OJCM  Vol.11 No.2 , April 2021
A Mini-Review and Perspective on Current Best Practice and Emerging Industry 4.0 Methods for Risk Reduction in Advanced Composites Manufacturing
Abstract: The manufacturing of composite structures is a highly complex task with inevitable risks, particularly associated with aleatoric and epistemic uncertainty of both the materials and processes, as well as the need for in-situ decision-making to mitigate defects during manufacturing. In the context of aerospace composites production in particular, there is a heightened impetus to address and reduce this risk. Current qualification and substantiation frameworks within the aerospace industry define tractable methods for risk reduction. In parallel, Industry 4.0 is an emerging set of technologies and tools that can enable better decision-making towards risk reduction, supported by data-driven models. It offers new paradigms for manufacturers, by virtue of enabling in-situ decisions for optimizing the process as a dynamic system. However, the static nature of current (pre-Industry 4.0) best-practice frameworks may be viewed as at odds with this emerging novel approach. In addition, many of the predictive tools leveraged in an Industry 4.0 system are black-box in nature, which presents other concerns of tractability, interpretability and ultimately risk. This article presents a perspective on the current state-of-the-art in the aerospace composites industry focusing on risk reduction in the autoclave processing, as an example system, while reviewing current trends and needs towards a Composites 4.0 future.
Cite this paper: Crawford, B. , Khayyam, H. and Milani, A. (2021) A Mini-Review and Perspective on Current Best Practice and Emerging Industry 4.0 Methods for Risk Reduction in Advanced Composites Manufacturing. Open Journal of Composite Materials, 11, 31-45. doi: 10.4236/ojcm.2021.112004.

[1]   Strong, A.B. (2008) Fundamentals of Composites Manufacturing: Materials, Methods and Applications. Society of Manufacturing Engineers, Southfield.

[2]   Mazumdar, S.K. (2002) Composites Manufacturing: Materials, Product, and Process Engineering. CRC Press, Boca Raton.

[3]   Mangalgiri, P. (1999) Composite Materials for Aerospace Applications. Bulletin of Materials Science, 22, 657-664.

[4]   Mesogitis, T.S., Skordos, A.A. and Long, A.C. (2014) Uncertainty in the Manufacturing of Fibrous Thermosetting Composites: A Review. Composites Part A: Applied Science and Manufacturing, 57, 67-75.

[5]   Fernlund, G. (2010) Risk Reduction in Composites Processing Using Prototype Data, Process Simulation, and Bayesian Statistics. Composites Part A: Applied Science and Manufacturing, 41, 295-303.

[6]   Potter, K. (2009) Understanding the Origins of Defects and Variability in Composites Manufacture. Proceedings of the International Conference on Composite Materials (ICCM)-17, Edinburgh, 27-31 July 2009.

[7]   Convergent (2018) COMPRO Simulation Software.

[8]   Esi-Group (2018) PAM-RTM—Confidently Build Lightweight Composite Products Free From Defects with Easy-to-Use Composite Simulation Software.

[9]   Revision, G. (2012) Composite Materials Handbook, Volume 3: Polymer Matrix Composites Materials Usage, Design and Analysis (CMH-17). SAE International, Wichita.

[10]   Dynamiccio (2018) Neither C-Suite Nor Millennial Workforce Feel Ready and Confident for Industry 4.0.

[11]   Xu, L., Xu, E. and Li, L. (2018) Industry 4.0: State of the Art and Future Trends. International Journal of Production Research, 8, 2941-2962.

[12]   McKinsey & Company (2013) Game Changers: Five Opportunities for US Growth and Renewal.

[13]   McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D. and Barton, D. (2012) Big Data. The Management Revolution. Harvard Business Review, 90, 60-66.

[14]   Polek, G. (2014) For Boeing and Airbus, Partnerships Proliferate in Asia.

[15]   Black, S. (2017) Composites and Industry 4.0: Where Are We?

[16]   Al-Lami, A. and Hilmer, P. (2016) Novel Applications in Assessing Manufacturing and Assembly of Complex Composite Structures: A Pace towards Industry 4.0 in Composite Manufacturing. Proceedings of the 11th AIRTEC Congress, Munich, 27 October 2016.

[17]   Ayafrudin, M., Alfian, G., Fitriyani, N.L. and Rhee, J. (2018) Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors, 18, 2946-2972.

[18]   Campos, J. (2010) Guest Editorial Special Section on Formal Methods in Manufacturing. IEEE Transactions on Industrial Informatics, 2, 125-126.

