Evaluation of the Calculated Sizes Based on the Neural Network Regression

Abstract

The evaluation models of circumference diameter, area diameter, and volume diameter were established based on the cylindrical coordinate measuring method, respectively. Many groups of roundness profiles of cylindrical specimens were extracted, and their pseudo and actual circumference diameters, area diameters, and volume diameters were evaluated according to the established evaluation models and the sampling data of the extracted roundness profiles. The relationship models between the pseudo calculated sizes and the actual calculated sizes were built through a series of training based on the neural network regression. The checked experiments for the training models showed that the evaluation of the calculated sizes can meet their measurement accuracy requirement through the pseudo calculated sizes, which were evaluated based on the roundness profiles by using the cylindricity or roundness measuring instrument.

Advancements in Roundness Measurement Parts for Industrial Automation Using Internet of Things Architecture-Based Computer Vision and Image Processing Techniques

“…These studies introduced a method for evaluating roundness and cylindricity tolerances, addressing difficulties encountered by technicians and engineers, focusing on optimizing manual procedures, and improving skills [34]. Furthermore, roundness profiles with pseudo and actual diameters were evaluated, and relationship models with neural network regression based on coordinate measuring machines were developed [35]. The "roundness tolerance band" is defined by the International Organization for Standardization (ISO) as the area between two concentric circles at the same cross-section where the difference in their radii equals the roundness tolerance value.…”

Study on the Extraction Simulation of the Cylindrical Features

“…e evaluation results of global sizes and cylindricity errors based on the cylindrical features simulated by using the parameters in Table 3 and the evaluation models [5] are shown in Figures 18 and 19, where LSD, MCD, MID, and MMD are the abbreviations of least-squares diameters, minimum circumscribed diameters, maximum inscribed diameters, and minimax diameters, respectively, and LSC, MCC, MIC, and MZC are the abbreviations of cylindricity errors evaluated by least-square criterion, minimum circumscribed criterion, maximum inscribed criterion, and minimum zone criterion [11][12][13][14], respectively. e evaluated roundness errors of the cylindrical features based on the roundness profile extraction strategy and the corresponding models [15,16] are shown in Figure 20, where MEAN, MAX, and MIN represent the mean, maximum, and minimum values of roundness errors for one cylindrical feature, respectively, and their corresponding calculated size is shown in Figure 21, where C_D, A_D, and V_D represent the circumference diameter, area diameter, and volume diameter of the calculated sizes, which were evaluated based on the corresponding roundness profiles above and the models [17], and the meanings of MEAN, MAX, and MIN can be seen in Figure 20.…”

Analysis for Mutual Impedance of Pistons by Neural Network and Its Extension of Derivative

“…It belongs to machine learning and has many applications in engineering. ese applications are basically mathematical problems in engineering [8][9][10]. e NN serves as a black box of nonlinear mapping that accepts certain inputs and produces certain outputs.…”

“…e neural-network machine-learning back box is expected to replace the difficult theoretical computations or practical experiments. ere have been widespread applications in applying the neural network to different engineering problems, e.g., [7][8][9][10][11][12][13][14][15]. In reference [7], several types of neural networks are utilized to model the relation between the input and output of different fundamental electromagnetic problems.…”

“…However, most applications of neural networks in engineering problems are for nonlinear mapping only, e.g. [7][8][9][10][11][12][13][14][15], as introduced above. In some application, the knowledge of slope information for the system output is necessary.…”