Digital technologies review for manufacturing processes
Article Sidebar
Main Article Content
صندلی اداریAbstract
It is apparent the industrial processes transformations caused by industry 4.0 are in advance in some countries like China, Japan, Germany and United States. But, in return, the developing countries, as the emergent Brazil, seem like to have a long way to achieve digital era. Considering manufacturing processes as the starting point the rise of industry 4.0, this research aims to show a review about the most important technologies used in smart manufacturing, including the main challenges to implement it at Brazil. The papers were collected from Web of Science (WoS), comprising 114 articles and 2 books to underpin this study. This exploratory research resulted in the presentation of some challenges faced by Brazilian industry to join the new industrial era, such as poor technological infrastructure, besides lack of investment in technologies and training of qualified people. Even though the primary motivation of this research was to present a panorama of smart manufacturing for Brazil, this study results contributes to the most of emergent countries, bringing together general concepts and addressing practical applications developed by several researchers from the international academic community.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
1. Proposal of Policy for Free Access Periodics
Authors whom publish in this magazine should agree to the following terms:
a. Authors should keep the copyrights and grant to the magazine the right of the first publication, with the work simultaneously permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 that allows the sharing of the work with recognition of the authorship of the work and initial publication in this magazine.
b. Authors should have authorization for assuming additional contracts separately, for non-exclusive distribution of the version of the work published in this magazine (e.g.: to publish in an institutional repository or as book chapter), with recognition of authorship and initial publication in this magazine.
c. Authors should have permission and should be stimulated to publish and to distribute its work online (e.g.: in institutional repositories or its personal page) to any point before or during the publishing process, since this can generate productive alterations, as well as increasing the impact and the citation of the published work (See The Effect of Free Access).
Proposal of Policy for Periodic that offer Postponed Free Access
Authors whom publish in this magazine should agree to the following terms:
a. Authors should keep the copyrights and grant to the magazine the right of the first publication, with the work simultaneously permitted under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 [SPECIFY TIME HERE] after the publication, allowing the sharing of the work with recognition of the authorship of the work and initial publication in this magazine.
b. Authors should have authorization for assuming additional contracts separately, for non-exclusive distribution of the version of the work published in this magazine (e.g.: to publish in institutional repository or as book chapter), with recognition of authorship and initial publication in this magazine.
c. Authors should have permission and should be stimulated to publish and to distribute its work online (e.g.: in institutional repositories or its personal page) to any point before or during the publishing process, since this can generate productive alterations, as well as increasing the impact and the citation of the published work (See The Effect of Free Access).
d. They allow some kind of open dissemination. Authors can disseminate their articles in open access, but with specific conditions imposed by the editor that are related to:
Version of the article that can be deposited in the repository:
Pre-print: before being reviewed by pairs.
Post-print: once reviewed by pairs, which can be:
The version of the author that has been accepted for publication.
The editor's version, that is, the article published in the magazine.
At which point the article can be made accessible in an open manner: before it is published in the magazine, immediately afterwards or if a period of seizure is required, which can range from six months to several years.
Where to leave open: on the author's personal web page, only departmental websites, the repository of the institution, the file of the research funding agency, among others.
Ricardo Silva Parente, Institute of Technology and Education Galileo of Amazon - ITEGAM
Italo Rodrigo Soares Silva, Institute of Technology and Education Galileo of Amazon - ITEGAM
Paulo Oliveira Siqueira Junior, Institute of Technology and Education Galileo of Amazon - ITEGAM
Prof. Dr. Iracyanne Retto Uhlmann, Institute of Technology and Education Galileo of Amazon - ITEGAM
References
Araújo, A. M., & Oliveira, M. M. (2020). Connectivity-based cylinder detection in unorganized point clouds. Pattern Recognition, 100, 107161.
Azouz, N., & Pierreval, H. (2019). Adaptive smart card-based pull control systems in context-aware manufacturing systems: Training a neural network through multi-objective simulation optimization. Applied Soft Computing, 75, 46-57.
Bauza, M. B., Tenboer, J., Li, M., Lisovich, A., Zhou, J., Pratt, D., Edwards, J., Zhang, H., Turch, C., & Knebel, R. (2018). Realization of industry 4.0 with high speed CT in high volume production. CIRP Journal of Manufacturing Science and Technology, 22, 121-125.
Benitez, G. B., Ayala, N. F., & Frank, A. G. (2020). Industry 4.0 innovation ecosystems: an evolutionary perspective on value cocreation. International Journal of Production Economics, 107735.
