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OPERATOR 4.0 FOR HYBRID MANUFACTURING

Published online by Cambridge University Press:  19 June 2023

Kenton Blane Fillingim*
Affiliation:
Oak Ridge National Laboratory
Thomas Feldhausen
Affiliation:
Oak Ridge National Laboratory
*
Fillingim, Kenton Blane, Oak Ridge National Laboratory, United States of America, fillingimkb@ornl.gov

Abstract

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Hybrid manufacturing, a combination of additive and subtractive manufacturing capabilities in one system, has recently become a more viable production option across several industries. Although current hybrid manufacturing research covers a broad range of topics, there is a lack of focus on how this new technology impacts both the designer and the operator of hybrid systems. This paper identifies areas of literature across design theory and Industry/Operator 4.0 research efforts and presents a path for applying this research to hybrid manufacturing users. The unique relationship between operator and designer is highlighted as they learn new strategies and develop new intuitive judgements over time to become the first experienced/expert users of hybrid manufacturing. The potential impact of excessive cognitive workload due to the novel combination of processes is discussed. This paper begins a critical discussion about proper knowledge transfer to other hybrid designers and operators, as well as towards efforts of monitoring, inspecting, and automating hybrid manufacturing processes.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Abdelall, E. S., Frank, M. C. & Stone, R. T. 2018a. Design for manufacturability-based feedback to mitigate design fixation. Journal of Mechanical Design, 140, 091701. https://doi.org/10.1115/1.4040424CrossRefGoogle Scholar
Abdelall, E. S., Frank, M. C. & Stone, R. T. 2018b. A study of design fixation related to additive manufacturing. Journal of Mechanical Design, 140, 041702. https://doi.org/10.1115/1.4039007CrossRefGoogle Scholar
Abdillah, H. & Ulikaryani, U. Hybrid Manufacturing and Rapid Prototyping in Metal Casting Industry: A Review. Proceedings of the 2nd International Conference of Science and Technology for the Internet of Things, ICSTI 2019, September 3rd, Yogyakarta, Indonesia, 2020. http://dx.doi.org/10.4108/eai.20-9-2019.2290957CrossRefGoogle Scholar
Adam, G. A. & Zimmer, D. 2014. Design for Additive Manufacturing—Element transitions and aggregated structures. CIRP Journal of Manufacturing Science and Technology, 7, 2028. https://doi.org/10.1016/j.cirpj.2013.10.001CrossRefGoogle Scholar
Biondi, F. N., Saberi, B., Graf, F., Cort, J., Pillai, P. & Balasingam, B. 2023. Distracted worker: using pupil size and blink rate to detect cognitive load during manufacturing tasks. Applied ergonomics, 106, 103867. https://doi.org/10.1016/j.apergo.2022.103867CrossRefGoogle ScholarPubMed
Blösch-Paidosh, A. & Shea, K. Design Heuristics for Additive Manufacturing. 21st International Conference on Engineering Design (ICED17), 21–25 August 2017 Vancouver, Canada.Google Scholar
Blösch-Paidosh, A. & Shea, K. 2019. Design Heuristics for Additive Manufacturing Validated Through a User Study. Journal of Mechanical Design, 141, 18. https://doi.org/10.1115/1.4041051CrossRefGoogle Scholar
Boivie, K., Dolinsek, S. & Homar, D. Hybrid manufacturing: integration of additive technologies for competitive production of complex tools and products. Proceedings of the 15th International Research/Expert Conference: “Trends in the Development of Machinery and Associated Technology” TMT, Prague, Czech Republic 12–18 September 2011.Google Scholar