[19]   National Science Foundation (2019) Cyber-Physical Systems (CPS).

[20]   Roski, J., Bo-Linn, G.W. and Andrews, T. (2014) Creating Value in health Care through Big Data: Opportunities and Policy Implications. Health Affairs, 33, 1115-1122.

[21]   Johnston, A.A. (1997) An Integrated Model of the Development of Process-Induced Deformation in Autoclave Processing of Composite Structures. Ph.D. Dissertation, University of British Columbia, Vancouver, Kelowna.

[22]   Chen, Y., Wang, L. and Chu, P. (2020) A Recipe Parameter Recommendation System for an Autoclave Process and an Empirical Study. Procedia Manufacturing, 51, 1046-1053.

[23]   Brunton, S.L. and Kutz, J.N. (2019) Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning. Cambridge University Press, Cambridge, 1-35.

[24]   Sacco, C., Radwan, A., Beatty, T., Harik, R. (2020) Machine Learning Based AFP Inspection: A Tool for Characterization and Integration. Proceedings of the SAMPE 2019 Conference, Charlotte, 20-23 May 2019.

[25]   Manohar, K., Hogan, T., Buttrick, J., Banerjee, A., Kutz, J.N. and Brunton, S. (2018) Predicting Shim Gaps in Aircraft Assembly with Machine Learning and Sparse Sensing. Journal of Manufacturing Systems, 48, 87-95.

[26]   Hueber, C., Fischer, G., Schwingshandl, N. and Schledjewski, R. (2009) Production Planning Optimization for Composite Aerospace Manufacturing. International Journal of Production Research, 57, 5857-5873.

[27]   Abdi, F., Surdenas, J., Munir, N., Housner, J. and Keshavanarayana, R. (2009) Computational Approach toward Advanced Composite Material Qualification and Structural Certification. In: Farahmand, B., Ed., Virtual Testing and Predictive Modeling, Springer, Boston, 137-185.

[28]   Abdi, F., Clarkson, E., Godines, C. and DorMohammadi, S. (2016) A-B Basis Allowable Test Reduction Approach and Composite Generic Basis Strength Values. Proceedings of the 18th AIAA Non-Deterministic Approaches Conference, San Diego, 4-8 January 2016.

[29]   Lee, H.S., Rhee, S.Y., Yoon, J.K., Yoo, J.T. and Min, K.J. (2015) Establishment of Aerospace Composite Materials Data Center for Qualification. Composites Research, 6, 402-407.

[30]   Berenberg, B. (2003) AGATE Methodology Proves Its Worth.

[31]   Rodrigues, A.E. and Minceva, M. (2005) Modelling and Simulation in Chemical Engineering: Tools for Process Innovation. Computers & Chemical Engineering, 29, 1167-1183.

[32]   Dobre, T.G. and Sanchez-Marcano, J.G. (2007) Chemical Engineering: Modelling, Simulation and Similitude. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

[33]   Qiu, J., Wu, Q., Ding, G., Xu, Y. and Feng, S. (2016) A Survey of Machine Learning for Big Data Processing. EURASIP Journal on Advances in Signal Processing, 2016, Article No. 85.

[34]   Wuest, T., Weimer, D., Irgens, C. and Thoben, K. (2016) Machine Learning in Manufacturing: Advantages, Challenges, and Applications. Production & Manufacturing Research, 4, 23-45.

[35]   Khan, M.E. and Khan, F. (2012) A Comparative Study of White Box, Black Box and Grey Box Testing Techniques. International Journal of Advanced Computer Science and Applications, 3, 12-25.

[36]   Rudin, C. (2019) Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence, 1, 206-215.

[37]   Khayyam, H., Jamali, A., Bab-Hashidar, A., Esch, T., Ramakrishna, S., Jalili, M., et al. (2020) A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0. IEEE Access, 8, 111381-111393.

[38]   Baker, N., Alexander, F., Bremer, T., Hagberg, A., Kevrekidis, Y., Habib, N., et al. (2019) Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. Office of Scientific and Technical Information, Oak Ridge.

[39]   Iten, R., Metger, T., Wilming, W., del Rio, L. and Renner, R. (2020) Discovering Physical Concepts with Neural Networks. Physical Review Letters, 124, 105-129.

[40]   Zobeiry, N. and Humfeld, K.D. (2020) A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications. Engineering Applications of Artificial Intelligence, 101, Article ID: 104232.

