Sensitivity-based conceptual design and tolerance allocation using the continuous ants colony algorithm (CACO)

Abstract

In an assembly, there are two ways to control the deviation of critical dimensions. One is by keeping the deviation of the critical dimension small by tightening manufacturing tolerances and controlling aging and environmental effects. This approach is traditional and expensive, as it requires tighter manufacturing tolerances and protection from aging and the environment. The second is by moving the nominal values of the non-critical dimensions to a less sensitive portion. This approach is very helpful in improving the quality with no additional cost. One can analyze any number of designs very early in the concept development stage of a project. After the concept design the cost-based optimal tolerances for the corresponding dimensions are allocated. The continuous ants colony algorithm, a kind of meta-heuristic approach, is used as an optimization tool for minimizing the critical dimension deviation and allocating the costbased optimal tolerances.

Optimal Tolerance Allocation in a Complex Assembly Using Evolutionary Algorithms

“…Prabhaharan et al [12] introduced the non-traditional approach to find the optimum tolerance and to overcome the traditional tolerance approach. Prabhaharan et al [13] imported a metaheuristic approach as an ant colony algorithm, to simultaneously allocate the tolerance and manufacturing cost. Krishna and Rao [14] incorporated an approach to reduce the cost of manufacturing.…”

A geometric accuracy design method of multi-axis NC machine tool for improving machining accuracy reliability

“…In 2005 Prabhaharan et al introduced a kind of metaheuristic approach as an optimization tool for minimizing the critical dimension deviation and allocating the costbased optimal tolerances [29]. In 2006 Huang and Shiau obtained the optimized tolerance allocation of a sliding vane rotary compressor's components for the required reliability with the minimum cost and quality loss [16].…”

Multi-contextual ant colony optimization of intermediate dynamic job shop problems

“…Prabhaharan, Asokan, and Rajendran [25] devised a continuous ant colony algorithm (CACO) that divides the problem domain into randomly distributed regions and uses both a local pheromone-based search and global searches that incorporate GA operators; the ratio between local and global searching was an important parameter.…”

“…Their local search dealt with the ages and pheromone levels of specific regions; their global search created new regions. Similarly, Prabharan, Asokan, and Rajendran [25] described a local search for their ants that altered both the pheromone levels and the ages of specific regions. They applied this to optimize costs and critical deviations for multi-objective problems.…”