Bracken Brennan, J., Miney, W. B., Simpson, T. W. & Jablokow, K. W. Manufacturing Fixation in Design: Exploring the Effects of Manufacturing Assumptions on Design Ideas. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2021. American Society of Mechanical Engineers, V006T06A029. https://doi.org/10.1115/DETC2021-70361CrossRefGoogle Scholar
Capraro, V. & Cococcioni, G. 2015. Social setting, intuition and experience in laboratory experiments interact to shape cooperative decision-making. Proceedings of the Royal Society B: Biological Sciences, 282, 20150237. https://doi.org/10.1098/rspb.2015.0237CrossRefGoogle ScholarPubMed
Carvalho, A. V., Chouchene, A., Lima, T. M. & Charrua-Santos, F. 2020. Cognitive manufacturing in industry 4.0 toward cognitive load reduction: A conceptual framework. Applied System Innovation, 3, 55. https://doi.org/10.3390/asi3040055CrossRefGoogle Scholar
Corporation, I. 2017. Cognitive Manufacturing: An Overview and Four Applications that are Transforming Manufacturing Today. Armonk, NY, USA.Google Scholar
Dávila, J. L., Neto, P. I., Noritomi, P. Y., Coelho, R. T. & da Silva, J. V. L. 2020. Hybrid manufacturing: a review of the synergy between directed energy deposition and subtractive processes. The International Journal of Advanced Manufacturing Technology, 110, 33773390. https://doi.org/10.1007/s00170-020-06062-7CrossRefGoogle Scholar
Dezaki, M. L., Serjouei, A., Zolfagharian, A., Fotouhi, M., Moradi, M., Ariffin, M. & Bodaghi, M. 2022. A Review on Additive/Subtractive Hybrid Manufacturing of Directed Energy Deposition (DED) Process. Advanced Powder Materials, 100054. https://doi.org/10.1016/j.apmate.2022.100054CrossRefGoogle Scholar
Duffy, V. G. Impact of an intelligent tutor on risk and sound perception in CNC machining. IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 99CH37028), 1999. IEEE, 1091-1094. https://dx.doi.org/10.1109/ICSMC.1999.814245CrossRefGoogle Scholar
Elbanna, S., Child, J. & Dayan, M. 2013. A model of antecedents and consequences of intuition in strategic decision-making: Evidence from Egypt. Long Range Planning, 46, 149176. https://doi.org/10.1016/j.lrp.2012.09.007CrossRefGoogle Scholar
Fantini, P., Palasciano, C. & Taisch, M. 2015. Back to intuition: Proposal for a performance indicators framework to facilitate eco-factories management and benchmarking. Procedia Cirp, 26, 16. https://doi.org/10.1016/j.procir.2014.07.099CrossRefGoogle Scholar
Feldhausen, T., Heinrich, L., Saleeby, K., Burl, A., Post, B., MacDonald, E., Saldana, C. & Love, L. 2022a. Review of Computer-Aided Manufacturing (CAM) Strategies for Hybrid Directed Energy Deposition. Additive Manufacturing, 102900. https://doi.org/10.1016/j.addma.2022.102900CrossRefGoogle Scholar
Feldhausen, T., Kannan, R., Raghavan, N., Saleeby, K., Kurfess, T. & Nandwana, P. 2022b. Investigation of interfacial structures for hybrid manufacturing. Materials Letters, 307, 131040. https://doi.org/10.1016/j.matlet.2021.131040CrossRefGoogle Scholar
Feldhausen, T., Saleeby, K. & Kurfess, T. 2021. Spinning the digital thread with hybrid manufacturing. Manufacturing Letters, 29, 1518. https://doi.org/10.1016/j.mfglet.2021.05.003CrossRefGoogle Scholar
Fillingim, K. B. & Fu, K. 2022. Framework for the Evolution of Heuristics in Advanced Manufacturing. Journal of Mechanical Design, 145, 011401. https://doi.org/10.1115/1.4055622CrossRefGoogle Scholar
