| Summary: | With the increasing micro-scale manufacturing industry, the micro-scale spherical and cylindrical surface modeling has become an important factor in the manufacturing process. Thus, the micro-scale manufacturing processes require efficient micro-scale spherical and cylindrical models to achieve accurate assembly. Therefore, it is necessary to implement models to represent micro-scale spherical and cylindrical surfaces. This study addresses metaheuristic algorithms based on micro laser line projection to perform micro-scale spherical and cylindrical surface modeling. In this technique, the micro-scale surface is recovered by an optical microscope system, which computes the surface coordinates via micro laser line projection. From the surface coordinates, a genetic algorithm determines the parameters of the mathematical models to represent the spherical and cylindrical surfaces. The genetic algorithm performs exploration and exploitation in the search space to optimize the models’ mathematical parameters. The search space is constructed via surface data to provide the optimal parameters, which determine the spherical and cylindrical surface models. The proposed technique improves the fitting accuracy of the micro-scale spherical and cylindrical surface modeling performed via optical microscope systems. This contribution is elucidated by a discussion about the model fitting between the genetic algorithms based on micro laser line projection and the optical microscope systems.
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