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Tytuł artykułu

Classification graph of Poka-Yoke techniques for industrial applications: assembly process case studies effectiveness evaluation

Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
Graf klasyfikacji technik Poka-Yoke do zastosowań przemysłowych: ocena efektywności studiów przypadków dla procesu montażu
Języki publikacji
EN
Abstrakty
EN
It is widely acknowledged that the expenses associated with substandard quality constitute a significant portion of a company's overall costs. Consequently, organizations adopt quality management systems and implement corrective and preventive measures to reduce these expenses. Within these implementations, Poka-Yoke (P-Y) techniques are notably prominent. Theoretically, these techniques are designed to prevent mistakes that lead to costs, especially quality-related costs associated with nonconforming products. This study proposes a classification graph of P-Y techniques, which serves as a tool for evaluating the effectiveness of these techniques in preventing errors that lead to product nonconformities, machine failures, operator injuries, or environmental threats. The Classification Graph was developed based on a study of 139 P-Y solutions implemented in 24 companies operating in the automotive, aviation, and metal processing industries. The value of this graph lies in its ability to easily evaluate and prioritize different P-Y techniques, aiding in the design of new techniques and the improvement of existing ones to enhance the reliability of production systems.
PL
Powszechnie uznaje się, że wydatki związane z jakością poniżej standardu stanowią znaczną część ogólnych kosztów firmy. W związku z tym organizacje przyjmują systemy zarządzania jakością i wdrażają środki korygujące i zapobiegawcze w celu zmniejszenia tych wydatków. W ramach tych wdrożeń techniki Poka-Yoke (P-Y) są szczególnie ważne. Teoretycznie techniki te mają na celu zapobieganie błędom, które prowadzą do kosztów, zwłaszcza kosztów związanych z wyrobami niezgodnymi. W niniejszym badaniu zaproponowano wykres klasyfikacji technik P-Y, który służy jako narzędzie do oceny skuteczności tych technik w zapobieganiu błędom, które prowadzą do niezgodności wyrobów, awarii maszyn, obrażeń operatorów lub zagrożeń dla środowiska. Graf klasyfikacji został opracowany na podstawie badania 139 rozwiązań P-Y wdrożonych w 24 firmach działających w branży motoryzacyjnej, lotniczej i obróbki metali. Wartość tego wykresu polega na jego zdolności do łatwej oceny i ustalania priorytetów różnych technik P-Y, co pomaga w projektowaniu nowych technik i ulepszaniu istniejących w celu zwiększenia niezawodności systemów produkcyjnych.
Rocznik
Tom
Strony
18--28
Opis fizyczny
Bibliogr. 39 poz., il. kolor., fot., wykr.
Twórcy
  • Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, Al. Powstancow Warszawy 12, Rzeszow, Poland
  • Department of Production Systems and Economics, Politecnico di Torino, Corso Duca degli Abruzzi 24, I 10129 Torino, Italy
Bibliografia
  • Amaral, L. C., Biazussi, J. L., Almeida, S. D., Gomes, F. J., & Araujo, D. A. (2023, October). Quick Response for Risk Mitigation in Oil Services Operations: A Case Study of Poka-Yoke Application. In Offshore Technology Conference Brasil (p. D032S055R003). OTC.
  • Andersen M., Andersen R., Larsen C., Moeslund T. B. and Madsen O. (2009). Interactive Assembly Guide Using Augmented Reality. Proceedings of the 5th International Symposium on Advances in Visual Computing (ISVC2009), Las Vegas, Nevada, 999-1008.
  • Antonelli D., Stadnicka D. (2016). Classification and efficiency estimation of mistake proofing solutions by Fuzzy Inference. IFAC Conference on Manufacturing Modelling, Management and Control. June 28-30, 2016. Troyes, France. IFAC-PapersOnLine, 49(12):1134-1139. Web of Science.