Fillingim, K. B., Nwaeri, R. O., Paredis, C. J., Rosen, D. & Fu, K. 2020. Examining the effect of design for additive manufacturing rule presentation on part redesign quality. Journal of Engineering Design, 31, 427460. https://doi.org/10.1080/09544828.2020.1789569CrossRefGoogle Scholar
Fraboni, F., Gualtieri, L., Millo, F., De Marchi, M., Pietrantoni, L. & Rauch, E. Human-Robot Collaboration During Assembly Tasks: The Cognitive Effects of Collaborative Assembly Workstation Features. Congress of the International Ergonomics Association, 2021. Springer, 242249. https://doi.org/10.1007/978-3-030-74614-8_29CrossRefGoogle Scholar
Fu, K. K., Yang, M. C. & Wood, K. L. 2016. Design principles: Literature review, analysis, and future directions. Journal of Mechanical Design, 130 (10). https://doi.org/10.1115/1.4034105Google Scholar
Gershwin, S. B. 2018. The future of manufacturing systems engineering. International Journal of Production Research, 56, 224237. https://doi.org/10.1080/00207543.2017.1395491CrossRefGoogle Scholar
Gómez, J. A. G. Simulation as an intuition building tool for factory physics. Proceedings of the 2007 Summer Computer Simulation Conference, 2007. 1–8.Google Scholar
Gualtieri, L., Fraboni, F., Marchi, M. D. & Rauch, E. Evaluation of Variables of Cognitive Ergonomics in Industrial Human-Robot Collaborative Assembly Systems. Congress of the International Ergonomics Association, 2021. Springer, 266273. https://doi.org/10.1007/978-3-030-74614-8_32CrossRefGoogle Scholar
Hart, S. G. NASA-task load index (NASA-TLX); 20 years later. Proceedings of the human factors and ergonomics society annual meeting, 2006. Sage publications Sage CA: Los Angeles, CA, 904908.CrossRefGoogle Scholar
Hopp, W. J. & Spearman, M. L. 2011. Factory physics, Waveland Press.Google Scholar
Hsieh, S.-J. & Li, Q. Lessons Learned from an Intelligent Tutoring System for Computer Numerical Control Programming (CNC Tutor). 2018 ASEE Annual Conference & Exposition, 2018. https://dx.doi.org/10.18260/1-2--30762. https://peer.asee.org/30762CrossRefGoogle Scholar
Jeng, J.-Y. & Lin, M.-C. 2001. Mold fabrication and modification using hybrid processes of selective laser cladding and milling. Journal of Materials Processing Technology, 110, 98103. https://doi.org/10.1016/S0924-0136(00)00850-5CrossRefGoogle Scholar
Jiménez, A., Bidare, P., Hassanin, H., Tarlochan, F., Dimov, S. & Essa, K. 2021. Powder-based laser hybrid additive manufacturing of metals: A review. The International Journal of Advanced Manufacturing Technology, 114, 6396. https://doi.org/10.1007/s00170-021-06855-4CrossRefGoogle Scholar
Jones, J. B., McNutt, P., Tosi, R., Perry, C. & Wimpenny, D. I. 2012. Remanufacture of turbine blades by laser cladding, machining and in-process scanning in a single machine.Google Scholar
Joshi, A. & Anand, S. 2017. Geometric complexity based process selection for hybrid manufacturing. Procedia Manufacturing, 10, 578589. https://doi.org/10.1016/j.promfg.2017.07.056CrossRefGoogle Scholar
Kaasinen, E., Schmalfuß, F., Özturk, C., Aromaa, S., Boubekeur, M., Heilala, J., Heikkilä, P., Kuula, T., Liinasuo, M. & Mach, S. 2020. Empowering and engaging industrial workers with Operator 4.0 solutions. Computers & Industrial Engineering, 139, 105678. https://doi.org/10.1016/j.cie.2019.01.052CrossRefGoogle Scholar
Kannan, R., Feldhausen, T., Saleeby, K. & Nandwana, P. 2022. Effect of humidity of build chamber in hybrid manufacturing systems on part performance. Manufacturing Letters, 32, 3943. https://doi.org/10.1016/j.mfglet.2022.02.005CrossRefGoogle Scholar
