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The International Journal of Advanced Manufacturing Technology
Erschienen in: The International Journal of Advanced Manufacturing Technology 5-6/2021

02.04.2021 | ORIGINAL ARTICLE

Smart technology–driven aspects for human-in-the-loop smart manufacturing

verfasst von: Jung-Sing Jwo, Ching-Sheng Lin, Cheng-Hsiung Lee

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 5-6/2021

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Abstract

Industry 4.0 has led to paradigm shifts and changes for planning and developing manufacturing processes. To successfully embrace this revolution and confront the numerous challenges, manufacturing enterprises have to cope with the need for technological advancement and provide sustainable training and education for their workforce. This great transformation affects not only the integration of the digital and physical environments but, most importantly, also the relationships between humans and manufacturing sites. In this paper, we take into account the human-in-the-loop for digitalization challenges and present a 3I (Intellect, Interaction, and Interface) aspect for factories to increase the adoption of smart technologies toward the smart manufacturing vision. The Intellect aspect aims to add knowledge to the manufacturing equipment. The Interaction aspect targets at the collaboration between humans and manufacturing equipment. The Interface aspect explores appropriate means for humans to exploit the intelligence of technologies for the communication with the manufacturing equipment. In addition to the concept-related propositions, we also demonstrate a set of selected application examples to illustrate how the proposed aspects can drive new growth opportunities for enterprises.
Vorheriger Artikel Extraction of the outer edge of spot welds from the acoustic image with the aid of image processing
Nächster Artikel Assessment of the effect of 3D printed sand mold thickness on solidification process of AlSi13 casting alloy

