Fig 1.
Implementing the automation system measures data through VIS, CMM, and CNN.
Table 1.
Diameter values from the Vision system of Model 1 and Model 2.
Table 2.
Diameter values of Model 1 and Model 2 from the CMM machine for the Delrin workpiece.
Fig 2.
The inspection procedure in CMM for Model 1 and Model 2.
Fig 3.
The flowchart illustrates the overview of the CNN process.
Table 3.
Diameter values from the CNN predictions data of Model 1 and Model 2.
Fig 4.
Image processing implementation for Models 1 &2.
Table 4.
Classification Report of Model Training.
Fig 5.
The Proposed Model of Confusion Matrix Analyses in the Testing Dataset.
Fig 6.
(a) The Accuracy Analysis Training and Testing Proposed Method, and (b) The loss analysis for training and testing for the suggested approach.
Fig 7.
Area under ROC curves of evaluated classifiers using the proposed method.
Fig 8.
(a) Detection example for Model 1 and (b) Detection example for Model 2.
Fig 9.
Model 1 Predicted values with the actual value for hole diameters using CNN, CMM, and Vision System.
Fig 10.
Model 2 Predicted values vs. actual measurements for hole diameters using CNN, CMM, and Vision System.
Table 5.
Characteristics and Specifications of Models 1 and 2 with software components.
Table 6.
Descriptive Statistics of Measurement Techniques: VI, CMM, and CNN.