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A SPC Strategy for Decision Making in Manufacturing Processes

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Tapping is an extensively employed manufacturing process by which a multi-teeth tool, known as a tap, cuts a mating thread when driven into a hole. When taps are new or slightly worn, the process is under control and the geometry of the resulting threads on the workpiece is correct. But as the tap wear increases, the thread geometry deviates progressively from the correct one and eventually the screw threads become unacceptable. The aim of this paper is to outline the development of a statistical process control strategy for decision making based on data coming from the current signal of the tap spindle for assessing thread quality. It could operate on line, and indicates when the tap wear is so critical that, if the process were continued, it would result in unacceptable screw threads. The system would be very cost-effective since the tapping process could be run without any operator intervention.
Słowa kluczowe
EN
tapping   SPC   decision   quality   PCA  
Rocznik
Strony
5--15
Opis fizyczny
Bibliogr. 15 poz., fot., rys., wykr., tab.
Twórcy
  • Tecnalia Research and Innovation, Donostia – San Sebastián, Gipuzkoa, Spain
  • AGH University of Science and Technology, Faculty of Management, Department of Applied Computer Science, Krakow, Poland
  • Public University of Navarre, Institute of Smart Cities, Integrated Group of Logistics and Transportation, Pamplona, Spain
  • Public University of Navarre, Institute of Smart Cities, Integrated Group of Logistics and Transportation, Pamplona, Spain
  • Public University of Navarre, Department of Engineering, Pamplona, Spain
Bibliografia
  • [1] Chen, Y.B., Sha, J.L., Wu, S.M., 1990. Diagnosis of tapping process by information measure and probability voting approach. Journal of Engineering for Industry, 112, pp. 319–325.
  • [2] Gil Del Val, A., Diéguez, P.M., Arizmendi, M., Estrems, M., 2015. Experimental study of tapping wear mechanisms on nodular cast iron. Procedia Engineering, 132, pp. 190–196.
  • [3] Gil Del Val, A., Fernández, J., Arizmendi, M., Veiga, F., Urízar, J.L., Berriozábal, A., Axpe, A., Diéguez, P.M., 2013. On line diagnosis strategy of thread quality in tapping. Procedia Engineering, 63, pp. 208–217.
  • [4] Jackson, J.E., 1991. A user’s guide to principal components. John Wiley & Sons, New York.
  • [5] Kiluk, S., 2014. Dynamic classification system in large-scale supervision of energy efficiency in buildings. Applied Energy, 132, pp. 1–14.
  • [6] Kiluk, S., 2017. Diagnostic information system dynamics in the evaluation of machine learning algorithms for the supervision of energy efficiency of district heating-supplied buildings. Energy Conversion and Management, 150, pp. 904–913.
  • [7] Li, W., Li D., Ni, J., 2002. Diagnosis of tapping process using spindle motor current. International Journal of Machine Tools & Manufacture, 43, pp. 73–79.
  • [8] Liu, T.-I, Lee, J., Liu, G., Wu, Z., 2013. Monitoring and diagnosis of the tapping process for product quality and automated manufacturing. The International Journal of Advanced Manufacturing Technology, 64(5–8), pp. 1273–1282.
  • [9] Liu, T.I., Ko, E.J., Sha, S.L., 1991. Diagnosis of Tapping Operations Using an AI Approach. Journal Materials Shaping Technology, 9(1), pp. 39–46.
  • [10] Lorentz, G., 1989. Principal component analysis in technology. Annals of CIRP, 38(1), pp. 107–109.
  • [11] Montgomery, D.C., 1996. Introduction to statistical quality control. 3rd edition, John Wiley & Sons, New York.
  • [12] Oezkaya, E., Biermann, D., 2018. Development of a geometrical torque prediction method (GTPM) to automatically determine the relative torque for different tapping tools and diameters. The International Journal of Advanced Manufacturing Technology, 97, pp. 1465–1479.
  • [13] Oppermann, M., Sauer, W., Wohlrabe, H., 2001. Optimization of inspection strategies by use of quality cost models and SPC. Proceedings – Electronic Components and Technology Conference (Cat. No.01EX492), IEEE Xplore, pp. 293–297. 10.1109/ISSE.2001.931086.
  • [14] Shu, M.-H., Wu, H.-C., 2011. Fuzzy X and R control charts: Fuzzy dominance approach. Computers and Industrial Engineering, 61, pp. 676–685.
  • [15] Zhou, B.,Ye, H., Zhang, H., Li, M., 2016. Process monitoring of iron-making process in a blast furnace with PCA-based methods. Control Engineering Practice, 47, pp. 1–14.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-bc663dda-53ba-4fdd-84d5-ab85bc0b5b57
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