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Literatur
1.
Thoben KD, Wiesner S, Wuest T (2017) “Industrie 4.0” and smart manufacturing-a review of research issues and application examples. Int J Autom Technol 11(1):4–16
2.
3.
Nunes D, Silva JS, Boavida F (2018) A practical introduction to human-in-the-loop cyber-physical systems. Wiley, Hoboken
4.
Nunes DS, Zhang P, Silva JS (2015) A survey on human-in-the-loop applications towards an internet of all. IEEE Commun Surv Tutor 17(2):944–965
5.
Shahin M, Chen FF, Bouzary H, Krishnaiyer K (2020) Integration of lean practices and Industry 4.0 technologies: smart manufacturing for next-generation enterprises. Int J Adv Manuf Technol 107:1–10
6.
Muller JM, Maier L, Veile J, Voigt KI (2017) Cooperation strategies among SMEs for implementing industry 4.0 environment. Proceedings of the Hamburg International Conference of Logistics (HICL) 23:301–318
7.
Kagermann H, Wahlster W, Helbig J (2013) Securing the future of German manufacturing industry. Recommendations for implementing the strategic initiative. Industrie 4(199):14
8.
Weyer S, Schmitt M, Ohmer M, Gorecky D (2015) Towards Industry 4.0-Standardization as the crucial challenge for highly modular, multi-vendor production systems. Ifac-Papersonline 48(3):579–584
9.
Loskyll M, Heck I, Schlick J, Schwarz M (2012) Context-based orchestration for control of resource-efficient manufacturing processes. Futur Internet 4(3):737–761
10.
Zamfirescu CB, Pirvu BC, Loskyll M, Zuehlke D (2014) Do not cancel my race with cyber-physical systems. IFAC Proc Vol 47(3):4346–4351
11.
12.
13.
Helu M, Libes D, Lubell J, Lyons K, Morris KC (2016) Enabling smart manufacturing technologies for decision-making support. Proceedings of the ASME international design engineering technical conferences and computers and information in engineering conference (IDETC/CIE) (2016), pp 1–10
14.
Li X, Lu R, Liang X, Shen X, Chen J, Lin X (2011) Smart community: an internet of things application. IEEE Commun Mag 49(11):68–75CrossRef
15.
Whitmore A, Agarwal A, Da Xu L (2015) The internet of things—a survey of topics and trends. Inf Syst Front 17(2):261–274CrossRef
16.
Zhang Y, Zhang G, Wang J, Sun S, Si S, Yang T (2015) Real-time information capturing and integration framework of the internet of manufacturing things. Int J Comput Integr Manuf 28(8):811–822CrossRef
17.
18.
19.
Huang HM, Tidwell T, Gill C, Lu C, Gao X, Dyke S (2010) Cyber-physical systems for real-time hybrid structural testing: a case study. In: Proceedings of the 1st ACM/IEEE international conference on cyber-physical systems. ACM, pp 69–78
20.
Kim J, Kim H, Lakshmanan K, Rajkumar RR (2013) Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car. In: Proceedings of the ACM/IEEE 4th international conference on cyber-physical systems. ACM, pp 31–40
21.
Wang L, Haghighi A (2016) Combined strength of holons, agents and function blocks in cyber-physical systems. J Manuf Syst 40:25–34
22.
23.
24.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
25.
Bochkovskiy A, Wang CY, Liao HYM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934
26.
Gatys LA, Ecker AS, Bethge M (2016) Image style transfer using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2414–2423
27.
Liu G, Reda FA, Shih KJ, Wang TC, Tao A, Catanzaro B (2018) Image inpainting for irregular holes using partial convolutions. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 85–100
28.
Cheng Z, Yang Q, Sheng B (2015) Deep colorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp 415–423
29.
Chiu CC, Sainath TN, Wu Y, Prabhavalkar R, Nguyen P, Chen Z et al (2018) State-of-the-art speech recognition with sequence-to-sequence models. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp 4774–4778
30.
Vinyals O, Toshev A, Bengio S, Erhan D (2015) Show and tell: a neural image caption generator. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3156–3164
31.
Klein G, Kim Y, Deng Y, Senellart J, Rush AM (2017) OpenNMT: open-source toolkit for neural machine translation. In: roceedings of ACL 2017, System Demonstrations, pp 67–72CrossRef
32.
Cheng J, Lapata M (2016) Neural summarization by extracting sentences and words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp 484–494CrossRef
33.
Kwiatkowski T, Palomaki J, Redfield O, Collins M, Parikh A, Alberti C et al (2019) Natural questions: a benchmark for question answering research. Trans Assoc Comput Linguist 7:453–466CrossRef
34.
Crandall DJ (2019) Artificial intelligence and manufacturing. Manufacturing policy initiative [2019]: Smart factories: issues of information governance. School of Public and Environmental Affairs, Indiana University, pp 10–17
35.
McKnight M (2017) Generative design: what it is? How is it being used? Why it’s a game changer. KnE Eng 2(2):176–181CrossRef
36.
Murthy DNP, Atrens A, Eccleston JA (2002) Strategic maintenance management. J Qual Maint Eng 8(4):287–305CrossRef
37.
Pirasteh P et al (2019) Interactive feature extraction for diagnostic trouble codes in predictive maintenance: a case study from automotive domain. In: Proceedings of the Workshop on Interactive Data Mining. ACM, p 4
38.
Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L (2018) Toward new-generation intelligent manufacturing. Engineering 4(1):11–20CrossRef
39.
Li BH, Hou BC, Yu WT, Lu XB, Yang CW (2017) Applications of artificial intelligence in intelligent manufacturing: a review. Front Inf Technol Electron Eng 18(1):86–96CrossRef
40.
Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156CrossRef