Bi, J., Sarpong, D., Botchie, D., & Rao-Nicholson, R. (2017). From imitation to innovation: The discursive processes of knowledge creation in the Chinese space industry. Technological Forecasting and Social Change, 120, 261-270.
Bogle, I. D. L. (2017). A perspective on smart process manufacturing research challenges for process systems engineers. Engineering, 3, 161-165.
Brito, A. (2017). A Quarta Revolução Industrial e as Perspectivas para o Brasil. Revista Científica Multidisciplinar Núcleo do Conhecimento. Edição, 7, 91-96.
Bu, S., Li, Q., Han, P., Leng, P., & Li, K. (2020). Mask-CDNet: A mask based pixel change detection network. Neurocomputing, 378, 166-178.
Buswell, R. A., Da Silva, W. L., Bos, F. P., Schipper, H., Lowke, D., Hack, N., Kloft, H., Mechtcherine, V., Wangler, T., & Roussel, N. (2020). A process classification framework for defining and describing Digital Fabrication with Concrete. Cement and Concrete Research, 134, 106068.
Catalá, L. P., Moreno, M. S., Blanco, A. M., & Bandoni, J. A. (2016). A bi-objective optimization model for tactical planning in the pome fruit industry supply chain. Computers and Electronics in Agriculture, 130, 128-141.
Chiarello, F., Trivelli, L., Bonaccorsi, A., & Fantoni, G. (2018). Extracting and mapping industry 4.0 technologies using wikipedia. Computers in Industry, 100, 244-257.
Craveiroa, F., Duartec, J. P., Bartoloa, H., & Bartolod, P. J. (2019). Additive manufacturing as an enabling technology for digital construction: A perspective on Construction 4.0. Sustainable Development, 4, 6.
Culot, G., Nassimbeni, G., Orzes, G., & Sartor, M. (2020). The future of manufacturing: a Delphi-based scenario analysis on Industry 4.0. Technological Forecasting and Social Change, 120092.
Ćwiklicki, M., Klich, J., & Chen, J. (2020). The adaptiveness of the healthcare system to the fourth industrial revolution: a preliminary analysis. Futures, 122, 102602.
Da Silva, A., & Almeida, I. (2020). Towards INDUSTRY 4.0 a case STUDY in ornamental stone sector. Resources Policy, 67, 101672.
Da Silva, S. A., De Souza Vasconcelos, R., & Campos, P. S. (2019). Indústria 4.0: um aporte teórico sobre o cenário atual da tecnologia no brasil. ITEGAM-JETIA, 5, 56-60.
D'anniballe, A., Silva, J., Marzocca, P., & Ceruti, A. (2020). The Role of Augmented Reality in Air Accident Investigation and Practitioner Training. Reliability Engineering & System Safety, 107149.
De Moura Souza, E. M., & De Castro Vieira, J. (2020). Desafios da indústria 4.0 no contexto brasileiro/Industry 4.0 challenges inside the brazilian context. Brazilian Journal of Development, 6, 5001-5022.
Den Boer, J., Lambrechts, W., & Krikke, H. (2020). Additive manufacturing in military and humanitarian missions: Advantages and challenges in the spare parts supply chain. Journal of Cleaner Production, 257, 120301.
Dev, N. K., Shankar, R., & Qaiser, F. H. (2020). Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resources, Conservation and Recycling, 153, 104583.
Evans, P. B. (2018). Dependent development: The alliance of multinational, state, and local capital in Brazil. Princeton University Press.
Fernando, S., Scott-Brown, J., Şerban, O., Birch, D., Akroyd, D., Molina-Solana, M., Heinis, T., & Guo, Y. (2020). Open Visualization Environment (OVE): A web framework for scalable rendering of data visualizations. Future Generation Computer Systems.
Fox, B., & Subic, A. (2019). An Industry 4.0 Approach to the 3D Printing of Composite Materials. Engineering, 5, 621-623.
Fox, S., Kotelba, A., Marstio, I., & Montonen, J. (2020). Aligning human psychomotor characteristics with robots, exoskeletons and augmented reality. Robotics and Computer-Integrated Manufacturing, 63, 101922.
Franklin, C. S., Dominguez, E. G., Fryman, J. D., & Lewandowski, M. L. (2020). Collaborative robotics: New era of human–robot cooperation in the workplace. Journal of Safety Research, 74, 153-160.
