# Intelligent Framework Design for Quality Control in Industry 4.0
## 5 Conclusions  
This paper has provided an intelligent framework for quality control from the perspective of Industry 4.0. The paper takes into account pre-production and post-production quality control. Two models are developed; one is an ANN with vibration data to monitor the health condition of the induction motor used in production, as the health of the production machine has a direct impact on the quality of the finished product. In the second part, a CNN is used with the machine vision system to collect images of manufactured products and classify the products as defective and good products. The pre-production model is capable of achieving accuracy of 96.44% in identifying defects in the machine under variable conditions. The machine vision-based model used for post-production quality control is successful in achieving accuracy of 96.56%. The accuracies of models can be further improved by collecting more and cleaner vibration data and high-quality images. The size of the data can increase the performance of both models by providing a better insight of data to the machine learning models. The framework can be extended to other sensor data, including temperature, acoustic sensors, and lasers. The temperature is time series data that would require a few adjustments in model parameters. Acoustic data can be analyzed with the developed framework with the addition of speech-processing techniques. Collecting vibration data with lasers can increase the efficiency of the model at the added cost of expensive laser installation. The LSTM model can be deployed for pre-production, and Vision Transformer can be explored for the post-production part of the framework in future studies. The framework can also be deployed in a variety of industries where vibration data and machine vision-based quality control are desired. The following statements specify the contribution of every author to the research and preparation of the manuscript: Y.A. : Conceptualization, Methodology, Visualization, Formal Analysis, Investigation, Software, Writing--Review and Editing. S.W.S. : Supervision, Project Administration, Conceptualization, Methodology, Visualization, Formal Analysis, Investigation, Validation. A.A.: Formal Analysis, Investigation, Validation, Visualization. M.T. : Writing--Reviewing and Editing, Supervision, Investigation, Validation. M.R.S. : Reviewing and Editing. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Technique/Model** & **Test Accuracy** \\ \hline
[45] & 2021 & DISA-KNN & 90\% \\
[46] & 2020 & 1 DCNN-SVM & 90.4\% \\
[47] & 2023 & EMD-ASO-SVM & 94\% \\
[48] & 2017 & DNN & 94.9\% \\
[49] & 2020 & ANN & 94.17\% \\ This research & 2024 & ANN using vibration data at variable loads & 96.44\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of gear health prediction systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Year** & **Technique/Model** & **Test Accuracy** \\ \hline
[26] & 2021 & CNN & 90.4\% \\
[50] & 2022 & VGG16 & 92.7\% \\
[51] & 2022 & SVM & 74\% \\
[52] & 2024 & Image processing & 92\% \\
[53] & 2023 & ResNet50 & 89\% \\ This research & 2024 & CNN using images of hex-Nut & 96.56\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of machine vision systems. **Funding:** The authors extend their appreciation to King Saud University for providing financial support for this work through the Researchers Supporting Project number (RSPD2024R685), King Saud University, Riyadh, Saudi Arabia. **Institutional Review Board Statement:** Not Applicable. **Informed Consent Statement:** Not Applicable. **Data Availability Statement:** The vibration data were recorded at Southern Denmark University. The data set can be accessed from [33]. The hex nut data set is available upon request. **Acknowledgments:** The authors acknowledge 'Researchers Supporting Project number (RSPD2024R685), King Saud University, Riyadh, Saudi Arabia'.