Kaufeld, M. & Nickel, P. Level of robot autonomy and information aids in human-robot interaction affect human mental workload–an investigation in virtual reality. International Conference on Human-Computer Interaction, 2019. Springer, 278291. https://doi.org/10.1007/978-3-030-22216-1_21CrossRefGoogle Scholar
Khamaisi, R. K., Brunzini, A., Grandi, F., Peruzzini, M. & Pellicciari, M. 2022. UX assessment strategy to identify potential stressful conditions for workers. Robotics and Computer-Integrated Manufacturing, 78, 102403. https://doi.org/10.1016/j.rcim.2022.102403CrossRefGoogle Scholar
Korherr, P., Kanbach, D. K., Kraus, S. & Jones, P. 2022. The role of management in fostering analytics: the shift from intuition to analytics-based decision-making. Journal of Decision Systems, 117. https://doi.org/10.1080/12460125.2022.2062848Google Scholar
Lagomarsino, M., Lorenzini, M., De Momi, E. & Ajoudani, A. 2022. An Online Framework for Cognitive Load Assessment in Industrial Tasks. Robotics and Computer-Integrated Manufacturing, 78, 102380. https://doi.org/10.1016/j.rcim.2022.102380CrossRefGoogle Scholar
Leahy, K., Daly, S. R., McKilligan, S. & Seifert, C. M. 2020. Design fixation from initial examples: Provided versus self-generated ideas. Journal of Mechanical Design, 142, 101402. https://doi.org/10.1115/1.4046446CrossRefGoogle Scholar
Liebowitz, J., Chan, Y., Jenkin, T., Spicker, D., Paliszkiewicz, J. & Babiloni, F. 2019. If numbers could “feel”: How well do executives trust their intuition? VINE Journal of Information and Knowledge Management Systems. 49, 531545. https://doi.org/10.1108/VJIKMS-12-2018-0129CrossRefGoogle Scholar
Lorenz, K. A., Jones, J. B., Wimpenny, D. I. & Jackson, M. R. 2015. A review of hybrid manufacturing. 2014 International Solid Freeform Fabrication Symposium. University of Texas at Austin. https://hdl.handle.net/2152/89311Google Scholar
Mangler, J., Diwol, K., Etz, D., Rinderle-Ma, S., Ferscha, A., Reiner, G., Kastner, W., Bougain, S., Pollak, C. & Haslgrübler, M. 2021. Sustainability Through Cognition Aware Safety Systems--Next Level Human-Machine-Interaction. arXiv preprint arXiv:2110.07003. https://doi.org/10.48550/arXiv.2110.07003CrossRefGoogle Scholar
Mogessie, M., Wolf, S. D., Barbosa, M., Jones, N. & McLaren, B. M. Work-in-Progress—A Generalizable Virtual Reality Training and Intelligent Tutor for Additive Manufacturing. 2020 6th international conference of the immersive learning research network (iLRN), 2020. IEEE, 355358. https://doi.org/10.23919/iLRN47897.2020.9155119CrossRefGoogle Scholar
Moreno, D. P., Blessing, L. T., Yang, M. C., Hernández, A. A. & Wood, K. L. 2016. Overcoming design fixation: Design by analogy studies and nonintuitive findings. AI EDAM, 30, 185199. https://doi.org/10.1017/S0890060416000068Google Scholar
Müller, J. 2020. Enabling Technologies for Industry 5.0. European Commission, 810. https://dx.doi.org/10.2777/082634Google Scholar
Newman, S. T., Zhu, Z., Dhokia, V. & Shokrani, A. 2015. Process planning for additive and subtractive manufacturing technologies. CIRP annals, 64, 467470. https://doi.org/10.1016/j.cirp.2015.04.109CrossRefGoogle Scholar
Nowotny, S., Muenster, R., Scharek, S. & Beyer, E. 2010. Integrated laser cell for combined laser cladding and milling. Assembly Automation. https://doi.org/10.1108/01445151011016046CrossRefGoogle Scholar
Paige, M. A., Fillingim, K. B., Murphy, A. R., Song, H., Reichling, C. J. & Fu, K. 2021. Examining the effects of mood on quality and feasibility of design outcomes. International Journal of Design Creativity and Innovation, 9, 79102. https://doi.org/10.1080/21650349.2021.1890228CrossRefGoogle Scholar
