This paper investigates the influence of ultraviolet (UV) lighting configurations on the automatic detection of Regions of Interest (ROIs) and the visual identification of surface defects in cast metal parts. A meticulous experimental setup was devised employing four UV light bars arranged in a rectangle, with varying combinations of active light sources. The study utilized the OpenCV library to automate the detection of mounting holes (ROIs) on the cast metal parts. Additionally, a visual inspection was carried out to ascertain the visibility of surface defects under different lighting configurations. The findings revealed a significant enhancement in both ROI detection efficiency and defect visibility as the number of active UV light sources increased. A comparative analysis illustrated the distinct impact of each lighting configuration on the visibility and detectability of critical features on the cast metal parts. This research underscores the importance of optimized lighting conditions in advancing automated inspection systems within the metal casting industry, laying a solid foundation for further exploration into advanced lighting models and sophisticated computer vision algorithms to bolster industrial inspection processes.
Cite this paper
Kyriakides, G. and Daskalakis, A. (2023). A Study of Lighting Models for Automatic Detection of Regions of Interest and Surface Defects in Cast Metal Parts. Open Access Library Journal, 10, e833. doi: http://dx.doi.org/10.4236/oalib.1110833.
Iwata, Y., Dong, S., Sugiyama, Y. and Iwahori, H. (2014) Change in Molten Metal Pressure and Its Effect on Defects of Aluminum Alloy Die Castings. Materials Transactions, 55, 311-317. https://doi.org/10.2320/matertrans.F-M2013838
Zhang, Y. and Zhang, J. (2018) A Review on Computer Vision-Based Defect Detection and Quality Evaluation in the Field of Manufacturing. Journal of Intelligent Manufacturing, 29, 787-805.
Cui, W., Zhang, Y., Zhang, X., Li, L. and Liou, F. (2020) Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network. Applied Sciences, 10, 545. https://doi.org/10.3390/app10020545
Martínez, S.S., Vázquez, C.O., García, J.G. and Ortega, J.G. (2017) Quality Inspection of Machined Metal Parts Using an Image Fusion Technique. Measurement, 111, 374-383. https://doi.org/10.1016/j.measurement.2017.08.002
Castillon, M., Palomer, A., Forest, J. and Ridao, P. (2021) Underwater 3D Scanner Model Using a Biaxial Mems Mirror. IEEE Access, 9, 50231-50243.
https://doi.org/10.1109/ACCESS.2021.3069189
Palomer, A., Ridao, P., Forest, J. and Ribas, D. (2019) Underwater Laser Scanner: Ray-Based Model and Calibration. IEEE/ASME Transactions on Mechatronics, 24, 1986-1997. https://doi.org/10.1109/TMECH.2019.2929652
Jang, J., Shin, M., Lim, S., Park, J., Kim, J. and Paik, J. (2019) Intelligent Image-Based Railway Inspection System Using Deep Learning-Based Object Detection and Weber Contrast-Based Image Comparison. Sensors, 19, Article 4738.
https://doi.org/10.3390/s19214738
Tao, X., Zhang, D., Ma, W., Liu, X. and Xu, D. (2018) Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks. Applied Sciences, 8, Article 1575. https://doi.org/10.3390/app8091575
Pang, G. and Chu, M.-H. (2009) Automated Optical Inspection of Solder Paste Based on 2.5D Visual Images. 2009 International Conference on Mechatronics and Automation, Changchun, 09-12 August 2009.
https://doi.org/10.1109/ICMA.2009.5246351
Essid, O., Laga, H. and Samir, C. (2018) Automatic Detection and Classification of Manufacturing Defects in Metal Boxes Using Deep Neural Networks. PLOS ONE, 13, e0203192. https://doi.org/10.1371/journal.pone.0203192
Yu, J., Han, S. and Lee, C.-O. (2023) Defect Inspection in Semiconductor Images Using Fast-Mcd Method and Neural Network. The International Journal of Advanced Manufacturing Technology, 129, 1547-1565.
https://doi.org/10.1007/s00170-023-12287-z
Valencia, Y.M., Majin, J.J., Taveira, V.B., Salazar, J.D., Stivanello, M.E., Ferreira, L. and Stemmer, M.R. (2021) A Novel Method for Inspection Defects in Commercial Eggs Using Computer Vision. The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, XLIII-B2-2021, 809-816.
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-809-2021
Iwata, Y., Dong, S., Sugiyama, Y. and Iwahori, H. (2013) Effects of Solidification Behavior During Filling on Surface Defects of Aluminum Alloy Die Casting. Materials Transactions, 54, 1944-1950. https://doi.org/10.2320/matertrans.F-M2013819
Dawda, A. and Nguyen, M. (2021) Comparison of Red Versus Blue Laser Light for Accurate 3D Measurement of Highly Specular Surfaces in Ambient Lighting Conditions. In: Nguyen, M., Yan, W.Q. and Ho, H. Eds., Geometry and Vision: First International Symposium, Springer, New York, 300-312.
https://doi.org/10.1007/978-3-030-72073-5_23
Smith, L. and Smith, M. (2005) The Virtual Point Light Source Model the Practical Realisation of Photometric Stereo for Dynamic Surface Inspection. In: Roli, F. and Vitulano, S., Eds., Image Analysis and Processing—ICIAP 2005, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 495-502.
https://doi.org/10.1007/11553595_61
