Advanced manufacturing with machine learning: enhancing predictive maintenance, quality control, and process optimization
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Abstract
This study examined the integration of machine learning (ML) techniques into advanced manufacturing processes to enhance predictive maintenance, quality control, and process optimization. The review covers various aspects of advanced manufacturing, including predictive maintenance, quality control, process optimization, supply chain management, inventory control, energy efficiency, human-machine collaboration, data security, scalability, regulatory compliance, and future trends. The results showed that ML algorithms offer capabilities to analyze large volumes of data, including sensor readings, production metrics, and historical performance data, enabling manufacturers to extract Vital understandings and optimize their operations. Predictive maintenance powered by ML facilitates proactive equipment maintenance, reducing downtime and preventing costly breakdowns. ML-based quality control systems enhance product consistency and reliability by detecting defects and deviations in real-time. Additionally, process optimization through ML techniques enables continuous improvement and adaptation to dynamic production environments. However, the adoption of ML in manufacturing also poses challenges related to data privacy, security, and regulatory compliance, which must be addressed to ensure responsible deployment. Overall, the integration of ML into advanced manufacturing holds immense potential for driving efficiency, productivity, and competitiveness in the industry.
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B. Leng, Y. Zhang, D. P. Tsai and S. Xiao, “Meta-device: advanced manuf acturing,” Light: Advanced Manufacturing, 5(1), pp. 117-132, 2024.
F. Islam, G.E. Granroth, J.J. Molaison, D. Goldsby, D. Siddel, D.C. Anderson, A.M. Elliott and B. Haberl, “Advanced manufacturing of 3D custom boron-carbide collimators designed for complex environments for neutron scattering,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, pp.169165, 2024..
M. Mozaffar, S. Liao, X. Xie, S. Saha, C. Park, J. Cao, W.K. Liu and Z. Gan, “Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives,” Journal of Materials Processing Technology, 302, pp.117485, 2022
E. Hopkins, “Machine learning tools, algorithms, and techniques. Journal of Self-Governance and Management Economics,” 10(1), pp. 43-55, 2022.
M. Alshurideh, B. Al Kurdi, S. A. Salloum, I. Arpaci and M. Al-Emran, “Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms,” Interactive Learning Environments, 31(3), pp. 1214-1228, 2023.
L. Yu, X. Zhao J. Huang, H. Hu and B. Liu, “Research on Machine Learning With Algorithms and Development,” Journal of Theory and Practice of Engineering Science, 3(12), 7-14, 2023.
M. Achouch, M. Dimitrova, R. Dhouib, H. Ibrahim, M. Adda, S. Sattarpanah Karganroudi, K. Ziane and A. Aminzadeh, “Predictive maintenance and fault monitoring enabled by machine learning: experimental analysis of a TA-48 multistage centrifugal plant compressor,” Applied Sciences, 13(3), pp.1790, 2023.
M. Achouch, M. Dimitrova, K. Ziane, S. Sattarpanah Karganroudi, R. Dhouib, H. Ibrahim and M. Adda, “On predictive maintenance in industry 4.0: Overview, models, and challenges,” Applied Sciences, 12(16), pp. 8081, 2022.
E. A. Etukudoh, “Theoretical frameworks of ecopfm predictive maintenance (ecopfm) predictive maintenance system,” Engineering Science & Technology Journal, 5(3), pp. 913-923, 2024.
H. Nordal and I. El-Thalji, “Assessing the technical specifications of predictive maintenance: A case study of centrifugal compressor,” Applied Sciences, 11(4), pp. 1527, 2021.
N. Kroell, X. Chen, B. Küppers, S. Schlögl, A. Feil and K. Greiff, “Near-infrared-based quality control of plastic pre-concentrates in lightweight-packaging waste sorting plants,” Resources, Conservation and Recycling, 201, pp. 107256, 2024.
R. L. Wiseman, J. S. Mesgarzadeh and L. M. Hendershot, “Reshaping endoplasmic reticulum quality control through the unfolded protein response,” Molecular cell, 82(8), pp.1477-1491, 2022.
L. Zhang, L. Yao, Z. Lu and H. Yu, “Current status of quality control in screening esophagogastroduodenoscopy and the emerging role of artificial intelligence,” Digestive Endoscopy, 36(1), pp. 5-15, 2024.
Z. Liu, N. Rolston, A. C. Flick, T. W. Colburn, Z. Ren, R. H. Dauskardt and T. Buonassisi, “Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing,” Joule, 6(4), pp.834-849, 2022.