Pragana, J., Sampaio, R., Bragança, I., Silva, C. & Martins, P. 2021. Hybrid metal additive manufacturing: A state–of–the-art review. Advances in Industrial and Manufacturing Engineering, 2, 100032. https://doi.org/10.1016/j.aime.2021.100032CrossRefGoogle Scholar
Rand, D. G., Peysakhovich, A., Kraft-Todd, G. T., Newman, G. E., Wurzbacher, O., Nowak, M. A. & Greene, J. D. 2014. Social heuristics shape intuitive cooperation. Nature communications, 5, 112. https://doi.org/10.1038/ncomms4677CrossRefGoogle ScholarPubMed
Reichler, A.-K., Gerbers, R., Falkenberg, P., Türk, E., Dietrich, F., Vietor, T. & Dröder, K. 2019. Incremental Manufacturing: Model-based part design and process planning for Hybrid Manufacturing of multi-material parts. Procedia Cirp, 79, 107112. https://doi.org/10.1016/j.procir.2019.02.020CrossRefGoogle Scholar
Ren, L., Panackal Padathu, A., Ruan, J., Sparks, T. E. & Liou, F. W. 2006. Three Dimensional Die Repair Using a Hybrid Manufacturing System. Proceedings of the 17th Annual Solid Freeform Fabrication Symposium, Aug 2006 Austin, TX. University of Texas at Austin, 5159.Google Scholar
Romero, D. & Stahre, J. 2021. Towards the resilient operator 5.0: the future of work in smart resilient manufacturing systems. Procedia CIRP, 104, 10891094. https://doi.org/10.1016/j.procir.2021.11.183CrossRefGoogle Scholar
Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., Fast-Berglund, Å. & Gorecky, D. Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. proceedings of the international conference on computers and industrial engineering (CIE46), Tianjin, China, 2016. 2931.Google Scholar
Ruppert, T., Jaskó, S., Holczinger, T. & Abonyi, J. 2018. Enabling technologies for operator 4.0: A survey. Applied sciences, 8, 1650. https://doi.org/10.3390/app8091650CrossRefGoogle Scholar
Ryan, M. 2022. Immersive Virtual Reality Error Management Training for CNC Machining Setup Procedures. Rochester Institute of Technology. https://www.proquest.com/dissertations-theses/immersive-virtual-reality-error-management/docview/2693712397/se-2?accountid=26379CrossRefGoogle Scholar
Saleeby, K., Feldhausen, T., Love, L. & Kurfess, T. 2020. Rapid retooling for emergency response with hybrid manufacturing. Smart and Sustainable Manufacturing Systems, 4. https://doi.org/10.1520/SSMS20200050CrossRefGoogle Scholar
Schauer, A. M., Fillingim, K. B. & Fu, K. 2022a. Impact of Timing in the Design Process on Students’ Application of Design for Additive Manufacturing Heuristics. Journal of Mechanical Design, 144, 062301. https://doi.org/10.1115/1.4053281CrossRefGoogle Scholar
Schauer, A. M., Fillingim, K. B., Pavleszek, A., Chen, M. & Fu, K. 2022b. Comparing the effect of virtual and in-person instruction on students’ performance in a design for additive manufacturing learning activity. Research in Engineering Design, 33, 385394. https://doi.org/10.1007/s00163-022-00399-8CrossRefGoogle Scholar
Schmitz, T., Cornelius, A., Dvorak, J., Nazario, J., Betters, E., Corson, G., Smith, S., Blue, C., Harmon, J. & Morrison, M. 2022. America's Cutting Edge CNC machining and metrology training. Manufacturing Letters, 33. https://doi.org/10.1016/j.mfglet.2022.07.113CrossRefGoogle Scholar
Sinclair, M. 2010. Misconceptions about intuition. Psychological Inquiry, 21, 378386. https://doi.org/10.1080/1047840X.2010.523874CrossRefGoogle Scholar
