Title: A Systems Solution to Quality Escapes in Composites Manufacturing
Authors: Scott Blake
DOI: 10.33599/nasampe/c.24.0295
Abstract: Despite ongoing procedural and training modifications for manual tasks on the composites manufacturing floor, limiting factors inherent to the human condition continue to induce costly quality escapes. Floor operators naturally become distracted or fatigued. Expectation bias may cause them to be less vigilant, for example, in checking that the next pattern in a kit correctly matches the layup sequence. With today’s intensifying schedule and cost pressures, operators may also be more likely to take shortcuts, such as prestamping or stamping behind a whole series of steps. The resulting quality escapes can and have resulted in component failures in the field, including some that have led to loss of life. Rather than seeking new ways to change human behavior and improve performance, this paper presents a systems solution to quality control comprised of (1) electronic process control and (2) AI-enabled automatic inspection. Electronic process control provides a digital solution that guides operators to perform the right task in the right sequence at the right time. It includes electronic buyoffs that preclude prestamping or stamping behind. Automatic inspection replaces error-prone manual inspection with machine vision image capture and analysis that achieve near-100% inspection accuracy. The paper describes this digital approach to quality control, the supporting technology, and a use case in which a composites fabricator transitioned from traditional manual quality control to the proposed digital quality control.
References: 1. M. Garcia, “Boeing 737-9 Max Grounding: FAA Leaves No Room for ‘Quality Escapes.’” Forbes 12 January 2024. https://www.forbes.com/sites/marisagarcia/2024/01/12/boeing-737-9-max-grounding-faa-leaves-no-room-for-quality-escapes/?sh=2dd60b451681. 2. P. L. Stumpff, Case histories, in: D.B. Miracle, S.L. Donaldson (Eds.), ASM Handbook, Volume 21: Composites, ASM International, Materials Park, 2001, pp. 988–993. DOI: 10.31399/asm.hb.v21.a0003467. 3. J. Ransom, E. Glaessgen, I. Raju, N. Knight and J. Reeder, “Lessons Learned from Recent Failure and Incident Investigations of Composite Structures,” 49th AIAA/ASME/ASCE/ AHS/ASC Structures, Structural Dynamics, and Materials Conference, April 2008. DOI: 10.2514/6.2008-2317. 4. N. Zimmermann, P.H. Wang, “A review of failure modes and fracture analysis of aircraft composite materials,” Engineering Failure Analysis 115 (2020) 104692. DOI: 10.1016/j.engfailanal.2020.104692. 5. C. Richard, “Siemens Gamesa to Focus on European Onshore Wind and Fixing 5.X Turbine Platform.” WindPower Monthly, 21 November 2023. 6. P. Veers, C. Bottasso, L. Manuel, J. Naughton, L. Pao, J. Paquette, A. Robertson, M. Robinson, S. Ananthan, T. Barlas, A. Bianchini, H. Bredmose, S. Horcas, J. Keller, H. Madsen, J. Manwell, P. Moriarty, S. Nolet and J. Rinker, “Grand Challenges in the Design, Manufacture, and Operation of Future Wind Turbine Systems.” Wind Energy Science (2023) 8:1071-1131. DOI: 10.5194/wes-8-1071-2023. 7. L. Mishnaevsky, Jr., “Root Causes and Mechanisms of Failure of Wind Turbine Blades: Overview.” Materials (2022) 15(9), 2959. DOI: 10.3390/ma15092959. 8. M. Leong, L. Overgaard, O. Thomsen, E. Lund, I. Daniel, “Investigation of Failure Mechanisms in GFRP Sandwich Structures with Face Sheet Wrinkle Defects Used for Wind Turbine Blades.” Compos. Struct. (2012) 94: 768-78. 9. Stephen Johnson, Interview by Karen Mason, 16 January 2024. 10. The FOD Control Corporation, “Controlling FOD in a Manufacturing Facility,” self-published article, https://www.fodcontrol.com/controlling-fod-in-a-manufacturing-facility/, accessed June 9, 2023 11. U. Khan, J. Iqbal and M. Khan, “Automatic Inspection System Using Machine Vision.” 34th Applied Imagery and Pattern Recognition Workshop (APR’05) (2005) 6-217. DOI: 10.1109/AIPR.2005.20. 12. S. Blake and A. Atai, “Accelerating In-Process Automatic Inspection Engineering: Removing Inhibitors to Rapid Machine Learning-Based Application Development.” SAMPE Conference January 2021. DOI:10.33599/nasampe/s.21.0472 13. G. Nguyen, S. Dlugolinsky, M. Bobák et al., “Machine Learning and Deep Learning Frameworks and Libraries for Large-Scale Data Mining: A Survey,” Artif Intell Rev 52, 77–124 (2019). DOI: 10.1007/s10462-018-09679-z. 14. S. Fahle, C. Prinz and B. Kuhlenkötter, “Systematic Review on Machine Learning (ML) for Manufacturing Processes – Identifying Artificial Intelligence (AI) Methods for Field Application,” 53rd CIRP Conference on Manufacturing Systems Proceedings 93 (2020), 413–418.
Conference: CAMX 2024 | San Diego CA
Publication Date: 2024/9/9
SKU: TP24-0000000295
Pages: 15
Price: $30.00
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