  • Antonelli, D., Stadnicka, D. (2019). Predicting and preventing mistakes in human-robot collaborative assembly. IFAC-PapersOnLine, 52(13): 743-748. https://doi.org/10.1016/j.ifacol.2019.11.204
  • Azuma R., Baillot Y., Behringer R., Feiner S., Julier S. and MacIntyre B. (2012). Recent advances in augmented reality. Computer Graphics and Applications IEEE, 1(6):34-47.
  • Chen L.H., Chang F. M. and Chen Y.L. (2011), The application of the multinomial control charts under inspection error. International Journal of Industrial Engineering, 18(5):244-253.
  • Chimienti V., Iliano S., Dassisti M., Dini G. and Failli F. (2010). Guidelines for implementing augmented reality procedures in assisting assembly operations. Proceedings of the 5th IFIP, Precision Assembly Technologies and Systems. IFIP Advances in Information and Communication Technology, 315:174-179.
  • Czajka P., Giesko T., Matecki K. and Zbrowski A. (2010). Methods and system of laser inspection of seal assembly in rolling bearings. Technologia i Automatyzacja Montażu, 2:22-28 (in Polish).
  • Dahari, S., Talib, M. A., & Ghapor, A. A. (2025). Robust Control Chart Application in Semiconductor Manufacturing Process. Journal of Advanced Research in Applied Sciences and Engineering Technology, 43(2), 203-219.
  • de Saint Maurice G., Giraud N., Ausset S., Auroy Y., Lenoir B. and Amalberti R. (2011). Comprendre la notion de détrompage. Annales françaises d'anesthésie et de reanimation, 30:51-56.
  • Evans J. R. and Lindsay W. M. (2005). The management and control of quality (6th ed.). Thomson, South-Western, United Kingdom.
  • Garza F. & Das M. (2001). On real time monitoring and control of resistance spot welds using dynamic resistance signatures. In Circuits and Systems. MWSCAS 2001. Proceedings of the 44th IEEE 2001 Midwest Symposium,1:41-44.
  • Gladysz, B., & Buczacki, A. (2018). Wireless technologies for lean manufacturing-a literature review. Management and Production Engineering Review, 9:20-34.
  • Hakkarainen M., Woodward C. and Billinghurst M. (2008). Augmented Assembly using a Mobile Phone. 7th IEEE International Symposium on Mixed and Augmented Reality (ISMAR2008), 167-168.
  • Hollnagel E. (2004). Barrier analysis and accident prevention. Ashgate.
  • Kozikowski, E., Hartman, N. W., Camba, J. D. (2022). Development and evaluation of a computer vision system for assembly bolt pattern traceability and poka-yoke. Proceedings of ASME 2022 17th International Manufacturing Science and Engineering Conference, MSEC 2022, 2, art. no. V002T06A023.
  • Kumar S., Kumar S.S.M. and Kumar A. (2009). Scrap reduction by using total quality management tools. International Journal of Industrial Engineering, 16(4):364-369.
  • Lazarevic, M., Mandic, J., Sremcev, N., Vukelic, D., & Debevec, M. (2019). A systematic literature review of Poka-Yoke and novel approach to theoretical aspects. Strojniski Vestnik/Journal of Mechanical Engineering, 65(7-8), 454-467.
  • Lucantoni, L., Antomarioni, S., Ciarapica, F. E., & Bevilacqua, M. (2022). Implementation of Industry 4.0 Techniques in Lean Production Technology: A Literature Review. Management and Production Engineering Review, 13:83-93.
  • Mabkhot, M.M.; Ferreira, P.; Maffei, A.; Podržaj, P.; Mądziel, M.; Antonelli, D.; Lanzetta, M.; Barata, J.; Boffa, E.; Finžgar, M.; Paśko Ł.; Minetola P.; Chelli R.; Nikghadam-Hojjati S.; Wang V.; Priarone P.C.; Litwin P.; Stadnicka D.; Lohse N. (2021). Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals. Sustainability 2021, 13, 2560. https://doi.org/10.3390/su13052560
  • Martinelli, M.; Lippi, M.; Gamberini, R.: Poka yoke meets deep learning: a proof of concept for an assembly line application. Appl. Sci. 12(21), 11071 (2022). https://doi.org/10.3390/app122111071
  • Middleton P. (2001). Lean software development: two case studies. Software Quality Journal, 9:241-52.
