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

Computer-Aided Design (Impact Factor: 1.52). 02/2011; 43:207-218. DOI: 10.1016/j.cad.2010.10.001
Source: DBLP

ABSTRACT 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.

  • Source
    [Show abstract] [Hide abstract]
    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
  • Source
    [Show abstract] [Hide abstract]
    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 rules 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. Based on the stacked tolerance, it can be verified with the design tolerance of the assembly. Based on the comparison, designer has to reassign the appropriate tolerances to fulfil the functionality if required. If the stacked tolerance is as per designer requirement, then reallocation of tolerances on individual components should be done. Costs versus tolerance data are available for each component. With optimization technique, the optimized cost has been calculated for the assembly.
    01/2014; 6:284–295. DOI:10.1016/j.mspro.2014.07.036
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Titanium alloys such as Ti–6Al–4V offer biocompatibility, corrosion resistance, and superb mechanical properties and are considered the most important metallic biomaterial for medical applications. However, mechanical machining of titanium alloys is still highly difficult and even more challenging for micro-scale machining such as micro-milling. Severe burr formation and rapid tool wear create significant problems such as poor surface roughness. In order to improve the performance of micro-milling Ti–6Al–4V alloy, this study proposes an integrated method in selecting the toolpath and optimum process parameters which can meet micro-machining requirements and constraints. Controlled micro-end-milling experiments for measuring burr formation and surface roughness, finite element simulations for predicting forces and tool wear, and mathematical modeling and optimization techniques have been utilized for determining optimum toolpath strategy and process parameters. Based on the micro-end-milling tests on a circular thin rib feature, process optimization results are validated and indicate a significant improvement in process performances in terms of minimizing burr formation, maximizing tool life, and surface quality.
    International Journal of Advanced Manufacturing Technology 10/2014; 75(1-4). DOI:10.1007/s00170-014-6102-2 · 1.78 Impact Factor


Available from