Ghayour, M., Hojjati, M., & Ganesan, R. (2020). Effect of Tow Gaps on Impact Strength of Thin Composite Laminates Made by Automated Fiber Placement: Experimental and Semi-Analytical Approaches. Composite Structures, 112536.
Giannuzzi, M., Papadia, G., & Pascarelli, C. (2020). IC. IDO as a tool for displaying machining processes. The logic interface between Computer-Aided-Manufacturing and Virtual Reality. Procedia CIRP, 88, 145-150.
Gonçalves, A. M., Sena, A. J., De Almeida Alencar, M. A., Rodrigues, R. A., Oliveira, W. E., Wobeto, R., & Queiroz, A. L. (2018). Implantação da Industrial 4.0 nos Estados Unidos e no Brasil. CIPEEX, 2, 2229-2236.
Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary perspectives on complex systems. Springer, Cham, 85-113.
Gupta, R., Tanwar, S., Kumar, N., & Tyagi, S. (2020). Blockchain-based security attack resilience schemes for autonomous vehicles in industry 4.0: A systematic review. Computers & Electrical Engineering, 86, 106717.
Hang, L., Ullah, I., & Kim, D.-H. (2020). A secure fish farm platform based on blockchain for agriculture data integrity. Computers and Electronics in Agriculture, 170, 105251.
Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the Digital Twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology.
Kerin, M., & Pham, D. T. (2019). A review of emerging industry 4.0 technologies in remanufacturing. Journal of Cleaner Production, 237, 117805.
Kiss, A. A., & Grievink, J. (2020). Process systems engineering developments in Europe from an industrial and academic perspective. Computers & Chemical Engineering, 106823.
Klöckner, M., Kurpjuweit, S., Velu, C., & Wagner, S. M. (2020). Does Blockchain for 3D Printing Offer Opportunities for Business Model Innovation? Research-Technology Management, 63, 18-27.
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56, 508-517.
Kusiak, A. (2019). Fundamentals of smart manufacturing: a multi-thread perspective. Annual Reviews in Control, 47, 214-220.
Lee, W. J., Kwag, S. I., & Ko, Y. D. (2020). Optimal capacity and operation design of a robot logistics system for the hotel industry. Tourism Management, 76, 103971.
Li, Q., He, T., & Fu, G. (2020). Judgment and optimization of video image recognition in obstacle detection in intelligent vehicle. Mechanical Systems and Signal Processing, 136, 106406.
Lin, B., Du, R., Dong, Z., Jin, S., & Liu, W. (2020). The impact of foreign direct investment on the productivity of the Chinese forest products industry. Forest Policy and Economics, 111, 102035.
Lins, T., & Oliveira, R. A. R. (2020). Cyber-physical production systems retrofitting in context of industry 4.0. Computers & Industrial Engineering, 139, 106193.
Liu, C., & Shi, Y. (2020). Design optimization for filament wound cylindrical composite internal pressure vessels considering process-induced residual stresses. Composite Structures, 235, 111755.
Liu, Y., Zhang, W., Pan, S., Li, Y., & Chen, Y. (2020). Analyzing the robotic behavior in a smart city with deep enforcement and imitation learning using IoRT. Computer Communications, 150, 346-356.
Lovreglio, R., & Kinateder, M. (2020). Augmented reality for pedestrian evacuation research: promises and limitations. Safety Science, 128, 104750.
Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H., & Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837.
Lu, Y., Xu, X., & Wang, L. (2020). Smart manufacturing process and system automation–A critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312-325.
Mana, R., César, F. I. G., Makiya, I. K., & Volpe, W. (2018). The concept of the industry 4.0 in a German multinational instrumentation and control company: a case study of a subsidiary in Brazil. Independent Journal of Management & Production, 9(3), 933-957.
Mao, S., Wang, B., Tang, Y., & Qian, F. (2019). Opportunities and challenges of artificial intelligence for green manufacturing in the process industry. Engineering, 5, 995-1002.
Maresch, D., & Gartner, J. (2020). Make disruptive technological change happen-The case of additive manufacturing. Technological Forecasting and Social Change, 155, 119216.
Masood, T., & Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261.
Melenbrink, N., Werfel, J., & Menges, A. (2020). On-site autonomous construction robots: Towards unsupervised building. Automation in Construction, 119, 103312.
Menelau, S., Macedo, F. G. L., Carvalho, P. L. D., Nascimento, T. G., & Carvalho Júnior, A. D. D. (2019). Mapeamento da produção científica da Indústria 4.0 no contexto dos BRICS: reflexões e interfaces. Cadernos EBAPE. BR, 17(4), 1094-1114.
