AI-Driven Quality Control in Manufacturing and Construction: Enhancing Precision and Reducing Human Error

Md Morshedul; Mohammad; Md. Baki Billah; Md Miraj Hossen

doi:10.25163/engineering.3110270

Information and engineering sciences | Online ISSN 3068-0115

REVIEWS (Open Access)

Previous Contents Vol 3 (1)

AI-Driven Quality Control in Manufacturing and Construction: Enhancing Precision and Reducing Human Error

Md Morshedul Hasan¹*, Mohammad Kasedullah², Md. Baki Billah Ripon³, Md Miraj Hossen Khan⁴

+ Author Affiliations

Applied IT & Engineering 3 (1) 1-8 https://doi.org/10.25163/engineering.3110270

Submitted: 07 April 2025 Revised: 11 June 2025 Published: 18 June 2025

Abstract

In recent decades, the demand for higher quality and precision in manufacturing and construction has increased significantly due to globalization, customer expectations, and the complexity of modern projects. Traditional quality control (QC) systems, reliant on manual inspections and human oversight, often fall short in delivering consistency, speed, and accuracy. This review explores how artificial intelligence (AI) technologies are reshaping the landscape of quality control in both manufacturing and construction industries. The methodology employed in this review includes a synthesis of peer-reviewed journal articles, industry reports, and case studies published before 2018, focusing on practical applications of AI, such as machine learning (ML), computer vision, and robotics in quality assurance. Evidence from these sources was critically analyzed to extract common trends, successes, and challenges in implementing AI-driven QC solutions. The results indicate that AI systems outperform traditional QC processes in defect detection, predictive maintenance, and process optimization. In manufacturing, AI tools identify product anomalies in real-time with remarkable accuracy. In construction, drones, sensors, and ML algorithms ensure structural integrity, monitor progress, and minimize material waste. AI also facilitates adaptive learning, enabling systems to evolve and improve with continuous data input.The review concludes that AI-driven quality control enhances efficiency, reduces human error, and lowers operational costs in both sectors. However, successful integration demands robust infrastructure, skilled personnel, and regulatory frameworks to address ethical concerns and safety standards. Ultimately, AI's role in QC is not a replacement of human expertise but a complement that augments capabilities and fosters innovation.

Keywords: Artificial Intelligence (AI), Quality Control (QC), Machine Learning (ML), Computer Vision, Predictive Maintenance.

References

Adams, S. J., Henderson, R. D. E., Yi, X., & Babyn, P. (2020). Artificial intelligence solutions for analysis of X-ray images. Canadian Association of Radiologists Journal, 72(1), 60–72. https://doi.org/10.1177/0846537120941671

Alamuru, S., Reddy, G. S., & Raju, M. J. (2024). Artificial intelligence and machine learning for defect detection in castings. Journal of Physics Conference Series, 2837(1), 012079. https://doi.org/10.1088/1742-6596/2837/1/012079

Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., Akinade, O. O., & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299. https://doi.org/10.1016/j.jobe.2021.103299

Aminizadeh, S., Heidari, A., Dehghan, M., Toumaj, S., Rezaei, M., Navimipour, N. J., Stroppa, F., & Unal, M. (2024). Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service. Artificial Intelligence in Medicine, 149, 102779. https://doi.org/10.1016/j.artmed.2024.102779

Ashebir, D. A., Hendlmeier, A., Dunn, M., Arablouei, R., Lomov, S. V., Di Pietro, A., & Nikzad, M. (2024). Detecting Multi-Scale Defects in material extrusion Additive manufacturing of Fiber-Reinforced thermoplastic composites: A review of challenges and Advanced Non-Destructive Testing techniques. Polymers, 16(21), 2986. https://doi.org/10.3390/polym16212986

Borboni, A., Reddy, K. V. V., Elamvazuthi, I., Al-Quraishi, M. S., Natarajan, E., & Ali, S. S. A. (2023). The expanding role of artificial intelligence in collaborative robots for industrial applications: A systematic review of recent works. Machines, 11(1), 111. https://doi.org/10.3390/machines11010111