Soffel, F., Eisenbarth, D., Hosseini, E. & Wegener, K. 2021. Interface strength and mechanical properties of Inconel 718 processed sequentially by casting, milling, and direct metal deposition. Journal of Materials Processing Technology, 291, 117021. https://doi.org/10.1016/j.jmatprotec.2020.117021CrossRefGoogle Scholar
Standridge, C. R. How factory physics helps simulation. Proceedings of the 2004 Winter Simulation Conference, 2004., 2004. IEEE, 11031108. https://doi.org/10.1109/WSC.2004.1371435CrossRefGoogle Scholar
Thien, A., Saldana, C. J., Feldhausen, T. & Kurfess, T. IoT Devices and Applications for Wire-Based Hybrid Manufacturing Machine Tools. International Manufacturing Science and Engineering Conference, 2020. American Society of Mechanical Engineers, V001T01A051. https://doi.org/10.1115/MSEC2020-8393CrossRefGoogle Scholar
Thorvald, P. 2011. Presenting information in manual assembly. © Peter Thorvald.Google Scholar
Thorvald, P., Lindblom, J. & Andreasson, R. 2017. CLAM–A method for cognitive load assessment in manufacturing. Advances in Manufacturing Technology XXXI, 114119. https://doi.org/10.3233/978-1-61499-792-4-114Google Scholar
Thorvald, P., Lindblom, J. & Andreasson, R. 2019. On the development of a method for cognitive load assessment in manufacturing. Robotics and Computer-Integrated Manufacturing, 59, 252266. https://doi.org/10.1016/j.rcim.2019.04.012CrossRefGoogle Scholar
Tong, S. & Nie, Y. 2022. Measuring Designers’ Cognitive Load for Timely Knowledge Push via Eye Tracking. International Journal of Human–Computer Interaction, 114. https://doi.org/10.1080/10447318.2022.2057898Google Scholar
Torn, R.-J., Chemweno, P., Vaneker, T. & Arastehfar, S. 2021. Towards a Structured Decision-Making Framework for Automating Cognitively Demanding Manufacturing Tasks. Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems. Springer. https://doi.org/10.1007/978-3-030-90700-6_21Google Scholar
Urbanic, R. & Hedrick, R. 2016. Fused deposition modeling design rules for building large, complex components. Computer-Aided Design and Applications, 13, 348368. https://doi.org/10.1080/16864360.2015.1114393CrossRefGoogle Scholar
Weflen, E. & Frank, M. C. 2021. Hybrid additive and subtractive manufacturing of multi-material objects. Rapid Prototyping Journal. https://doi.org/10.1108/RPJ-06-2020-0142CrossRefGoogle Scholar
Yamazaki, T. 2016. Development of a Hybrid Multi-tasking Machine Tool: Integration of Additive Manufacturing Technology with CNC Machining. Procedia CIRP, 42, 8186. https://doi.org/10.1016/j.procir.2016.02.193CrossRefGoogle Scholar
Zhang, W., Soshi, M. & Yamazaki, K. 2020. Development of an additive and subtractive hybrid manufacturing process planning strategy of planar surface for productivity and geometric accuracy. International Journal of Advanced Manufacturing Technology, 109, 14791491. https://doi.org/10.1007/s00170-020-05733-9CrossRefGoogle Scholar
Zhu, Z. 2013. A process planning approach for hybrid manufacture of prismatic polymer components. University of Bath.Google Scholar
Zhu, Z., Dhokia, V. G., Nassehi, A. & Newman, S. T. 2013. A review of hybrid manufacturing processes–state of the art and future perspectives. International Journal of Computer Integrated Manufacturing, 26, 596615. https://doi.org/10.1080/0951192X.2012.749530CrossRefGoogle Scholar
Zimmer, M., Al-Yacoub, A., Ferreira, P., Hubbard, E.-M. & Lohse, N. 2022. Experimental study to investigate mental workload of local vs remote operator in human-machine interaction. Production & Manufacturing Research, 10, 410427. https://doi.org/10.1080/21693277.2022.2090458CrossRefGoogle Scholar