Abstract
The actualization of the befitting sampling strategy and the application of an appropriate evaluation algorithm have been elementary issues in the coordinate metrology. The decisions regarding their choice for a given geometrical feature customarily rely upon the user’s instinct or experience. As a consequence, the measurement results have to be accommodated between the accuracy and the inspection time. Certainly, a reliable and efficient sampling plan is imperative to accomplish a dependable inspection in minimal time, effort, and cost. This paper deals with the determination of an optimal sampling plan that minimizes the inspection cost, while still promising a measurement quality. A cylindrical-shaped component has been utilized in this work to achieve the desired objective. The inspection quality of the cylinder using a coordinate measuring machine (CMM) can be enhanced by controlling the three main parameters, which are used as input variables in the data file, namely, point distribution schemes, total number of points, and form evaluation algorithms. These factors affect the inspection output, in terms of cylindricity and measurement time, which are considered as target variables. The dataset, which comprises input and intended parameters, has been acquired through experimentation on the CMM machine. This work has utilized state-of-the-art machine learning algorithms to establish predictive models, which can predict the inspection output. The different algorithms have been examined and compared to seek for the most relevant machine learning regression method. The best performance has been observed using the support vector regression for cylindricity, with a mean absolute error of 0.000508 mm and a root-mean-squared error of 0.000885 mm. Likewise, the best prediction performance for measuring time has been demonstrated by the decision trees. Finally, the optimal parameters are estimated by employing the grey relational analysis (GRA) and the fuzzy technique for order performance by similarity to ideal solution (FTOPSIS). It has been approved that the values obtained from GRA are comparable with those of the FTOPSIS. Moreover, the quality of the optimal results is bettered by incorporating the measurement uncertainty in the outcome.
Highlights
Coordinate measuring machine (CMM) has revolutionized the manufacturing industries, owing to its high accuracy and precision capabilities
Predictive Models. is section covers the illustration of the relative performance of different machine learning (ML) algorithms for estimating cylindricity and measurement time. e resulting performance indices and their corresponding parameter setting are presented in Table 2. e comparison of the performance designators indicates that the Gaussian support vector regression (SVR), with the highest prediction accuracy (RMSE − 0.000885, mean absolute error (MAE) − 0.000508, and R2 − 0.74), is the best regression method for predicting cylindricity
A dependable sampling plan is necessary if an accurate inspection is to be carried out in minimal time, effort, and resources. e accomplishment of the effective sampling strategy and the usage of relevant evaluation algorithms are fundamental problems in the CMM inspection
Summary
Coordinate measuring machine (CMM) has revolutionized the manufacturing industries, owing to its high accuracy and precision capabilities. It is being widely used in the automotive, aerospace, and medical industries to perform the part inspection. It is a complex machine, where different components and subcomponents with their varied performance influence the measurement outcome. With growing intricacy of parts and tighter tolerances, intelligible approaches and techniques are needed to effectively plan measurement on the CMM. According to Baldwin et al [2] and Weckenmann et al [3], numerous variables as depicted in Figure 1 can influence the output of the CMM measurement. According to Baldwin et al [2] and Weckenmann et al [3], numerous variables as depicted in Figure 1 can influence the output of the CMM measurement. ese factors encompass the sampling strategy, evaluation algorithm, workpiece position and orientation, surface conditions, sensor type and configuration, environment conditions, etc
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