Bhati, D., Neha, F., & Amiruzzaman, M. (2024). A survey on Explainable Artificial intelligence (XAI) techniques for visualizing deep learning models in medical imaging. Journal of Imaging, 10(10), 239. https://doi.org/10.3390/jimaging10100239

Boppana, V. R. (2022, October 14). Machine Learning and AI Learning: Understanding the Revolution. https://acadexpinnara.com/index.php/JIT/article/view/368

Bayomi, N., & Fernandez, J. E. (2023). Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges. Drones, 7(10), 637. https://doi.org/10.3390/drones7100637

Cognominal, M., Patronymic, K., & Wankowicz, A. (2021, July 2). Evolving field of autonomous mobile robotics: technological advances and applications. https://fusionproceedings.com/fmr/1/article/view/28

Dandanelle, K., & Tomasson, M. (2018). Stories from construction inspections A case study of the challenges in the inspection process at a major construction company in Sweden. https://odr.chalmers.se/items/2e3e94e2-f3fd-4d37-8c32-c11f01bb63bf

Duarte, J. G., Duarte, M. G., Piedade, A. P., & Mascarenhas-Melo, F. (2025). Rethinking Pharmaceutical Industry with Quality by Design: Application in Research, Development, Manufacturing, and Quality Assurance. The AAPS Journal, 27(4). https://doi.org/10.1208/s12248-025-01079-w

Elizabeth, I., & Barshilia, H. C. (2024). A Comprehensive Review on Corrosion Detection Methods for Aircraft: Moving from Offline Methodologies to Real-Time Monitoring Combined with Digital Twin Technology. Engineering Science & Technology, 69–98. https://doi.org/10.37256/est.6120255638

Elenchezhian, M. R. P., Vadlamudi, V., Raihan, R., Reifsnider, K., & Reifsnider, E. (2021). Artificial intelligence in real-time diagnostics and prognostics of composite materials and its uncertainties—a review. Smart Materials and Structures, 30(8), 083001. https://doi.org/10.1088/1361-665x/ac099f

Escobar, C. A., Macias-Arregoyta, D., & Morales-Menendez, R. (2023). The decay of Six Sigma and the rise of Quality 4.0 in manufacturing innovation. Quality Engineering, 36(2), 316–335. https://doi.org/10.1080/08982112.2023.2206679

Eagar, R. W. a. T., Program, L. F. G. O., & Mit, L. F. G. O. P. A. (2016). Investing in quality?: identifying the true value of advanced weld inspection technology. https://dspace.mit.edu/handle/1721.1/104312

Fan, C. (2022). Evaluation of Classification for Project Features with Machine Learning Algorithms. Symmetry, 14(2), 372. https://doi.org/10.3390/sym14020372

George, A. S. (2024). AI-Enabled Intelligent Manufacturing: a path to increased productivity, quality, and insights. puirp.com. https://doi.org/10.5281/zenodo.13338085

Higham, C. F., Murray-Smith, R., Padgett, M. J., & Edgar, M. P. (2018). Deep learning for real-time single-pixel video. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-20521-y

Hanafi, A., Moawed, M., & Abdellatif, O. (2024). Advancing Sustainable Energy Management: A Comprehensive review of artificial intelligence techniques in building. Deleted Journal, 53(2), 26–46. https://doi.org/10.21608/erjsh.2023.226854.1196

Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2021). Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study. Journal of Industrial Integration and Management, 07(01), 83–111. https://doi.org/10.1142/s2424862221300040

Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2020, April 14). Enhancing predictive maintenance in manufacturing using machine learning algorithms and IoT-Driven data analytics. https://cognitivecomputingjournal.com/index.php/IJAIML-V1/article/view/50

Keleko, A. T., Kamsu-Foguem, B., Ngouna, R. H., & Tongne, A. (2022). Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis. AI And Ethics, 2(4), 553–577. https://doi.org/10.1007/s43681-021-00132-6

Kekäle, T., & Phusavat, K. (2010, June 21). Integrated ESSQ management?: as a part of excellent operational and business management—a framework, integration and maturity. OuluREPO. https://oulurepo.oulu.fi/handle/10024/35380

Kulawiak, K. E. (2021). Manufacturing the platform economy. An exploratory case study of MindSphere, the industrial digital platform from Siemens (Master's thesis).