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2019). Smart manufacturing: characteristics, technologies and enabling factors. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233, 1342-1361.
Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56, 1118-1136.
Mokhtar, A., & Nasooti, M. (2020). A decision support tool for cement industry to select energy efficiency measures. Energy Strategy Reviews, 28, 100458.
Moktadir, M. A., Ali, S. M., Kusi-Sarpong, S., & Shaikh, M. A. A. (2018). Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection, 117, 730-741.
Moon, S.-W., Kim, R. E., Cheng, A. C., Li, Y. E., & Ku, T. (2020). Post-processing of background noise from SCPT auto source signal: A feasibility study for soil type classification. Measurement, 156, 107610.
Moreira, M. M., & Correa, P. G. (1998). A first look at the impacts of trade liberalization on Brazilian manufacturing industry. World Development, 26, 1859-1874.
Müller, F., Jaeger, D., & Hanewinkel, M. (2019). Digitization in wood supply–A review on how Industry 4.0 will change the forest value chain. Computers and Electronics in Agriculture, 162, 206-218.
Müller, J. M., Buliga, O., & Voigt, K.-I. (2020). The role of absorptive capacity and innovation strategy in the design of industry 4.0 business Models-A comparison between SMEs and large enterprises. European Management Journal.
Nara, E. O. B., Da Costa, M. B., Baierle, I. C., Schaefer, J. L., Benitez, G. B., Do Santos, L. M. A. L., & Benitez, L. B. (2020). Expected Impact of Industry 4.0 Technologies on Sustainable Development: A study in the context of Brazil's Plastic Industry. Sustainable Production and Consumption, 25, 102-122.
Naranjo, D. M., Risco, S., De Alfonso, C., Pérez, A., Blanquer, I., & Moltó, G. (2020). Accelerated serverless computing based on GPU virtualization. Journal of Parallel and Distributed Computing, 139, 32-42.
Nazaré, T. B., Da Rocha, J. T., Oliveira, L. A. T., De Souza, F. L., & Ramos, R. B. (2018). Os desafios da indústria 4.0 no Brasil. Revista Mythos, 10(2), 129-137.
Negri, F. (2018). Novos caminhos para a inovação no Brasil. Washington, DC: Wilson Center.
Nwankwo, C. D., Theophilus, S. C., & Arewa, A. O. (2020). A comparative analysis of process safety management (PSM). systems in the process industry. Journal of Loss Prevention in the Process Industries, 104171.
Ozkan-Ozen, Y. D., Kazancoglu, Y., & Mangla, S. K. (2020). Synchronized barriers for circular supply chains in industry 3.5/industry 4.0 transition for sustainable resource management. Resources, Conservation and Recycling, 161, 104986.
Pacchini, A. P. T., Lucato, W. C., Facchini, F., & Mummolo, G. (2019). The degree of readiness for the implementation of Industry 4.0. Computers in Industry, 113, 103125.
Pallavicini, F., Argenton, L., Toniazzi, N., Aceti, L., & Mantovani, F. (2016). Virtual reality applications for stress management training in the military. Aerospace medicine and human performance, 87, 1021-1030.
Parashar, P., Chen, C. H., Akbar, C., Fu, S. M., Rawat, T. S., Pratik, S., Butola, R., Chen, S. H., & Lin, A. S. (2019). Analytics-statistics mixed training and its fitness to semisupervised manufacturing. PloS one, 14, e0220607.
Pejic-Bach, M., Bertoncel, T., Meško, M., & Krstić, Ž. (2020). Text mining of industry 4.0 job advertisements. International Journal of Information Management, 50, 416-431.
Pekkarinen, S., Hennala, L., Tuisku, O., Gustafsson, C., Johansson-Pajala, R.-M., Thommes, K., Hoppe, J. A., & Melkas, H. (2020). Embedding care robots into society and practice: Socio-technical considerations. Futures.
Petrick, I. J., & Simpson, T. W. (2013). 3D printing disrupts manufacturing: how economies of one create new rules of competition. Research-Technology Management, 56, 12-16.
Pólvora, A., Nascimento, S., Lourenço, J. S., & Scapolo, F. (2020). Blockchain for industrial transformations: A forward-looking approach with multi-stakeholder engagement for policy advice. Technological Forecasting and Social Change, 157, 120091.
