Classification of Carbon Steels by Automated Spark Test Technique Using Feature Extraction Based on Machine Learning Image Processing
DOI: 10.14416/j.ind.tech.2022.08.005
Keywords:
Spark Testing, JIS G 0566, Image Processing, Machine LearningAbstract
There are several methods of steel classification testing. The test through an emission spectrometer is one of the test methods for classifying steel. However, there are limitations involved in terms of time-consumption and cost-effectiveness in this method. The spark test is another method to classify steel, but this method still requires the knowledge and proficiency skills of the tester. Hence, this method is not very popular. The advantage of this analysis is that the testing process is not complicated and easy to do. However, the spark test requires high proficiency skills in classifying metals. This research applied the principle of steel classification to analyze the spark characteristics by categorizing metal groups using the machine learning characteristics according to JIS G 0566 standard. The results showed that low carbon steel was classified with an accuracy of 100%, medium carbon steel was classified with an accuracy of 95%, and high carbon steel was classified with an accuracy of 90%.
References
JIS G 0566, Method of Spark Test for Steels, 1980.
S. Wongsa-ad, S. Wattanasriyakul andP. Jenkittiyon, The development of spark testing for steel identify by digital photographing, Industrial Engineering Network Conference 2007, Proceeding, 2007, 1364-1369. (In Thai)
J. La-or and S. Wattanasriyakul, The feasibility study steels identification with spark test image analysis by image processing technique and neural network, Industrial Engineering Network Conference 2008, Proceeding, 2008, 870-875. (in Thai)
P. Rienpradub, S. Wattanasriyakul andP. Jenkittiyon, Application of web camera technique for steel identification using spark test, The Journal of Industrial Technology, 2013, 9(1), 49-58. (in Thai)
T. Nakata, Development of automated spark testing technique by image processing to measure carbon content in steel materials, Automation in the Mining, Mineral and Metal Industries 2013, Proceeding, 2013, 118-119.
T. Dalke, J. Brink and M. Weller, Material determination using spark observation, Global Journal of Engineering Education, 2013, 15(3), 165-170.
K.H. Rawani, S.V. Painjane, A.P. Salunkhe,S.S. Patil, S.S. Dharmarao, and P. Deshpande, Experimental analysis and determination of various plain carbon steel by using spark test, International Journal of Research Publications in Engineering and Technology, 2017, 3(4), 123-128.
R.W. Picard, E. Vyzas and J. Healey, Toward machine emotional intelligence: Analysis of affective physiological state, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(10), 1175-1191.
T.F. Bastos-Filho, A. Ferreira, A.C. Atencio, S. Arjunan and D. Kumar, Evaluation of feature extraction techniques in emotional state recognition, The 4th International Conference on Intelligent Human Computer Interaction (IHCI), Proceeding, 2012, 1-6.
B. Bataineh, S.N.H.S. Abdullah, K. Omar, A novel Statistical Feature Extraction Method for Textual Images: Optical Font Recognition, Expert System with Applications, 2012, 39(5), 5470-5477.
N. Elavarasan and K. Mani, A survey on feature extraction techniques, International Journal of Innovation Research in Computer and Communication Engineering, 2015, 3, 52-55.
M.A. Mohammed, K.H. Abdulkareem, B. Garcia-Zapirain, S.A. Mostafa, M.S. Maashi, A.S. Al-Waisy, M.A. Subhi, A.A. Mutlag and D. Le, A comprehensive investigation of machine learning feature extraction and classification methods for automated diagnosis of COVID-19 based on x-ray images, Computers, Materials and Continua, 2021, 66(3), 3289-3310.
M. AlQuraishi, Machine learning in protein structure prediction, Current Opinion in Chemical Biology, 2021, 65, 1-8.
www.mathworks.com/help/matlab/ref/rgb2gray.html. (Accessed on 29 December 2021)
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ผลงานวิจัยและบทความวิชาการที่ปรากฏในวารสารนี้ เป็นความคิดเห็นอิสระของผู้เขียน ผู้เขียนจะต้องเป็นผู้รับผิดชอบต่อผลทางกฎหมายใด ๆ ที่อาจจะเกิดขึ้นจากบทความนั้น กองบรรณาธิการและคณะจัดทำวารสารฯไม่จำเป็นต้องเห็นด้วยเสมอไป