A. Attari, A. Abbaszadeh-Mayvan and A. Taghizadeh-Alisaraei, “Process optimization of ultrasonic-assisted biodiesel production from waste cooking oil using waste chicken eggshell-derived CaO as a green heterogeneous catalyst,” Biomass and Bioenergy, 158, pp.106357, 2022.
K. Stergiou, C. Ntakolia, P. Varytis, E. Koumoulos, P. Karlsson and S. Moustakidis, “Enhancing property prediction and process optimization in building materials through machine learning: A review,” Computational Materials Science, 220, pp. 112031, 2023.
T. Yasuda, S. Ookawara, S. Yoshikawa and H. Matsumoto, “Materials processing model-driven discovery framework for porous materials using machine learning and genetic algorithm: A focus on optimization of permeability and filtration efficiency,” Chemical Engineering Journal, 453, pp. 139540, 2023.
R. Hasanzadeh, P. Mojaver, T. Azdast, S. Khalilarya and A. Chitsaz, “Developing gasification process of polyethylene waste by utilization of response surface methodology as a machine learning technique and multi-objective optimizer approach,” International Journal of Hydrogen Energy, 48(15), pp. 5873-5886, 2023.
T. Ghosh and K. Martinsen, “Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms,” Engineering Science and Technology, an International Journal, 23(3), pp. 650-663, 2020.
M. Wang, G. Hui, Y. Pang, S. Wang and S. Chen, “Optimization of machine learning approaches for shale gas production forecast,” Geoenergy Science and Engineering, 226, pp. 211719, 2023.
W. M. Ashraf and V. Dua, “Machine learning based modelling and optimization of post-combustion carbon capture process using MEA supporting carbon neutrality,” Digital Chemical Engineering, 8, pp. 100115, 2023.
N. Voß, “Navigating the intersection of developers, domain experts and AI: An identity work perspective (Master's thesis, University of Twente), 2023 .
P. Gackowiec, “General overview of maintenance strategies–concepts and approaches,” Multidisciplinary Aspects of Production Engineering,2(1), pp.126-139, 2019.
M. H. N. Yousefi, V. Degeler and A. Lazovik, “Empowering Machine Learning Development with Service-Oriented Computing Principles,” In Symposium and Summer School on Service-Oriented Computing, 2023, June, pp. 24-44, Cham: Springer Nature Switzerland.
M. Qraitem, D. Kularatne, E. Forgoston and M. A. Hsieh, “Bridging the gap: Machine learning to resolve improperly modeled dynamics,” Physica D: Nonlinear Phenomena, 414, pp. 132736, 2020.
H. H. Van-Rossum and H. Kemperman, “Optimization and validation of moving average quality control procedures using bias detection curves and moving average validation charts,” Clinical Chemistry and Laboratory Medicine (CCLM), 55(2), pp. 218-224, 2017.
T. Khinvasara, S. Ness and A. Shankar, “Leveraging AI for Enhanced Quality Assurance in Medical Device Manufacturing,” Asian Journal of Research in Computer Science, 17(6), pp. 13-35, 2024.
J. Silva, P. Coelho, L. Saraiva, P. Vaz, P. Martins and A. López-Rivero, “Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study,” Applied Sciences, 14(5), pp. 1850, 2024.
T. Abass, E. O. Itua, T. Bature and M. A. Eruaga, “Concept paper: Innovative approaches to food quality control: AI and machine learning for predictive analysis,” World Journal of Advanced Research and Reviews, 21(3), pp. 823-828, 2024.
J. Da-Wu and Y. H, Huang, “Enhanced Identification of Internal Casting Defects in Vehicle Wheels Using YOLO Object Detection and X-Ray Inspection,”Traitement du Signal, 40(5), 2023.
C. Bergès, C. Céneray, G. Albermann, J. Bird, B. Balakrishnan, S.M. Mustafa, S. Moeller, B. Rubink and C. Smith, “Detection and prevention of assembly defects, by machine learning algorithms, in semiconductor industry for automotive,” In 2020 IEEE International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA), 2020, July, (pp. 1-9) IEEE.
Z. M. Çınar, A. Abdussalam Nuhu, Q. Zeeshan, O. Korhan, M. Asmael and B. Safaei, “Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0,” Sustainability, 12(19), pp. 8211, 2020.
A. Theissler, J. Pérez-Velázquez, M. Kettelgerdes and G. Elger, “Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry,” Reliability engineering & system safety, 215, pp. 107864, 2021.