Porpiglia, F., Checcucci, E., Amparore, D., Piana, A., Piramide, F., Volpi, G., De Cillis, S., Manfredi, M., Piazzolla, P., & Fiori, C. (2020). V14-02 computer vision algorithm allows to perform 3d automatic augmented-reality robot-assisted radical prostatectomy. The Journal of Urology, 203, e1306-e1306.
Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. Ieee Access, 6, 3585-3593.
Qin, S., Wang, Q., & Chen, X. (2020). Application of virtual reality technology in nuclear device design and research. Fusion Engineering and Design, 161, 111906.
Raj, A., Dwivedi, G., Sharma, A., De Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546.
Rampasso, I. S., Mello, S. L., Walker, R., Simão, V. G., Araújo, R., Chagas, J., Quelhas, O. L. G., & Anholon, R. (2020). An investigation of research gaps in reported skills required for Industry 4.0 readiness of Brazilian undergraduate students. Higher Education, Skills and Work-Based Learning.
Ren, S., Zhang, Y., Liu, Y., Sakao, T., Huisingh, D., & Almeida, C. M. (2019). A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions. Journal of cleaner production, 210, 1343-1365.
Robinson, D. K., Lagnau, A., & Boon, W. P. (2019). Innovation pathways in additive manufacturing: Methods for tracing emerging and branching paths from rapid prototyping to alternative applications. Technological Forecasting and Social Change, 146, 733-750.
Roldán, J. J., Crespo, E., Martín-Barrio, A., Peña-Tapia, E., & Barrientos, A. (2019). A training system for Industry 4.0 operators in complex assemblies based on virtual reality and process mining. Robotics and Computer-Integrated Manufacturing, 59, 305-316.
Romeo, L., Loncarski, J., Paolanti, M., Bocchini, G., Mancini, A., & Frontoni, E. (2020). Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0. Expert Systems with Applications, 140, 112869.
Ruiz-Sarmiento, J.-R., Monroy, J., Moreno, F.-A., Galindo, C., Bonelo, J.-M., & Gonzalez-Jimenez, J. (2020). A predictive model for the maintenance of industrial machinery in the context of industry 4.0. Engineering Applications of Artificial Intelligence, 87, 103289.
Sahal, R., Breslin, J. G., & Ali, M. I. (2020). Big data and stream processing platforms for Industry 4.0 requirements mapping for a predictive maintenance use case. Journal of Manufacturing Systems, 54, 138-151.
Santos, M. Y., E Sá, J. O., Andrade, C., Lima, F. V., Costa, E., Costa, C., Martinho, B., & Galvão, J. (2017). A big data system supporting bosch braga industry 4.0 strategy. International Journal of Information Management, 37, 750-760.
Sehmi, M., Christensen, J., Bastien, C., Wilson, A., & Kanarachos, S. (2020). Automated post-processing for sheet metal component manufacturing. Advances in Engineering Software, 143, 102794.
Shabani, A., Asgarian, B., Salido, M., & Gharebaghi, S. A. (2020). Search and Rescue optimization algorithm: a new optimization method for solving constrained engineering optimization problems. Expert Systems with Applications, 113698.
Shah, D., Wang, J., & He, Q. P. (2020). Feature Engineering in Big Data Analytics for IoT-Enabled Smart Manufacturing–Comparison between Deep Learning and Statistical Learning. Computers & Chemical Engineering, 106970.
Sharpe, R., Van Lopik, K., Neal, A., Goodall, P., Conway, P. P., & West, A. A. (2019). An industrial evaluation of an Industry 4.0 reference architecture demonstrating the need for the inclusion of security and human components. Computers in Industry, 108, 37-44.
Shukla, A. K., Nath, R., Muhuri, P. K., & Lohani, Q. D. (2020). Energy efficient multi-objective scheduling of tasks with interval type-2 fuzzy timing constraints in an Industry 4.0 ecosystem. Engineering Applications of Artificial Intelligence, 87, 103257.
Souza, M. L. H., Da Costa, C. A., De Oliveira Ramos, G., & Da Rosa Righi, R. (2020). A survey on decision-making based on system reliability in the context of Industry 4.0. Journal of Manufacturing Systems, 56, 133-156.
Storolli, W. G., Makiya, I. K., & Cesar, F. I. G. (2019). Comparative analyzes of technological tools between industry 4.0 and smart cities approaches: the new society ecosystem. Independent Journal of Management & Production, 10(3), 1134-1158.
Syed, R., Suriadi, S., Adams, M., Bandara, W., Leemans, S. J., Ouyang, C., Ter Hofstede, A. H., Van De Weerd, I., Wynn, M. T., & Reijers, H. A. (2020). Robotic Process Automation: Contemporary themes and challenges. Computers in Industry, 115, 103162.