Kaul, D., & Khurana, R. (2022). Ai-driven optimization models for e-commerce supply chain operations: Demand prediction, inventory management, and delivery time reduction with cost efficiency considerations. International Journal of Social Analytics, 7(12), 59-77.

Khan, A. A., Laghari, A. A., Li, P., Dootio, M. A., & Karim, S. (2023). The collaborative role of blockchain, artificial intelligence, and industrial internet of things in digitalization of small and medium-size enterprises. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-28707-9

Lu, Y., Mathur, A. K., Blunt, B. A., Glüer, C. C., Will, A. S., Fuerst, T. P., Jergas, M. D., Andriano, K. N., Cummings, S. R., & Genant, H. K. (1996). Dual X-ray absorptiometry quality control: Comparison of visual examination and process-control charts. Journal of Bone and Mineral Research, 11(5), 626–637. https://doi.org/10.1002/jbmr.5650110510

Lee, J., Bagheri, B., & Kao, H. (2014). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. https://doi.org/10.1016/j.mfglet.2014.12.001

Leberruyer, N., Bruch, J., Ahlskog, M., & Afshar, S. (2023). Toward Zero Defect Manufacturing with the support of Artificial Intelligence—Insights from an industrial application. Computers in Industry, 147, 103877. https://doi.org/10.1016/j.compind.2023.103877

Liang, C., Le, T., Ham, Y., Mantha, B. R., Cheng, M. H., & Lin, J. J. (2024). Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry. Automation in Construction, 162, 105369. https://doi.org/10.1016/j.autcon.2024.105369

Lee, S. L., O’Connor, T. F., Yang, X., Cruz, C. N., Chatterjee, S., Madurawe, R. D., Moore, C. M. V., Yu, L. X., & Woodcock, J. (2015). Modernizing Pharmaceutical Manufacturing: from Batch to Continuous Production. Journal of Pharmaceutical Innovation, 10(3), 191–199. https://doi.org/10.1007/s12247-015-9215-8

Luo, D., Wang, K., Wang, D., Sharma, A., Li, W., & Choi, I. H. (2025). Artificial intelligence in the design, optimization, and performance prediction of concrete materials: a comprehensive review. Npj Materials Sustainability, 3(1). https://doi.org/10.1038/s44296-025-00058-8

Liang, W., Tadesse, G. A., Ho, D., Fei-Fei, L., Zaharia, M., Zhang, C., & Zou, J. (2022). Advances, challenges and opportunities in creating data for trustworthy AI. Nature Machine Intelligence, 4(8), 669–677. https://doi.org/10.1038/s42256-022-00516-1

Lee, S. M., Lee, D., & Kim, Y. S. (2019). The quality management ecosystem for predictive maintenance in the Industry 4.0 era. International Journal of Quality Innovation, 5(1). https://doi.org/10.1186/s40887-019-0029-5

Liu, C., & Chien, C. (2012). An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing. Engineering Applications of Artificial Intelligence, 26(5–6), 1479–1486. https://doi.org/10.1016/j.engappai.2012.11.009

Love, P. E., Fang, W., Matthews, J., Porter, S., Luo, H., & Ding, L. (2023). Explainable artificial intelligence (XAI): Precepts, models, and opportunities for research in construction. Advanced Engineering Informatics, 57, 102024. https://doi.org/10.1016/j.aei.2023.102024

Laofor, C., & Peansupap, V. (2012). Defect detection and quantification system to support subjective visual quality inspection via a digital image processing: A tiling work case study. Automation in Construction, 24, 160–174. https://doi.org/10.1016/j.autcon.2012.02.012

Moleda, M., Malysiak-Mrozek, B., Ding, W., Sunderam, V., & Mrozek, D. (2023). From Corrective to Predictive Maintenance—A review of maintenance approaches for the power industry. Sensors, 23(13), 5970. https://doi.org/10.3390/s23135970