R. Bartels, J. Dudink, S. Haitjema, D. Oberski and A. van ‘t Veen, “A perspective on a quality management system for AI/ML-based clinical decision support in hospital care,” Frontiers in Digital Health, 4, pp. 942588, 2022.
M. Krishnan, “Against interpretability: a critical examination of the interpretability problem in machine learning,” Philosophy & Technology, 33(3), pp.487-502, 2020.
T.P. Pagano, R.B. Loureiro, F.V. Lisboa, R.M. Peixoto, G.A. Guimarães, G.O. Cruz, M.M. Araujo, L.L. Santos, M.A. Cruz, E.L. Oliveira and I. Winkler, “Bias and unfairness in machine learning models: a systematic review on datasets, tools, fairness metrics, and identification and mitigation methods,” Big data and cognitive computing, 7(1), pp.152023.
D. Weichert, P. Link, A. Stoll, S. Rüping, S. Ihlenfeldt and S. Wrobel, “A review of machine learning for the optimization of production processes,” The International Journal of Advanced Manufacturing Technology, 104(5), pp. 1889-1902, 2019.
B. R. Hough, D. A. Beck, D. T. Schwartz and J. Pfaendtner, “Application of machine learning to pyrolysis reaction networks: Reducing model solution time to enable process optimization,” Computers & Chemical Engineering, 104, pp. 56-63, 2017.
K. D. Humfeld, D. Gu, G. A. Butler, K. Nelson and N. Zobeiry, “A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing control,” Composites Part B: Engineering, 223, pp. 109150, 2021.
G. P. Rangaiah, Z. Feng and A. F. Hoadley, “Multi-objective optimization applications in chemical process engineering: Tutorial and review. Processes, 8(5), pp. 508, 2020.
A. Mitsos, N. Asprion, C.A. Floudas, M. Bortz, M. Baldea, D. Bonvin, A. Caspari and P. Schäfer, “Challenges in process optimization for new feedstocks and energy sources,” Computers & Chemical Engineering, 113, pp. 209-221, 2018.
L. F. De-Carvalho Botega and J. C. da Silva, “A data-driven Machine Learning approach to creativity and innovation techniques selection in solution development,” Knowledge-Based Systems, 257, pp. 109893, 2022.
B.J. Shields, J., Li, J. Stevens, M. Parasram, F. Damani, J.I.M. Alvarado, J.M. Janey, R.P. Adams and A.G. Doyle, “Bayesian reaction optimization as a tool for chemical synthesis,” Nature, 590(7844), pp.89-96, 2021.
A. M. Schweidtmann, A. D. Clayton, N. Holmes, E. Bradford, R. A. Bourne and A. A. Lapkin, “Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives,” Chemical Engineering Journal, 352, pp. 277-282, 2018.
W. Xu, Y. Wang, D. Zhang, Z. Yang, Z. Yuan, Y. Lin, H. Yan, X. Zhou and C. Yang, “Transparent AI-assisted chemical engineering process: Machine learning modeling and multi-objective optimization for integrating process data and molecular-level reaction mechanisms,” Journal of Cleaner Production, 448, pp.141412, 2024.
L. M. Daling and S. J. Schlittmeier, “Effects of augmented reality-, virtual reality-, and mixed reality–based training on objective performance measures and subjective evaluations in manual assembly tasks: a scoping review,” Human factors, 66(2), pp. 589-626, 2024.
M. Baptista, S. Sankararaman, I. P. de Medeiros, C. Nascimento Jr, H. Prendinger and E. M. Henriques, “Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modelling,” Computers & Industrial Engineering, 115, pp. 41-53, 2018.
I. Paprocka, W. M. Kempa and G. Ćwikła, “Predictive maintenance scheduling with failure rate described by truncated normal distribution,” Sensors, 20(23), pp. 6787, 2020.
D. Meister, “The history of human factors and ergonomics,” CRC Press, 2018.
P. A. Hancock and G. Matthews, “Workload and performance: Associations, insensitivities, and dissociations,” Human factors, 61(3), pp.374-392, 2019.
M. Bhattacharya, M. Penica, E. O’Connell, M. Southern and M. Hayes, “Human-in-loop: a review of smart manufacturing deployments,” Systems, 11(1), pp. 35, 2023.
C. J. Turner, R. Ma, J. Chen and J. Oyekan, “Human in the Loop: Industry 4.0 technologies and scenarios for worker mediation of automated manufacturing,” IEEE access, 9, pp. 103950-103966, 2021.
