Machine Learning (ML) is a subset of AI, which is concerned with the design and development of algorithms that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed to do so. In other words, ML is the process of training a model using data, so that it can make predictions or decisions about new data that it has not seen before.

Nowadays, manufacturing is entering a period of substantial innovation and change driven by the increased integration of sensors and the Internet-of-things (IoT), increased data availability, and advances in robotics and automaton. Its operations across industries are faced with the challenge of meeting throughput, quality, and cost objectives while ensuring a safe working environment. This has become increasingly difficult due to growing product and process complexity, higher variability in customer demand and preferences, and relentless competitive pressures [1]. Artificial Intelligence (AI) is a great ally in meeting these needs.

In recent years, Artificial Intelligence has shown significant progress in automating activities that are associated with human thinking, such as planning, decision making, and problem solving. An over sixfold increase in the number of publications from 2015 to 2020 an estimated 15.7 trillion-dollar worth of economy in 2030 [2] are only a few indications of the vast existing and future impact of AI in both academia and industry.

AI offers unique capabilities for problem-solving, especially in identifying and classifying multivariate, nonlinear patterns in operational and performance data that are hidden to plant engineers. There is a huge amount of continuously generated data from machines, ambient sensors, controllers, and labour records, which can be categorized into environmental data, process data, production operation data, and measurement or check data. These data are the reflection of the physical machine characteristics [3]. AI techniques have advanced the state of monitoring, diagnosis, and prognosis, which are of critical value to modern manufacturing. This allows for an abundance of applications, such as predictive maintenance or quality control.

Some of the applications of AI are listed below:

  • Quality control: AI can be used to inspect and detect defects in real-time, improving the quality control process and reducing defects.
  • Predictive maintenance: By analysing sensor data, AI can predict when maintenance is required, reducing downtime, and increasing productivity.
  • Process optimization: AI can analyse data from various sensors to optimize manufacturing processes, reducing waste, and increasing efficiency.
  • Supply chain management: AI can be used to monitor and optimize supply chains, reducing costs and increasing delivery efficiency.
  • Autonomous robotics: AI-powered robots can perform repetitive and dangerous tasks, increasing productivity and reducing the risk of accidents.
  • Product design: AI can assist in designing products by generating and evaluating various design options based on customer requirements and market trends.

It is important to recognize the significance of getting companies ready for the shift towards digitalization and adapting to the challenges that come with technological disruptions. This highlights the need for educating and providing training for the younger generation in these areas, which is where the DTAM project comes in. The main objective of the DTAM project is to prepare students in thIe field of digital transformation and equip them with the necessary skills to face the challenges in this area.

Our training course on Digital Transformation Technologies is on its way to being pilot tested. Stay in the loop as we are going to be sharing more exciting news very soon.


[1] Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., and Zhang, J. (August 13, 2020). “Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook.” ASME. J. Manuf. Sci. Eng. November 2020; 142(11):110804.

[2] PWC, 2021. Pwc’s Global Artificial Intelligence Study: Sizing the Prize. 

[3] Mojtaba Mozaffar, Shuheng Liao, Xiaoyu Xie, Sourav Saha, Chanwook Park, Jian Cao, Wing Kam Liu, Zhengtao Gan, “Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives, Journal of Materials Processing Technology”, Volume 302, 2022.

[4] Featured image: Freepik/vectorjuice

Recommended Posts

No comment yet, add your voice below!

Add a Comment

Your email address will not be published. Required fields are marked *