[41]   Zobeiry, N., Van Ee, D.A. and Poursartip, A. (2019) Theory-Guided Machine Learning Composites Processing Modelling for Manufacturability Assessment in Preliminary Design. Proceedings of the NAFEMS 17th World Congress, Quebec City, 17-20 June 2019.

[42]   Lample, G. and Charton, F. (2020) Deep Learning for Symbolic Mathematics. Proceedings of International Conference on Learning Representations, Virtual conference, Addis Ababa, 26 April-1 May 2020.

[43]   Mojsilovic, A. (2019) Introducing AI Explainability 360.

[44]   Google (2019) AI Explainability Whitepaper.

[45]   Riberio, M., Singh, S. and Guestrin, C. (2016) “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 1135-1144.

[46]   Merrick, L. and Taly, A. (2020) The Explanation Game: Explaining Machine Learning Models Using Shapley Values. Proceedings of the International Cross-Domain Conference for Machine Learning and Knowledge Extraction, Dublin, 25-28 August 2020, 17-38.

[47]   Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. and Brendel, W. (2019) ImageNet-Trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness. Proceedings of the International Conference on Learning Representations, New Orleans, LA, 6-9 May 2019, 22.

[48]   Khayyam, H., Naebe, M., Zabihi, O., Zamani, R., Atkiss, S. and Fox, B. (2015) Dynamic Prediction Models and Optimization of (PAN) Stabilization Processes for Production of Carbon Fiber. IEEE Transactions on Industrial Informatics, 11, 887-896.

[49]   Khayyam, H., Jazar, R., Nunna, S., Golkarnarenji, G., Badii, K., Fakhrhoseini, S., et al. (2020) PAN Precursor Fabrication, Applications and Thermal Stabilization Process in Carbon Fiber Production: Experimental and Mathematical Modelling. Progress in Materials Science, 107, 100575-100593.

[50]   Golkarnarenji, G., Naebe, M., Church, J.S., Badii, K., Bab-Hadiashar, A. and Atkiss, S. (2017) Development of a Predictive Model for Study of Skin-Core Phenomenon in Stabilization Process of PAN Precursor. Journal of Industrial and Engineering Chemistry, 49, 46-60.

[51]   Golkarnarenji, G., Naebe, M., Badii, K., Milani, A.S., Jazar, R.N. and Khayyam, H. (2018) Support Vector Regression Modelling and Optimization of Energy Consumption in Carbon Fiber Production Line. Computers & Chemical Engineering, 109, 276-288.

[52]   Ramezankhani, M., Crawford, B., Khayyam, H., Naebe, M., Seethaler, R. and Milani, A.S. (2019) A Multi-Objective Gaussian Process Approach for Optimization and Prediction of Carbonization Process in Carbon Fiber Production under Uncertainty. Advanced Composites and Hybrid Materials, 2, 444-455.

[53]   Golkarnarenji, G., Naebe, M., Badii, K., Milani, A.S, Jamali, A., Bab-Hadiashar, A. and Khayyam, H. (2019) Multi-Objective Optimization of Manufacturing Process in Carbon Fiber Industry Using Artificial Intelligence Techniques. IEEE Access, 7, 67576-67588.

[54]   Brüning, J., Denkena, B., Dittrich, M.A. and Hocke, T. (2017) Machine Learning Approach for Optimization of Automated Fiber Placement Processes. Procedia CIRP, 66, 74-78.

[55]   Carlone, P., Aleksendric, D., Rubino, F. and Cirovic, V. (2018) Artificial Neural Networks in Advanced Thermoset Matrix Composite Manufacturing. Proceedings of the 3rd International Conference on the Industry 4.0 Model for Advanced Manufacturing, Belgrade, 5-7 June 2018, 78-88.

[56]   Schmitt, R., Orth, A. and Tilo, P. (2006) Feasible Production of Fiber-Reinforced Composites through Inline Inspection with Machine Vision. Proceedings of the IMEKO XVIII World Congress and IV Brazilian Congress of Metrology, Rio de Janeiro, 17-22 September 2006.

[57]   Hernán, D., Loaiza., B.H., Caicedo, E., Ibarra-Castanedo, C., Bendada, A.H. and Maldague, X. (2009) Defect Characterization in Infrared Non-Destructive Testing with Learning Machines. NDT & E International, 42, 630-643.

[58]   Fernandes, H., Zhang, H., Figueiredo, A., Malheiros, F., Ignacio, L.H., Sfarra, S., et al, (2018) Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts. Sensors, 18, 288-298.