Artificial Intelligence (AI) provides several applications for systems to work in an automated environment and Machine Learning (ML) is one of those applications. ML provides the ability for a computer system to learn, adapt and improve from previous accessed data and procedures, without the need to be specially programmed. Firstly, the learning begins with the data collection and observation while using the data to find patterns and improve the decisions of the system in the future. The goal of the system is to learn automatically and readapt its procedures, without the need of human intervention or supervision. Note must be made that the difference in context for the ML and classical algorithms can be easily understood regarding a simple text. Classical algorithms interpret it as a sequence of keywords, while the ML algorithms use semantic analysis and mimic the human ability to understand the content of the text. The most uses ML algorithms are supervised ML algorithms, unsupervised ML algorithm, semi – supervised ML algorithms and reinforcement ML algorithms.
Furthermore, the techniques used in ML can discover valuable patterns in data. This characteristic is very valuable in the manufacturing sector and leads to Advanced Manufacturing (AM) procedures. Because the manufacturing world is ever evolving, there are no universally applicable methods and is important to have a clear understanding of the requirements of each task. ML applications are useful in the manufacturing because:
- deal with different data (numerical, nominal, text, and image),
- handle noise, outliers, fuzzy data,
- real time processing,
- deal with large data sets and data of high dimensions,
- produce easily understood results,
- simple to implement.
Finally, there are several advantages in the ML application for the AM, such are:
- the techniques are able to handle NP complete problems for optimization of intelligent manufacturing
- ML handles high – dimensional, multivariable data and extracts relationships within large datasets and evolving manufacturing environment
- ML increases the understanding of the manufacturing domain
- Improves the lifecycle of the manufacturing process
- Discovers unknown relationships between the manufacturing entities (Pham & Afify, 2015)
Future workers in advanced manufacturing need to be trained not only in cutting edge technology, but also in creativity, team leading, problem solving, self-learning, adaptability and flexibility in order to be employable and provide services of high quality. DTAM’s training curriculum aims to fulfil this emergent need and provide and integrated programme with the appropriate balance of skills for advanced manufacturing workforce of today and tomorrow.
Thus, one of five Innovative Training Modules of DTAM will be dedicated to Machine Learning as a modular digital training pathway for IT and OT technicians in AM.
Stay tuned to take full advantage of the DTAM training course we are working on.
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 Pham, D., & Afify, A. (2015). Machine-learning techniques and their applications in manufacturing . Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering, 219(5): 395-412.
 Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016). Machine learning in manufacturing: advantages, challenges and applications. Production & Manufacturing Research, 4:1, 23-45.