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A product is referred to as robust when its performance is consistent. In current product robustness paradigms, robustness is the responsibility of engineering design. Drawings and 3D models should be released to manufacturing after applying all the possible robust design principles. But there are no methods referred for manufacturing to carry and improve product robustness after the design freeze. This paper proposes a process of inducing product robustness at all stages of product development from design release to the start of mass production. A manufacturing strategy of absorbing all obvious variations and an approach of turning variations to cancel one another are defined. Verified the application feasibility and established the robustness quantification method at each stage. The theoretical and actual sensitivity of different parameters is identified as indicators. Theoretical and actual performance variation and accuracy of estimation are established as robustness metric. Manufacturing plan alignment to design, complimenting the design and process sensitivities, countering process mean shifts with tool deviations, higher adjustable assembly tools are enablers to achieve product robustness.
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Tom
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3--12
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
- Engineering Design and Product Development Section Mechanical Engineering Denmark Technical University 2800 Kgs. Lyngby, Denmark
autor
- Technical University of Denmark, Department of Mechanical Engineering, Denmark
autor
- Technical University of Denmark, Department of Mechanical Engineering, Denmark
autor
- Technical University of Denmark, Department of Mechanical Engineering, Denmark
Bibliografia
- [1] Phadke M.S., Quality engineering using robust design. Englewood Cliffs, N.J Prentice Hall, Englewood Cliffs, N.J, ISBN: 0137451679, 1995.
- [2] Saha A., Ray T., Practical robust design optimization using evolutionary algorithms, Journal of Mechanical Design, 133, 10, 101012-101012-19, 2011, doi: 10.1115/1.4004807.
- [3] Ebro M., Howard T.J., Robust design principles for reducing variation in functional performance, Journal of Engineering Design, 27, 1–3, 75–92, 2016, doi: 10.1080/09544828.2015.1103844.
- [4] Ebro M., Howard T.J., Rasmussen J.J., The foundation for robust design: Enabling robustness through kinematic design and design clarity, DS 70: Proceedings of Design 2012, the 12th International Design Conference, Dubrovnik, Croatia, 817–826, 2012.
- [5] El-Midany T.T., El-Baz M.A., Abdelwahed M.S., Improve characteristics of manufactured products using artificial neural network performance prediction model, International Journal of Recent Advances in Mechanical Engineering, 2, 4, 23–34, 2013.
- [6] Zhang C., Liu X., Shi J., Zhu J., Neural softsensor of product quality prediction, 6th World Congress on Intelligent Control and Automation, Dalian, China, 4881–4885, 2006, doi: 10.1109/WCICA.2006.1713312.
- [7] Boorla S.M., Howard T.J., Production monitoring system for understanding product robustness, Advances in Production Engineering & Management, 11, 3, 159–172, 2016, doi: 10.14743/apem2016.3.217.
- [8] Mital A., Desai A., Subramanian A., Mital A., Chapter 9, Product development: a structured approach to consumer product development, design, and manufacture, Elsevier, USA, 2014, doi: 10.1016/B978-0-12-799945-6.00015-6.
- [9] Taguchi G., Clausing D., Robust quality, Harvard Business Review, 68, 1, 65–75, 1990.
- [10] Sullivan L.P., The power of Taguchi methods to impact change in US Companies, Quality Progress, 76– 79, 1987.
- [11] Jaff T., Ivanov A., Manufacturing lead time reduction towards zero-defect manufacturing, Sustainable Design and Manufacturing, 7, 2, 628–640, 2016, doi:sdm14-09.
- [12] Alexopoulos T., Packianather M., A monitoring and data analysis system to achieve zero-defects manufacturing in highly regulated industries, International Conference on Sustainable Design and Manufacturing, pp. 303–313, 2017, Springer, Cham., doi: 10.1007/978-3-319-57078-5 30.
- [13] Chu W.S., Kim M.S., Jang K.H., Song J.H., Rodrigue H., Chun D.M., Min S., From design for manufacturing (DFM) to manufacturing for design (MFD) via hybrid manufacturing and smart factory: a review and perspective of paradigm shift, International Journal of Precision Engineering and Manufacturing-Green Technology, 3, 2, 209–222, 2016, doi: 10.1007/s40684-016-0028-0.
- [14] Taguchi G., Cariapa V., Taguchi on robust technology development: brining quality engineering upstream, The Americal Society of Mechanical Engineers, New York, USA, 1993, doi: 10.1115/1.800288.
- [15] Whitney D.E., Manufacturing by design, Harvard Business Review, 66, 4, 83–91, 1988.
- [16] Murthy B.S., Howard T.J., Product Maturation Guide-a digital simulation outcome, Procedia CIRP, 43, 82–87, 2016, doi: 10.1016/j.procir.2016.02.044.
- [17] Suh N.P., Axiomatic design theory for systems, Research in Engineering Design, 10. 4, 189–209, 1998, doi: 10.1007/s001639870001.
- [18] Doshi J.A., Desai D.A., Role of production part approval process in continuous quality improvement and customer satisfaction, International Journal of Engineering Research in Africa, 22, 22, 174–183, 2016, doi: 10.4028/www.scientific.net/JERA.22.174.
- [19] Attridge A., Williams M., Tennant C., Achieving craftsmanship targets across the UK automotive supply base, through the use of quality maturation tools and processes SAE, Technical Paper, No. 2005- 01-1566, 2005, doi: 10.4271/2005-01-1566.
- [20] Oborski P., Integrated monitoring system of production processes, Management and Production Engineering Review, 7, 4, 86–96, 2016, doi: 10.1515/mper-2016-0039.
- [21] Howard T.J., Eifler T., Pedersen S.N., Göhler S.M., Boorla S.M., Christensen M.E., The Variation Management Framework (VMF): a unifying graphical representation of robust design, Quality Engineering, 2017, doi: 10.1080/08982112.2016.1272121.
- [22] Marianthi G. Ierapetritou, Rohit Ramachandran, Process simulation and data modeling in solid oral drug development and manufacture, Springer New Yark, 2016, doi: 10.1007/978-1-4939-2996-2.
- [23] Boorla S.M., Troldtoft M.E., Eifler T., Howard T.J., Quantifying the robustness of process manufacturing concept – a medical product case study, Advances in Production Engineering & Management, 12, 2, 127– 138, 2017, doi: 10.14743/apem2017.2.245.
- [24] Zawadzki P., Żywicki K., Smart product design and production control for effective mass customization in the Industry 4.0 concept, Management and Production Engineering Review, 7, 3, 105–112, 2016, doi: 10.1515/mper-2016-0030.
- [25] Lee J., Bagheri B., Kao H.A., A cyber-physical systems architecture for industry 4.0-based manufacturing systems, Manufacturing Letters, 3, 18–23, 2015, doi: 10.1016/j.mfglet.2014.12.001.
- [26] Saldivar A.A.F., Goh C.S., Chen W.N., Li Y., Selforganizing tool for smart design with predictive customer needs and wants to realize Industry 4.0, Proceedings of CEC 2016, World Congress on Computational Intelligence, Vancouver, Canada, 2016, doi: 10.1109/CEC.2016.7748366.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
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