Takezawa, A., To, A. C., Chen, Q., Liang, X., Dugast, F., Zhang, X., & Kitamura, M. (2020). Sensitivity analysis and lattice density optimization for sequential inherent strain method used in additive manufacturing process. Computer Methods in Applied Mechanics and Engineering, 370, 113231.
Tang, C. S., & Veelenturf, L. P. (2019). The strategic role of logistics in the industry 4.0 era. Transportation Research Part E: Logistics and Transportation Review, 129, 1-11.
Tao, F., & Qi, Q. (2017). New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49, 81-91.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157-169.
Tao, F., Qi, Q., Wang, L., & Nee, A. (2019). Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering, 5, 653-661.
Teixeira, R. L. P., Teixeira, C. H. S. B., De Araujo Brito, M. L., & Silva, P. C. D. (2019). Os discursos acerca dos desafios da siderurgia na indústria 4.0 no Brasil/The discussions about the challenges of steel industry 4.0 in Brazil. Brazilian Journal of Development, 5, 28290-28309.
Tortorella, G. L., & Fettermann, D. (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56, 2975-2987.
Tortorella, G. L., Vergara, A. M. C., Garza-Reyes, J. A., & Sawhney, R. (2020). Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics, 219, 284-294.
Tortorella, G., Miorando, R., Caiado, R., Nascimento, D., & Portioli Staudacher, A. (2018). The mediating effect of employees’ involvement on the relationship between Industry 4.0 and operational performance improvement. Total Quality Management & Business Excellence, 1-15.
Van Lopik, K., Sinclair, M., Sharpe, R., Conway, P., & West, A. (2020). Developing augmented reality capabilities for industry 4.0 small enterprises: Lessons learnt from a content authoring case study. Computers in Industry, 117, 103208.
Vello, A. C. P., & Volante, C. R. (2019). O conceito de indústria 4.0 e os principais desafios de sua implantação no brasil. Revista Interface Tecnológica, 16, 325-336.
Walheer, B., & He, M. (2020). Technical efficiency and technology gap of the manufacturing industry in China: Does firm ownership matter? World Development, 127, 104769.
Wang, X., Zhou, X., Xia, Z., & Gu, X. (2020). A survey of welding robot intelligent path optimization. Journal of Manufacturing Processes.
Wedel, M., Bigné, E., & Zhang, J. (2020). Virtual and augmented reality: Advancing research in consumer marketing. International Journal of Research in Marketing.
Wu, W., Pirbhulal, S., Sangaiah, A. K., Mukhopadhyay, S. C., & Li, G. (2018). Optimization of signal quality over comfortability of textile electrodes for ECG monitoring in fog computing based medical applications. Future Generation Computer Systems, 86, 515-526.
Xia, K., Sacco, C., Kirkpatrick, M., Saidy, C., Nguyen, L., Kircaliali, A., & Harik, R. (2020). A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence. Journal of Manufacturing Systems.
Xu, W., Cui, J., Li, L., Yao, B., Tian, S., & Zhou, Z. (2020). Digital twin-based industrial cloud robotics: Framework, control approach and implementation. Journal of Manufacturing Systems.
Yadav, G., Kumar, A., Luthra, S., Garza-Reyes, J. A., Kumar, V., & Batista, L. (2020). A framework to achieve sustainability in manufacturing organisations of developing economies using industry 4.0 technologies’ enablers. Computers in Industry, 122, 103280.
Yan, H., Hua, Q., Wang, Y., Wei, W., & Imran, M. (2017). Cloud robotics in smart manufacturing environments: challenges and countermeasures. Computers & Electrical Engineering, 63, 56-65.
Yun, J. J., Won, D., Jeong, E., Park, K., Yang, J., & Park, J. (2016). The relationship between technology, business model, and market in autonomous car and intelligent robot industries. Technological Forecasting and Social Change, 103, 142-155.
Zhang, Z., & David, J. (2020). Structural order measure of manufacturing systems based on an information-theoretic approach. Expert Systems with Applications, 113636.
Zheng, P., & Sivabalan, A. S. (2020). A generic tri-model-based approach for product-level digital twin development in a smart manufacturing environment. Robotics and Computer-Integrated Manufacturing, 64, 101958.
Zhuang, C., Gong, J., & Liu, J. (2020). Digital twin-based assembly data management and process traceability for complex products. Journal of Manufacturing Systems.