Design and Development of the MixPro AI Color Station System for Automated Automotive Paint Mixing Using Artificial Intelligenc
Main Article Content
Abstract
The objectives of this research were: 1) To create a prototype of the MixPro AI Color Station: Intelligent Automotive Paint Mixing System, 2) To design a system capable of accurately analyzing color shades from photographs of car parts, and 3) To pilot the system with the target group and gather feedback for further improvement. The sample group used in this research consisted of 10 individuals selected by purposive sampling from Prayoon Kan Chang Garage , Cap Auto Sapa Shop, and the Automotive Technician Department, Vehicle Body and Paint Work Branch, Roi-Et Technical College.The research instruments used were: 1) An evaluation form for the MixPro AI Color Station: Intelligent Automotive Paint Mixing System by experts, 2) A color difference evaluation form for the MixPro AI Color Station: Intelligent Automotive Paint Mixing System, and 3) A user satisfaction evaluation form for the system usability. The findings were presented in a report using description with supporting examples. The statistics used in the research were the mean , percentage , and standard deviation (S.D.) of the quantitative data obtained from the evaluation forms. The research findings revealed the following: 1) The expert evaluation results for the MixPro AI Color Station: Intelligent Automotive Paint Mixing System showed an overall rating at Excellent level (= 4.65,S.D. = 0.49). (2) The results from the color difference evaluation of the MixPro AI Color Station system set, after 20 operational tests, showed a system performance effectiveness of 80%, indicating that the system operates with high stability and accuracy. (3) Overall user satisfaction with the MixPro AI Color Station: Intelligent Automotive Paint Mixing System was rated at the Highest Satisfaction level (
= 4.55, S.D. = 0.65).
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
สงวนสิทธิ์ โดย สถาบันการอาชีวศึกษาภาคตะวันออกเฉียงเหนือ 1
306 หมู่ 5 ถนนมิตรภาพ หนองคาย-อุดรธานี ตำบลโพธิ์ชัย อำเภอเมืองหนองคาย จังหวัดหนองคาย 43000
โทร 0-4241-1445,0-4241-1447
ISSN : 3027-6861 (print) ISSN : 3027-687X (online)
References
S. Santiwes, “Computer software for color matching and determining color mixing ratios in architectural design,” M.Arch. thesis, Chulalongkorn Univ., Bangkok, Thailand, 2003. (in Thai)
S. Klapsaeng, “Color management tools,” M.Sc. thesis, Chulalongkorn Univ., Bangkok, Thailand, 1996, doi: 10.58837/CHULA.THE.1996.1471. (in Thai)
V. Wannakovit *et al*., “The combination of technology and automation to reduce the amount remaining after the application of paint and paint it the color of the wash pump,” *Kasem Bundit Engineering Journal*, vol. 3, no. 2, pp. 96–108, Jul.–Dec. 2013. (in Thai)
D. Prommin and S. Tangsitcharoen, “Management for installation of pigment mixer,” in *Proc. 38th Industrial Engineering Network Conf. (IENET)*, Bangkok, Thailand, May 6–8, 2020, pp. 673–679. (in Thai)
T. Wichianpong, “Satisfaction of automatic tinting dealers in Bangkok and its vicinities about paint company,” master’s thesis, Thammasat Univ., Bangkok, Thailand, 2005. (in Thai)
P. Kaewklub, “PTFE solution for mixing business plan,” M.B.A. thesis, Thammasat Univ., Bangkok, Thailand, 2021. (in Thai)
H. Buachuen and A. Tantrapiwat, “Tree image processing for remote control spraying vehicle,” in *Proc. 30th Conf. Mechanical Engineering Network of Thailand*, Thailand, Jul. 5–8, 2016, pp. 551–558. (in Thai)
P. Raguraman, A. Meghana, Y. Navya, Sk. Karishma, and S. Iswarya, “Color detection of RGB images using Python and OpenCV,” *International Journal of Scientific Research in Computer Science, Engineering and Information Technology*, vol. 7, no. 1, pp. 109–112, Jan.–Feb. 2021, doi: 10.32628/CSEIT217119.
G. Sharma, W. Wu, and E. N. Dalal, “The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations,” *Color Research & Application*, vol. 30, no. 1, pp. 21–30, Feb. 2005, doi: 10.1002/col.20070.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in *Advances in Neural Information Processing Systems 25 (NIPS 2012)*, vol. 25, pp. 1097–1105, 2012.
R. S. Sandhya Devi, V. R. Vijay Kumar, and P. Sivakumar, “EfficientNetV2 model for plant disease classification and pest recognition,” *Computer Systems Science and Engineering*, vol. 45, no. 2, pp. 2249–2263, 2023, doi: 10.32604/csse.2023.032231.
M. Tan and Q. V. Le, “EfficientNetV2: Smaller models and faster training,” in *Proc. 38th International Conference on Machine Learning*, PMLR, vol. 139, pp. 10096–10106, 2021.
R. Szeliski, *Computer Vision: Algorithms and Applications*, 2nd ed. Cham, Switzerland: Springer, 2022, doi: 10.1007/978-3-030-34372-9.