T. Jensen, M. M. H. Khan, M. A. A. Fahim and Y. Albayram, “Trust and anthropomorphism in tandem: the interrelated nature of automated agent appearance and reliability in trustworthiness perceptions,” In Proceedings of the 2021 ACM Designing Interactive Systems Conference, 2021, June, pp. 1470-1480.
B. A. Kadir and O. Broberg, “Human well-being and system performance in the transition to industry 4.0,” International Journal of Industrial Ergonomics, 76, pp. 102936, 2020.
P. J. Bota, C. Wang, A. L. Fred and H. P. Da Silva, “A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals,” IEEE access, 7, pp. 140990-141020, 2019.
D. C. Hernandez-Bocanegra and J. Ziegler, “(Explaining review-based recommendations: Effects of profile transparency, presentation style and user characteristics” i-com, 19(3), pp. 181-200, 2021.
E. J. De-Visser, R. Pak and T. H. Shaw, “From ‘automation’to ‘autonomy’: the importance of trust repair in human–machine interaction. Ergonomics, 61(10), pp. 1409-1427, 2018.
V. Patel, A. Chesmore, C. M. Legner and S. Pandey, “Trends in workplace wearable technologies and connected‐worker solutions for next‐generation occupational safety, health, and productivity,” Advanced Intelligent Systems, 4(1), pp. 2100099, 2022.
P. M. Kuber and E. Rashedi, “Product ergonomics in industrial exoskeletons: potential enhancements for workforce efficiency and safety,” TheoreTical issues in ergonomics science, 22(6), pp. 729-752, 2020.
I. Priadythama, L. Herdiman and S. Susmartini, “Role of rapid manufacturing technology in wearable customized assistive technology for modern industry,”” In AIP Conference Proceedings, 2020, April, Vol. 2217, No. 1, AIP Publishing.
S. Kolasani, “Revolutionizing manufacturing, making it more efficient, flexible, and intelligent with Industry 4.0 innovations,” International Journal of Sustainable Development Through AI, ML and IoT, 3(1), pp. 1-17, 2024.
N. Yas, M. N. I. Elyat, M. Saeed, F. Shwedeh and S. Lootah, “The Impact of Intellectual Property Rights and the Work Environment on Information Security in the United Arab Emirates,” Kurdish Studies, 12(1), pp. 3931-3948, 2024.
C. J. Haug and J. M. Drazen, “Artificial intelligence and machine learning in clinical medicine, 2023,” New England Journal of Medicine, 388(13), pp. 1201-1208, 2023.
A. F. Psaros, X. Meng, Z. Zou, L. Guo and G. E. Karniadakis, “Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons,” Journal of Computational Physics, 477, pp. 111902, 2023.
S. O. Olabanji, Y. Marquis, C. S. Adigwe, S. A. Ajayi, T. O. Oladoyinbo and O. O. Olaniyi, “AI-Driven cloud security: Examining the impact of user behavior analysis on threat detection,” Asian Journal of Research in Computer Science, 17(3), pp. 57-74, 2024.
M. Humerick, “Taking AI personally: how the EU must learn to balance the interests of personal data privacy & artificial intelligence,” Santa Clara High Tech. LJ, 34, pp. 393, 2017.
K. Rantos, G. Drosatos, K. Demertzis, C. Ilioudis, A. Papanikolaou and A. Kritsas, “ADvoCATE: a consent management platform for personal data processing in the IoT using blockchain technology,” In Innovative Security Solutions for Information Technology and Communications: 11th International Conference, SecITC 2018, Bucharest, Romania, November 8–9, 2018, Revised Selected Papers 11, 2019, pp. 300-313, Springer International Publishing.
O. Demir and B. Kocak, “A decentralized file sharing framework for sensitive data,” In Big Data Innovations and Applications: 5th International Conference, Innovate-Data 2019, Istanbul, Turkey, August 26–28, 2019, Proceedings 5 (pp. 142-149). Springer International Publishing.
V. H. Hoang, E. Lehtihet and Y. Ghamri-Doudane, “Privacy-preserving blockchain-based data sharing platform for decentralized storage systems. In 2020 IFIP Networking conference (networking), 2020, June, pp. 280-288, IEEE.
Y. Hu, S. Kumar and R. A. Popa, “Ghostor: Toward a Secure {Data-Sharing} System from Decentralized Trust,” In 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI 20), pp. 851-877, 2020.
