Machine Learning Techniques for Smart Manufacturing: Applications and Challenges in Industry 4.0

25Feb

Machine Learning Techniques for Smart Manufacturing: Applications and Challenges in Industry 4.0

by Bojana Bajic R&D

The Industry 4.0 is now underway, changing traditional manufacturing into smart manufacturing and creating new opportunities, where machines learn to understand those processes, interact with environment and intelligently adapt their behaviour. Big data and artificial intelligence (AI) make machines in industrial production smarter than before addressing the question of how to build computers that improve automatically through experience. Machine learning (ML), as a subfield of AI, has become the main driver of those innovations in industrial sectors, which provides the opportunity to further accelerate discovery processes as well as enhancing decision making.

However, ML algorithms learn directly from the examples, data and experience and are able to figure out how to perform important tasks by generalizing from them. The paper Machine Learning Techniques for Smart Manufacturing: Applications and Challenges in Industry 4.0, published in the 9th International Scientific and Expert Conference TEAM 2018, summarizes challenges and future trends of ML applications for smart manufacturing and provides an overview of several ML algorithms (e.g. support vector machine, k-nearest neighbor, neural network etc.) that are able to give the answers to those issues and avoid the potential problems in the future.