Genetic algorithm-based form error evaluation

Abstract

Form error evaluation of geometrical products is a nonlinear optimization problem, for which a solution has been attempted by different methods with some complexity. A genetic algorithm (GA) was developed to deal with the problem, which was proved simple to understand and realize, and its key techniques have been investigated in detail. Firstly, the fitness function of GA was discussed emphatically as a bridge between GA and the concrete problems to be solved. Secondly, the real numbers-based representation of the desired solutions in the continual space optimization problem was discussed. Thirdly, many improved evolutionary strategies of GA were described on emphasis. These evolutionary strategies were the selection operation of ‘odd number selection plus roulette wheel selection’, the crossover operation of ‘arithmetic crossover between near relatives and far relatives’ and the mutation operation of ‘adaptive Gaussian’ mutation. After evolutions from generation to generation with the evolutionary strategies, the initial population produced stochastically around the least-squared solutions of the problem would be updated and improved iteratively till the best chromosome or individual of GA appeared. Finally, some examples were given to verify the evolutionary method. Experimental results show that the GA-based method can find desired solutions that are superior to the least-squared solutions except for a few examples in which the GA-based method can obtain similar results to those by the least-squared method. Compared with other optimization techniques, the GA-based method can obtain almost equal results but with less complicated models and computation time.

Evaluating Geometric Characteristics of Planar Surfaces using Improved Particle Swarm Optimization

“…This will further help in minimizing the economic loss occurring in manufacturing of the part. The result shows that MPSO algorithm has higher computational accuracy and its optimization result surpassed those from the other methods [3], [8], [29] and from LSM. The iterative curve for PSO and MPSO is shown in Fig.4.a), Fig.4.b) confirming better performance and efficiency of the proposed MPSO algorithm.…”

“…Table 2 shows the results presented in literature [30] along with the solution provided by the proposed MPSO algorithm. For example 1, it is observed that minimum zone straightness error obtained by LSM is 0.0017, Optimization Technique Zone (OTZ) [8] is 0.0017, Linear Approximation Technique (LAT) [8] is 0.0017, GA [3] is 0.001672, and PSO [29] is 0.001711, while the minimum zone straightness error obtained by the proposed MPSO is 0.00160. If the allowable straightness tolerance is 0.00165 inch, all the algorithms except the MPSO algorithm overestimate the tolerances and hence result in rejection of good parts.…”

“…On the contrary, some of the nature inspired optimization algorithms have been used for form tolerance evaluation and they include genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), and artificial bee colony (ABC) [14]- [17]. GA was to be more complex than PSO in the principle for the same work [3], [18]. ACO is time consuming and convergence time is also uncertain.…”

A Practical Study on Quality Evaluation for Age Recognition Systems

“…In the field of Agricultural, water conservancy, weather and economy, some error factors are used to evaluate the error, such as average absolute error, relative error [12][13][14][15]. But these quality factors haven't been used systematically and adaptively to evaluate an age recognition system.…”

Form Error Evaluation of Noncontact Scan Data Using Constriction Factor Particle Swarm Optimization

“…Earlier, coordinate measuring machines (CMMs) are used for acquiring 3D cloud points and for performing o®line/ online inspection activities. [2][3][4] But nowadays, use of noncontact 3D scanner is increasing for CAD modeling, drawing of physical components and part inspection purposes. [5][6][7] The laser-scanning technology has an inbuilt capacity to capture the pro¯le of a component, and therefore, has received considerable appreciation in the industry.…”