A. Gupta, R. Christie and R. Manjula, “Scalability in internet of things: features, techniques and research challenges,” Int. J. Comput. Intell. Res, 13(7), pp. 1617-1627, 2017.
A. Čolaković and M. Hadžialić, “Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues,” Computer networks, 144, pp. 17-39, 2018.
A. Angelopoulos, E. T. Michailidis, N. Nomikos, P. Trakadas, A. Hatziefremidis, S. Voliotis and T. Zahariadis, “Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects,” Sensors, 20(1), pp. 109, 2019.
I. Banerjee, Y. Ling, M.C. Chen, S.A. Hasan, C.P. Langlotz, N. Moradzadeh, B. Chapman, T. Amrhein, D. Mong, D.L. Rubin and O. Farri, “Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification,” Artificial intelligence in medicine, 97, pp.79-88, 2019.
P. Dhruv and S. Naskar, “Image classification using convolutional neural network (CNN) and recurrent neural network (RNN): A review,” Machine learning and information processing: proceedings of ICMLIP 2019, pp. 367-381, 2020.
J. Rahm, D. Henselmann and L. Urbas, “Synchronization network of data models in the process industry,” In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2021, September, pp. 1-8. IEEE.
J. Dokulil and S. Benkner, “Exploring the performance of fine-grained synchronization and data exchange across process boundaries on modern multi-core architectures,” In Computational Science–ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part V 19 (pp. 514-520), Springer International Publishing.
G. Fusco and L. Aversano, “An approach for semantic integration of heterogeneous data sources,” PeerJ Computer Science, 6, e254, 2020.
A. Jha, M. Dave and S. Madan, “Big data security and privacy: A review on issues challenges and privacy preserving methods,” International Journal of Computer Applications, 975, pp. 8887, 2017.
P. Rosati and T. Lynn, “Measuring the Business Value of Infrastructure Migration to the Cloud,” Measuring the Business Value of Cloud Computing, pp. 19-37, 2020.
C.K. Wong, J. Chen, S.K. Fung, M.M. Mok, Y.L. Cheng, I. Kong, W.K. Lo, S.L. Lui, T.M. Chan and C.L. Lam, “Direct and indirect costs of end-stage renal disease patients in the first and second years after initiation of nocturnal home haemodialysis, hospital haemodialysis and peritoneal dialysis,” Nephrology Dialysis Transplantation, 34(9), pp.1565-1576, 2019.
O. M. Horani, A. S. Al-Adwan, H. Yaseen, H. Hmoud, W. M. Al-Rahmi and A. Alkhalifah, “The critical determinants impacting artificial intelligence adoption at the organizational level,” Information Development, 02666669231166889, 2023.
H. Kornelius, I. Bernarto, A. W. Widjaja and A. Purwanto, “Competitive strategic maneuverability: The missing link between strategic planning and firm’s performance,” International Journal of Advanced Science and Technology, 29(3), 7413-7422, 2020.
J. Hutson and J. Ceballos, “Rethinking education in the age of AI: the importance of developing durable skills in the Industry 4.0,” Journal of Information Economics, 1(2), 2023.
A. S. George, A. H. George and A. G. Martin, “ChatGPT and the future of work: a comprehensive analysis of AI'S impact on jobs and employment,” Partners Universal International Innovation Journal, 1(3), pp. 154-186, 2023.
S. Khanna and S. Srivastava, “Patient-centric ethical frameworks for privacy, transparency, and bias awareness in deep learning-based medical systems,” Applied Research in Artificial Intelligence and Cloud Computing, 3(1), pp. 16-35, 2020.
A. Cebulla, Z. Szpak, C. Howell, G. Knight and S. Hussain, “Applying ethics to AI in the workplace: the design of a scorecard for Australian workplace health and safety,” AI & society, 38(2), pp. 919-935, 2023.
J. Du, “The Impact of Artificial Intelligence Adoption on Employee Unemployment: A Multifaceted Relationship,” International Journal of Social Sciences and Public Administration, 2(3), pp. 321-327, 2024.
M. Xue, X. Cao, X. Feng, B. Gu and Y. Zhang, “The Effect of Artificial Intelligence (AI) on Firm Labor Structure,” In HICSS pp. 1-10, 2022, January.
V. Franzoni, “From black box to glass box: advancing transparency in artificial intelligence systems for ethical and trustworthy AI,” In International Conference on Computational Science and Its Applications, 2023, June, pp. 118-130, Cham: Springer Nature Switzerland.
