proeng.2014.03.075
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    • "This paper adopts the solution spread measure (SSM), ratio of nondominated individuals (RNI), and optimizer overhead (OO) to compare the multiobjective solutions, as shown in Table 8 [5, 35]. "
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    ABSTRACT: When an enterprise has thousands of varieties in its inventory, the use of a single management method could not be a feasible approach. A better way to manage this problem would be to categorise inventory items into several clusters according to inventory decisions and to use different management methods for managing different clusters. The present study applies DPSO (dynamic particle swarm optimisation) to a problem of clustering of inventory items. Without the requirement of prior inventory knowledge, inventory items are automatically clustered into near optimal clustering number. The obtained clustering results should satisfy the inventory objective equation, which consists of different objectives such as total cost, backorder rate, demand relevance, and inventory turnover rate. This study integrates the above four objectives into a multiobjective equation, and inputs the actual inventory items of the enterprise into DPSO. In comparison with other clustering methods, the proposed method can consider different objectives and obtain an overall better solution to obtain better convergence results and inventory decisions.
    08/2014; 2014:805879. DOI:10.1155/2014/805879
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