41.
Matt DT, Orzes G, Rauch E, Dallasega P (2018) Urban production–a socially sustainable factory concept to overcome shortcomings of qualified workers in smart SMEs. Comput Ind Eng 139:105384
42.
Romero D, Bernus P, Noran O, Stahre J, Fast-Berglund A (2016) The Operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems. In: IFIP international conference on advances in production management systems. Springer, Cham, pp 677–686
43.
Wilkins MJ (2007) Changing workforce demographics transform manufacturing. INSIGHT 2007:21M
44.
Schinner M, Valdez AC, Noll E, Schaar AK, Letmathe P, Ziefle M (2017) ‘Industrie 4.0’ and an aging workforce–a discussion from a psychological and a managerial perspective. In: International Conference on Human Aspects of IT for the Aged Population. Springer, Cham, pp 537–556
45.
Kim Y (2014) Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882
46.
Tai, K. S., Socher, R., & Manning CD (2015) Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075
47.
Lee CH, Jwo JS, Hsieh HY, Lin CS (2020) An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning. IEEE Access 8:58279–58289CrossRef
48.
Vibration Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S et al (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345CrossRef
49.
Müller R, Ritz F, Illium S, Linnhoff-Popien C (2020) Acoustic anomaly detection for machine sounds based on image transfer learning. arXiv preprint arXiv:2006.03429
50.
Ren R, Hung T, Tan KC (2017) A generic deep-learning-based approach for automated surface inspection. IEEE Trans Cybernetics 48(3):929–940CrossRef
51.
Meire M, Karsmakers P (2019) Comparison of deep autoencoder architectures for real-time acoustic based anomaly detection in assets. In: 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol 2. IEEE, pp 786–790
52.
Czimmermann T, Ciuti G, Milazzo M, Chiurazzi M, Roccella S, Oddo CM, Dario P (2020) Visual-based defect detection and classification approaches for industrial applications—a survey. Sensors 20(5):1459CrossRef
53.
Cheng Y, Zhu H, Wu J, Shao X (2018) Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans Ind Informatics 15(2):987–997CrossRef
54.
Malhotra P, Vig L, Shroff G, Agarwal P (2015) Long short term memory networks for anomaly detection in time series. In: Proceeding of European symposium on artificial neural networks, computational intelligence, and machine learning, Bruges, Belgium, pp 89–94
55.
Elkington M, Ward C, Potter KD (2016) Automated layup of sheet prepregs on complex moulds. J Adv Mater 3:70–84
56.
Kikuchi T, Tani Y, Takai Y, Goto A, Hamada H (2014) Biomechanics investigation of skillful technician in spray-up fabrication method. In: International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Springer, Cham, pp 24–34
57.
Prabhu VA, Elkington M, Crowley D, Tiwari A, Ward C (2017) Digitisation of manual composite layup task knowledge using gaming technology. Compos Part B 112:314–326CrossRef
58.
Kothamasu R, Huang SH, VerDuin WH (2006) System health monitoring and prognostics—a review of current paradigms and practices. Int J Adv Manuf Technol 28(9-10):1012–1024CrossRef
59.
Zhao R, Yan R, Wang J, Mao K (2017) Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors 17(2):273CrossRef
60.
Wan J, Yang J, Wang Z, Hua Q (2018) Artificial intelligence for cloud-assisted smart factory. IEEE Access 6:55419–55430CrossRef
61.
Gil M, Albert M, Fons J, Pelechano V (2020) Engineering human-in-the-loop interactions in cyber-physical systems. Inf Softw Technol 126:106349
62.
Emmanouilidis C, Pistofidis P, Bertoncelj L, Katsouros V, Fournaris A, Koulamas C, Ruiz-Carcel C (2019) Enabling the human in the loop: linked data and knowledge in industrial cyber-physical systems. Annu Rev Control 47:249–265CrossRef
63.
Ferreira J (2007) Interaction design and agile development: a real world perspective. Master’s Thesis, Victoria University of Wellington, New Zealand
64.
Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
65.
66.
Xue W, Li Q, Xue Q (2019) Text detection and recognition for images of medical laboratory reports with a deep learning approach. IEEE Access 8:407–416CrossRef
67.
Qin S, Bissacco A, Raptis M, Fujii Y, Xiao Y (2019) Towards unconstrained end-to-end text spotting. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4704–4714
68.
Booth J, Di Eugenio B, Cruz IF, Wolfson O (2015) Robust natural language processing for urban trip planning. Appl Artif Intell 29(9):859–903CrossRef
69.
Mennicken S, Brillman R, Thom J, Cramer H (2018) Challenges and methods in design of domain-specific voice assistants. In: 2018 AAAI Spring Symposium Series
70.
Mekni M, Baani Z, Sulieman D (2020) A smart virtual assistant for students. In: Proceedings of the 3rd International Conference on Applications of Intelligent Systems, pp 1–6
71.
Pan MJ, Jang WY (2008) Determinants of the adoption of enterprise resource planning within the technology-organization-environment framework: Taiwan’s communications industry. J Comput Inf Syst 48(3):94–102
72.
Jung-Sing Jwo, Chao-Lin Wu, Hsu-Cheng Hsu (2019) Method and system for automated learning of a virtual assistant, T.W. Patent TW201947428A, issued December 12
Metadaten
Titel
Smart technology–driven aspects for human-in-the-loop smart manufacturing
verfasst von
Jung-Sing Jwo
Ching-Sheng Lin
Cheng-Hsiung Lee
Publikationsdatum
02.04.2021
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 5-6/2021
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-021-06977-9

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