ANALYSIS OF DETECTION SYSTEM FOR COVER TAPE OFFSET IN THE TAP AND REEL PROCESS USING NEURAL NET TIME SERIES METHOD
DOI:
https://doi.org/10.11113/aej.v15.22322Keywords:
bayesian regularization, scaled conjugate gradient, predictive analysis, Tape and reel, computer visionAbstract
This technical report presents a comprehensive study on the detection of cover tape offset or misalignment during the tape and reel process, which is crucial for packaging electronic components into individual pockets of carrier tape. The research aims to develop an efficient system utilizing the Raspberry Pi Camera Module for detecting and analyzing cover tape misalignment. The methodology involves integrating the Raspberry Pi Camera Module with a microcontroller to capture and process images of the carrier tape, employing image processing techniques for misalignment detection. The resulting data is displayed in a user-friendly dashboard format using Node-RED. Additionally, the data is analyzed in MATLAB Neural Net Time Series for predictive analysis. The findings of this research, including the analysis of training results, demonstrate the successful implementation of a reliable cover tape misalignment detection system. Notably, the Bayesian Regularization (BR) training algorithm outperformed the Scaled Conjugate Gradient (SCG) training algorithm for cover tape offset's predictive analysis, exhibiting lower Mean Squared Error (MSE) with 0.0015874 for BR compared to 0.0017839 for SCG, consistently lower Mean Absolute Error (MAE) values, stronger linear correlations, and superior overall performance. It emphasizes its effectiveness for accurate predictions.
References
Gallegos-Hernandez, A., Ruiz-Sanchez, F.J. and Villalobos-Cano, J.R., 2002, November. 2D automated visual inspection system for the remote quality control of SMD assembly. In IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02. 3: 2219–2224, DOI: 10.1109/iecon.2002.1185317.
Cen, Y., He, J. and Won, D., 2022. Defect patterns study of pick-and-place machine using automated optical inspection data. Soldering & Surface Mount Technology, 34(2): 69–78. DOI: 10.1108/SSMT-03-2021-0007
S. Qiao, L. Q. Tao, T. L. Ren, and Z. L. Liu, 2016 "Tape & Reel single side peel force test verification," 2016 17th International Conference on Electronic Packaging Technology, ICEPT 2016, 1483–1486. DOI: 10.1109/ICEPT.2016.7583404.
Ye, F., Zhang, Z., Chakrabarty, K. and Gu, X., 2013. Board-level functional fault diagnosis using artificial neural networks, support-vector machines, and weighted-majority voting. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 32(5): 723–736. DOI: 10.1109/TCAD.2012.2234827.
Cai, N., Cen, G., Wu, J., Li, F., Wang, H. and Chen, X., 2018. SMT solder joint inspection via a novel cascaded convolutional neural network. IEEE Transactions on Components, Packaging and Manufacturing Technology. 8(4): 670–677. DOI: 10.1109/TCPMT.2018.2789453.
S. Srivastava, 2021, "Path Detection for Self-Driving Carts by using Canny Edge Detection Algorithm," 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 1–5, DOI: 10.1109/ICRITO51393.2021.9596109.
D. Duan, M. Xie, Q. Mo, Z. Han, and Y. Wan, 2010, "An improved Hough transform for line detection," ICCASM 2010 - 2010 International Conference on Computer Application and System Modeling, Proceedings, 2: 354- 357, DOI: 10.1109/ICCASM.2010.5620827.
C. Wu, 2022,"An Improved Canny Edge Detection Algorithm with Iteration Gradient Filter," 2022 6th International Conference on Imaging, Signal Processing and Communications (ICISPC), 16–21, DOI: 10.1109/ICISPC57208.2022.00011.
L. Chandrasekar and G. Durga, 2014,"Implementation of Hough Transform for Image Processing Applications," 2014 International Conference on Communication and Signal Processing, 843–847, DOI: 10.1109/ICCSP.2014.6949962.
Kolluri, J., Kotte, V.K., Phridviraj, M.S.B. and Razia, S., 2020, June. Reducing overfitting problem in machine learning using novel L1/4 regularization method. In 2020 4th international conference on trends in electronics and informatics (ICOEI)(48184). 934-938. IEEE.
M. Ma, Y. Zhang, Z. Yang, and Y. Shang, 2011, "A class of Nonmonotone Spectral conjugate gradient methods with the generalized quasi-Newton equation," 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC), 1880–1883, DOI: 10.1109/MEC.2011.6025852.
Yuan, J., 2022, May. Research on Employee Performance Prediction Based on Machine Learning. In 2022 IEEE 5th International Conference on Electronics Technology (ICET) IEEE. 1296–1302, DOI: 10.1109/ICET55676.2022.9824477.
C. Webb, 2020. "Developing and evaluating predictive conveyor belt wear models,", DOI: 10.1017/dce.2020.1.
R. B. Roy, 2021 "A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System," IEEE Access, 9: 102137–102152. DOI: 10.1109/ACCESS.2021.3096864.
N. Ahmad, Y. Ghadi, and S. Member, 2022, "Load Forecasting Techniques for Power System: Research Challenges and Survey," IEEE Access, vol. 10: 71054–71090, DOI: 10.1109/ACCESS.2022.3187839.
Qi, J., Du, J., Siniscalchi, S.M., Ma, X. and Lee, C.H., 2020. On mean absolute error for deep neural network based vector-to-vector regression. IEEE Signal Processing Letters, 27: 1485–1489, DOI: 10.1109/LSP.2020.3016837.
Gopane, S., Kotecha, R., Obhan, J., & Pandey, R. K. (2024). Cheat Detection In Online Examinations Using Artificial Intelligence. ASEAN Engineering Journal, 14(1): 121–128. https://doi.org/10.11113/aej.v14.20188
