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Abstrakty
The Mahalanobis-Taguchi System (MTS) is, today, widely used to define the optimal conditions for the design stage of product development especially, in the field of Artificial Intelligence (AI) considering the non-linear properties and non-digital data. In this paper, an approach to identify the several interactions in a MTS is proposed. The MTS contains four methods; Mahalanobis-Taguchi (MT) method, Mahalanobis Taguchi Adjoint (MTA) method, Recognition Taguchi (RT) method and Taguchi (T) method. The method to use for the analysis is selected based on the system’s properties. For the case of study used in this research, the unit space is created through the RT method and used to calculate the Mahalanobis-Taguchi distances (MTD). For the method proposed in this paper, the relationships between control factors and MTDs were firstly clarified by MTS (RT), then the same relationships were clarified using a modified design of experiments method, and the several interactions between control factors in MTS (RT) were finally identified by comparing the two relationships. Then effectiveness of the proposed method was evaluated by using a mathematical model.
Czasopismo
Rocznik
Tom
Strony
96--110
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
- Department of Mechanical Engineering, Nagaoka University of Technology, Japan
autor
- Department of Mechanical Engineering, Nagaoka University of Technology, Japan
- School of Engineering, Sanjyo City University, Japan
autor
- School of Engineering, Sanjyo City University, Japan
autor
- Department of Mechanical Engineering, Nagaoka University of Technology, Japan
Bibliografia
- [1] TANABE I., TAKAHASHI S., 2018, Development of the Program for Searching the Optimum Condition Using Design of Experiments, Transactions of the JSME, 84/862, DOI:10.1299/transjsme.18-00171, (in Japanese).
- [2] YANO K., 2015, Fundamentals of Quality Engineering and Parameter Design, Journal of the Society for Precision Engineering, 81/11, 1008–1012 (in Japanese).
- [3] FUJIKAWA S., 1999, Optimum Parameter Design Using the Taguchi Method for Finite-Element Analysis of 3D Forging Deformation, Journal of Japan Society for Technology of Plasticity, 40/466, 1061–1065, (in Japanese).
- [4] NISHIOKA T., ITO K., 2004, Study of Conditions for Microdrilling Process with High-Speed Spindle, Journal of Quality Engineering Society, 12/3, 85–89, (in Japanese).
- [5] SHIMIZU Y., TAKEZAWA Y., AMAYA K., YANO H., 2016, Machining Center Tool Magazine Program Evaluation Using an Orthogonal Array, Journal of Quality Engineering Society, 24/1, 15–21, (in Japanese).
- [6] KAMIKURA K., DAIKUHARA T., WATANABE Y., 2006, Optimization of High Speed Deep-Hole Drilling Conditions with Mist Coolant, Journal of Quality Engineering Society, 14/1, 93–100, (in Japanese).
- [7] KOMURA A., KOBASHI K., IWASHIGE K., HATTORI K., WATANABE T., 2012, Parameter Design for Air-Cooling of Rotating Machine, Journal of Quality Engineering Society, 20/3, 116–122, (in Japanese).
- [8] MIZUTANI J., HAMAMOTO S., MUKAIYAMA T., TANABE I., YAMADA Y., 2002, Functional Evaluation of a Micro-Polishing Process by a Combined Method of Physical and Chemical Treatments, Journal of Quality Engineering Society, 10/1, 84–90, (in Japanese).
- [9] IYAMA T., TANABE I., TAKAHASHI T., 2009, Optimization of Lapping Slurry in Automatic Lapping System for Dies with Cemented Carbide and Its Evaluation, Transactions of Japan Society of Mechanical Engineers, Series C, 75/749, 210–215, (in Japanese).
- [10] YUKI H. and KAI D., 2013, Application of Mahalanobis-Taguchi Method to Identification of the Initial Motion Part in Acoustic Emission Waveforms, Transactions of Japan Society of Mechanical Engineers, Series C, 79/797 145–149, (in Japanese).
- [11] MIKI S., OKAZAWA H., INUJIMA H., 2007, Evaluation of the Deterioration Degree of Insulations for Breakers Using Chemical Analysis and the Mahalanobis-Taguchi (MT) Method, The Institute of Electrical Engineers of Japan Trans. PE, 127/9, 1033–1040, (in Japanese).
- [12] NAKAJIMA H., YANO K., UETAKE S., TAKAGI I., 2012, Diagnosis of Liver Diseases by Classification of Laboratory Signal Factor Pattern Findings with the Mahalanobis Taguchi Adjoint Method, Journal of Japanese Society of Gastroenterology, 109/2, 34–46, (in Japanese).
- [13] KURIHARA K., HAMADA K., MA Z., KUBO H., IKUTA K., 2005, Design of Diagnosis System for Load on Weld Robot Cables by MT System, Journal of Quality Engineering Society, 13/5, 55–63, (in Japanese).
- [14] WATANABE J., 2017, Relevant IoT to Enrich Smart Life, The Journal of the Institute of Electrical Installation Engineers of Japan, 37/5, 281–284 (in Japanese).
- [15] KONDO N., HATANAKA T., 2016, Modelling of Students’ Learning States Using Big Data of Students Through the Baccalaureate Degree Program, Journal of Japanese Society for Information and Systems in Education, 33/2, 94‒103, (in Japanese).
- [16] FUKUSHIMA T., FUJIMAKI R., OKANOHARA D., SUGIYAMA M., 2017, Outlook for Big Data and Machine Learning Cutting-Edge Technological Challenges and Expanding Applications, Journal of Information Processing and Management, 60/8, 543–554, (in Japanese).
- [17] TAKAHAMA M., MIKAMI N., 2012, Detection of Abnormal Signs for Gas Turbine Power Plant, Journal of Quality Engineering Society, 20/4, 45–51, (in Japanese).
- [18] TANIMURA T., HOSHIDA T., SHIOTA K., KATAYAMA E., KATO T., WATANABE S., MORIKAWA H., 2018, Deep Learning Based Optical Monitoring Toward Optical Network Automation, Transactions of Institute of Electronics, Information and Communication Engineers, Series B, J101-B/12, 1014–1025, (in Japanese).
- [19] UHLMANN E., POLTE M., BLUMBERG J., LI Z., KRAFT A., 2021, Hyperparameter Optimization of Artificial Neural Networks to Improve the Positional Accuracy of Industrial Robots, Journal of Machine Engineering, 21/2, 47–59.
- [20] PUTNIK G.D., SHAH V., PUTNIK Z., FERREIRA L., 2020, Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – it Does Not Mean Total Automation, Human is Still in the Centre: Part I–Manufacturing Singularity and an Intelligent Machine Architecture, Journal of Machine Engineering, 20/4, 161–184.
- [21] TAGUCHI G., CHOWDHURY S., WU Y., McGRAW H., 2001, Mahalanobis-Taguchi, Google Books, ISBN 0071362630, 1‒190.
- [22] TAGUCHI G., JUGULUM R., 2002, The Mahalanobis-Taguchi Strategy: A Pattern Technology System, Springer-Verlag London, Ltd., United Kingdom.
- [23] TANABE I., MIZUTANI J., TAKAHASHI S., KUMAI T., 2018, Development of a Program for the Determination of Control Factor Synergistic Effects in the Design of Experiments, The Japan Society of Mechanical Engineers, 84/863, 1–13 (in Japanese).
- [24] SUZUKI M., 2012, Introduction to Analysis Method Using MT System, Nikkankougyoushinbunsya, (in Japanese).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d97a0c21-291c-414c-9582-f32e70f2b55c