N. L. Eyo-Udo, A. C. Odimarha and O. O. Kolade, “Ethical supply chain management: balancing profit, social responsibility, and environmental stewardship,” International Journal of Management & Entrepreneurship Research, 6(4), pp. 1069-1077, 2024.
Ersoy, E. “EU artificial intelligence regulation,” In Computational Intelligence and Blockchain in Complex Systems, 2024, pp. 113-119, Morgan Kaufmann.
S. Watkins, “ISO/IEC 27001: 2022: An introduction to information security and the ISMS standard,” 2022.
H. Roberts, J. Cowls, J. Morley, M. Taddeo, V. Wang and L. Floridi, “The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation,” Ethics, governance, and policies in artificial intelligence, 47-79, 2021.
B. Zareiyan and M. Korjani, “Blockchain technology for global decentralized manufacturing: challenges and solutions for supply chain in fourth industrial revolution,” Int. J. Adv. Robot. Autom, 3(2), pp. 1-10, 2018.
J. Leng, Y. Zhong, Z. Lin, K. Xu, D. Mourtzis, X. Zhou, P. Zheng, Q. Liu, J.L. Zhao and W. Shen, “Towards resilience in Industry 5.0: A decentralized autonomous manufacturing paradigm,” Journal of Manufacturing Systems, 71, pp.95-114, 2023.
H. Roberts, J. Cowls, E. Hine, J. Morley, V. Wang, M. Taddeo and L. Floridi, “Governing artificial intelligence in China and the European Union: Comparing aims and promoting ethical outcomes,” The Information Society, 39(2), pp. 79-97, 2023.
G. Fragapane, R. De Koster, F. Sgarbossa and J. O. Strandhagen, “Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda,” European Journal of Operational Research, 294(2), pp. 405-426, 2021.
C. Saidy, K. Xia, A. Kircaliali, R. Harik and A. Bayoumi, “The application of statistical quality control methods in predictive maintenance 4.0: An unconventional use of statistical process control (SPC) charts in health monitoring and predictive analytics,” In Advances in Asset Management and Condition Monitoring: COMADEM 2019, 2020, pp. 1051-1061, Cham: Springer International Publishing.
A. D. A. Stanton and W. W. Stanton, “Closing the skills gap: Finding skilled analytics professionals for a dynamically changing data-driven environment,” Applied Marketing Analytics, 5(2), 170-184, 2019.
CE Okafor, AC Onovo, OI Ani, CM Obele, D Dziki, CC Ihueze, UC Okonkwo. Mathematical study of bio-fibre comminution process as first step towards valorization of post-harvest waste materials. Cleaner Materials. 2022 Jun 1;4:100067
A. Alibasic, H. Upadhyay, M. C. E. Simsekler, T. Kurfess, W. L. Woon and M. A. Omar, “Evaluation of the trends in jobs and skill-sets using data analytics: A case study,” Journal of Big Data, 9(1), 32, 2022.
O Ani, A Ani, J Chukwuneke. Spectrophotometric Data in Human Immunodeficiency Virus (HIV)-Antiretroviral Drug Coated Blood Interactions. Journal of Biosciences and Medicines. 2015 Aug 5;3(8):44-52.
G. Li, C. Yuan, S. Kamarthi, M. Moghaddam and X. Jin, “Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis,” Journal of Manufacturing Systems, 60, pp. 692-706, 2021.
CE Okafor, DU Okpe, OI Ani, UC Okonkwo. Development of carbonized wood/silicon dioxide composite tailored for single-density shoe sole manufacturing. Materials Today Communications. 2022 Aug 1;32:104184.
B., Gao, S., Peng, T., Li, F., Wang, J., Guo, C., Liu, & H. Zhang, Integration of improved meta-heuristic and machine learning for optimizing energy efficiency in additive manufacturing process. Energy, 306, 132518. 2024.
C. C., Ihueze, C. E., Okafor, & E. O. Obende. Robust design and intelligent modelling of organic-based composites for armoury applications. SN Computer Science, SPRINGER. 2024, DOI : 10.1007/s42979-024-03199-0.
N., Bharat, & P. S. C. Bose. Integrating genetic algorithms, particle swarm, and neural networks for wear optimization of AA7178 matrix with Nano-SiC particles. JOM, 2024, 76(6), 2772-2785.
J., Song, B., Wang, & X. Hao, Optimization Algorithms and Their Applications and Prospects in Manufacturing Engineering. Materials, 2024, 17(16), 4093.