Abstract
Industry 4.0 capabilities have enabled manufacturers to collect and analyze smart manufacturing data across a broad array of diverse domains including but not limited to scheduling, production, maintenance, process, and quality. This development necessarily proceeds in a logical sequence by which first the organization develops the capability to capture and store this data and, at best concurrently but frequently lagging, develops and refines the competencies to analyze and effectively utilize it. This research presents an applied case study in surface mount technology (SMT) manufacture of printed circuit board (PCB) assemblies. Parametric data captured at the solder paste inspection (SPI) station is analyzed with machine learning models to identify patterns and relationships that can be harnessed to preempt electrical defects at downline inspection stations. This project is enabled by the recent conclusion of an Industrial Internet of Things (IIoT) capability enhancement at the manufacturing facility from which the data is drawn and is the logical next step in achieving value from the newly-available smart manufacturing data. The operationalization of this analysis is contextualized within the product-resource-order-staff architecture (PROSA) of a Holonic Manufacturing Systems (HMS). A Trigger Holon is nested between the Resource Holarchy and Product Holarchy that, at scheduling, distributes implementation instructions for the defect-prediction model. The Defect Prediction Holon is containerized within the Product Holarchy and provides instructions for corrective action when the model flags a record as exhibiting increased probability of a downline electrical defect.
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Notes
- 1.
In a hierarchical control architecture, levels of control have specific functionality with strict relationships constraining control decisions from one level to the next.
- 2.
In a heterarchical control architecture, distributed locally autonomous entities cooperate with each other directly but without higher-echelon oversight or control.
- 3.
A holarchy is a system of holons that cooperate to achieve a goal or objective.
References
Koestler, A.: The Ghost in the Machine. Macmillan, New York (1968)
Valckenaers, P., Van Brussel, H.: Laws of the artificial. In: Design for the Unexpected, pp. 27–40. Elsevier (2016)
Valckenaers, P., Van Brussel, H.: Holonic manufacturing systems. In: Design for the Unexpected, pp. 41–76. Elsevier (2016)
Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., Peeters, P.: Reference architecture for holonic manufacturing systems: PROSA. Comput. Ind. 37(3), 255–274 (1998)
Bernard, D., Krastev, E.: Modern 2D X-ray tackles BGA defects. SMT Surf. Mt. Technol. Mag. 22(7), 22–24 (2008)
Do Peng, S., Nam, H.: Void defect detection in ball grid array X-ray images using a new blob filter. J. Zhejiang Univ. Sci. C 13(11), 840–849 (2012)
Wang, Y., Wang, M., Zhang, Z.: Optik microfocus X-ray printed circuit board inspection system. Opt. - Int. J. Light Electron Opt. 125(17), 4929–4931 (2014)
Castellanos, A., Feng, Z., Geiger, D., Kurwa, M.: Head-in-pillow X-ray inspection. SMT Surf. Mt. Technol. Mag. 29(5), 16–29 (2014)
Sumimoto, T., et al.: Detection of defects of BGA by tomography imaging. J. Syst. Cybern. Inform. 3(4), 10–14 (2005)
Hui, T.W., Pang, G.K.H.: Solder paste inspection using region-based defect detection. Int. J. Adv. Manuf. Technol. 42(7–8), 725–734 (2009)
Kuo, C.H., Yang, F.C., Wing, J.J., Yang, C.K.: Construction of 3D solder paste surfaces using multi-projection images. Int. J. Adv. Manuf. Technol. 31(5–6), 509–519 (2006)
Chu, M.H., Pang, G.K.H.: Solder paste inspection by special led lighting for SMT manufacturing of printed circuit boards. In: IFAC, vol. 8, no. Part 1 (2007)
Li, X.P., Xia, J.M., Zhou, M.B., Ma, X., Zhang, X.P.: Solder volume effects on the microstructure evolution and shear fracture behavior of ball grid array structure Sn-3.0 Ag-0.5 Cu solder interconnects. J. Electron. Mater. 40(12), 2425–2435 (2011)
Yang, D., Cai, J., Wang, Q., Li, J., Hu, Y., Li, L.: IMC growth and shear strength of Sn–Ag–Cu/Co–P ball grid array solder joints under thermal cycling. J. Mater. Sci.: Mater. Electron. 26(2), 962–969 (2014)
Pandher, R., Jodhan, N., Raut, R., Liberatore, M.: Head-in-pillow defect - Role of the solder ball alloy. In: 2010 12th Electronics Packaging Technology Conference, EPTC 2010, pp. 151–156 (2010)
Scalzo, M.: Addressing the Challenge of Head-In-Pillow Defects in Electronics Assembly. In: APEX EXPO Technical Conference (2009)
Zhao, Z.: Effects of package warpage on head-in-pillow defect. Mater. Trans. 56(7), 1037–1042 (2015)
Chen, C., et al.: Characterization of after-reflow misalignment on Head-in-Pillow defect in BGA assembly. In: 2014 15th International Conference on Electronic Packaging Technology, pp. 1177–1180 (2014)
Wettermann, B.: Top 5 BGA challenges to overcome. SMT Surf. Mt. Technol. Mag. 32(9), 25–29 (2017)
LaCasse, P.M., Otieno, W., Maturana, F.P.: A hierarchical, fuzzy inference approach to data filtration and feature prioritization in the connected manufacturing enterprise. J. Big Data 5(1), 45 (2018)
Omwando, T.A., Otieno, W.A., Farahani, S., Ross, A.D.: A bi-level fuzzy analytical decision support tool for assessing product remanufacturability. J. Clean. Prod. 174, 1534–1549 (2018)
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LaCasse, P.M., Otieno, W., Maturana, F.P. (2019). Operationalization of a Machine Learning and Fuzzy Inference-Based Defect Prediction Case Study in a Holonic Manufacturing System. In: Mařík, V., et al. Industrial Applications of Holonic and Multi-Agent Systems. HoloMAS 2019. Lecture Notes in Computer Science(), vol 11710. Springer, Cham. https://doi.org/10.1007/978-3-030-27878-6_8
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