  • Muharam M. and Latif M. (2019), Design of poka-yoke system based on fuzzy neural network for rotary machinery monitoring, IOP Conference Series: Materials Science and Engineering, Volume 602, Conference on Innovation in Technology and Engineering Science (CITES 2018) 08/11/2018- 09/11/2018 Padang, West Sumatra, Indonesia.
  • Pinosova, M., & Andrejiova, M. (2023). Analysis of the error rate in PCB production using quality management tool. Annals of Faculty Engineering Hunedoara - International Journal of Engineering. Tom XXI, 4:27-34.
  • Rahardjo, B., Wang, F. K., Yeh, R. H., & Chen, Y. P. (2023). Lean manufacturing in industry 4.0: a smart and sustainable manufacturing system. Machines, 11(1), 72.
  • Ramadan M. and Salah B. (2019), Smart Lean Manufacturing in the Context of Industry 4.0: A Case Study, International Journal of Industrial and Manufacturing Engineering, No. 3, Vol. 13, pp. 174-181.
  • Saurin T. A., Ribeiro J. L. D., Vidor G. (2012). A framework for assessing poka-yoke devices. Journal of Manufacturing Systems, 31:358-366.
  • Sellappan N., Palanikumar K. (2013). Modified prioritization methodology for risk priority number in failure mode and effects analysis. International Journal of Applied Science and Technology, 3(4):27-36.
  • Shingo S. (1988). Zero quality control: source inspection and the poka-yoke system. Productivity Press.
  • Stadnicka, D., and Antonelli, D. (2019). Human-robot collaborative work cell implementation through lean thinking. International Journal of Computer Integrated Manufacturing, 32(6): 580-595.
  • Stewart M. and Grout R. (2001). The human side of mistake-proofing. Production and Operations Management, 10(4):440-459.
  • Tkaczyk S. and Jagła J. (2001). The economic aspects of the implementation of a quality system process in Polish enterprises. Journal of Materials Processing Technology, 109:196-205.
  • Trojanowska, J., Husár, J., Hrehova, S., & Knapčíková, L. (2023). Poka Yoke in Smart Production Systems with Pick-to-Light Implementation to Increase Efficiency. Applied Sciences, 13(21), 11715.
  • Valles A., Noriega S., Sanchez J., Martínez E. and Salinas J. (2009). Six Sigma improvement project for automotive speakers in an assembly process. International Journal of Industrial Engineering, Special Issue: Six Sigma - Its Application, Practice and Utility, 16(3):182-190.
  • Wyskiel M. (2014). Kaizen, as a form of continuous improvement in Lean culture - example of kitting line for assembly sets reorganization. IV Conference Lean Learning Academy "Lean Tools implementation in improvement projects". May 2014 Rzeszow, unpublished work (in Polish).
  • Xiuxu Z. (2011) A Process Oriented Quality Control Approach Based on Dynamic SPC and FMEA. International Journal of Industrial Engineering, 18(8):244-253.
  • Yin X., Fan X., Gu Y., and Wang J. (2017), Sequential dynamic gesture recognition controlled poka-yoke system for manual assembly, CIMS, No. 7, Vol. 23, pp. 1457-1468.
  • Yoo S. H., Kim D. S. and Park M. S. (2012). Lot sizing and quality investment with quality cost analyses for imperfect production and inspection processes with commercial return. Int. J. Production Economics, 140:922-933.
  • Yusuf, Y. B., & Halim, M. S. (2023). Stationary spot welding (ssw) quality improvement using six sigma methodology and a poka yoke jig design. Journal of Engineering Science and Technology, 18(1), 210-226.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-734b99e7-8bae-4372-ab97-578f79b9a5b1
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