Murzin, S. P. (2024). Artificial Intelligence-Driven innovations in laser processing of metallic materials. Metals, 14(12), 1458. https://doi.org/10.3390/met14121458

Matveev, A. (2025). Artificial intelligence in Maritime fleet Management: Enhancing operational efficiency and cost reduction. The American Journal of Engineering and Technology, 07(03), 133–140. https://doi.org/10.37547/tajet/volume07issue03-13

Okuyelu, O., & Adaji, O. (2024). AI-driven real-time quality monitoring and process optimization for enhanced manufacturing performance. J. Adv. Math. Comput. Sci, 39(4), 81-89.

Patel, R. (2024). Implementing AI based quality inspection system to improve quality management system performance. Theseus. https://www.theseus.fi/handle/10024/873927

Pop, G. I., Titu, A. M., & Pop, A. B. (2023). Enhancing aerospace industry efficiency and sustainability: process integration and quality management in the context of industry 4.0. Sustainability, 15(23), 16206. https://doi.org/10.3390/su152316206

Roskladka, N., & Miragliotta, G. (2024, August 2). Artificial Intelligence for predictive maintenance. https://www.politesi.polimi.it/handle/10589/222753

Rane, N., Choudhary, S., & Rane, J. (2023). Artificial Intelligence (AI) and Internet of Things (IoT) - based sensors for monitoring and controlling in architecture, engineering, and construction: applications, challenges, and opportunities. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4642197

Rožanec, J. M., Križnar, K., Montini, E., Cutrona, V., Koehorst, E., Fortuna, B., Mladenic, D., & Emmanouilidis, C. (2023). Predicting operators’ fatigue in a human in the artificial intelligence loop for defect detection in manufacturing. IFAC-PapersOnLine, 56(2), 7609–7614. https://doi.org/10.1016/j.ifacol.2023.10.1157

Tien, J. M. (2017). Internet of Things, Real-Time decision making, and artificial intelligence. Annals of Data Science, 4(2), 149–178. https://doi.org/10.1007/s40745-017-0112-5

Sarkar, B., & Paul, R. K. (2025). AI for Advanced Manufacturing and Industrial Applications. https://doi.org/10.1007/978-3-031-86091-1

Shaban, A. A., & Zeebaree, S. R. (2025). Building Scalable Enterprise Systems: The Intersection of Web Technology, Cloud Computing, and AI Marketing.

Sim, K. L., & Rogers, J. W. (2008). Implementing lean production systems: barriers to change. Management Research News, 32(1), 37–49. https://doi.org/10.1108/01409170910922014

Senthil, K., Arockiarajan, A., Palaninathan, R., Santhosh, B., & Usha, K. (2013). Defects in composite structures: Its effects and prediction methods – A comprehensive review. Composite Structures, 106, 139–149. https://doi.org/10.1016/j.compstruct.2013.06.008

Shneiderman, B. (2020). Bridging the gap between ethics and practice. ACM Transactions on Interactive Intelligent Systems, 10(4), 1–31. https://doi.org/10.1145/3419764

Sani, A. R., Zolfagharian, A., & Kouzani, A. Z. (2024). Artificial Intelligence-Augmented Additive Manufacturing: Insights on Closed-Loop 3D Printing. Advanced Intelligent Systems. https://doi.org/10.1002/aisy.202400102\

Toward Intelligent monitoring in IoT: AI Applications for Real-Time analysis and Prediction. (2024). IEEE Journals & Magazine | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/10471529/

Thakfan, A., & Salamah, Y. B. (2024). Artificial-Intelligence-Based Detection of defects and faults in Photovoltaic Systems: a survey. Energies, 17(19), 4807. https://doi.org/10.3390/en17194807

Wang, Y., Ma, H. S., Yang, J. H., & Wang, K. S. (2017). Industry 4.0: a way from mass customization to mass personalization production. Advances in manufacturing, 5(4), 311-320.

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