Special Issue "Industry 4.0 Based Smart Manufacturing Systems"
Deadline for manuscript submissions: 30 November 2020.
Interests: Deep and Machine Learning; Transfer Learning; Explainable and Transparent Artificial Intelligence; Knowledge Graphs; Semantic Interoperability
In a traditional way, manufacturing means the engineering process of creating industrial products from raw materials using a variety of subtractive and additive methods. However, in recent years, the concept of manufacturing has drastically shifted. After the first wave of digitization, new and modernized technologies such as integrated sensors, advanced robotics, and artificial intelligence led to the so-called Smart Manufacturing as part of the fourth industrial revolution—often referred to as Industry 4.0. In Smart Manufacturing, production tools are connected to constantly gather data, monitor production processes, and perform real-time optimization. Smart Manufacturing therefore includes not only data collection and processing, but also inferring from and reasoning about data by means of cognitive computing to improve the end product. In doing so, the vision of Smart Manufacturing leads to a self-monitoring and self-optimization of the entire end-to-end manufacturing process.
The key challenges of Smart Manufacturing are manifold, and several aspects need to be taken into consideration:
- New ways of data acquisition that require implementing new sensors and the capability for connectivity in production machines and products, as well as new ways to store and propagate such data in a meaningful way;
- Employing data science approaches to automate or optimize manufacturing to remove ‘trial-and-error’ approaches;
- Developing new robotics and closed loop control feedback at the hardware level;
- Sustainably transferring and deploying solutions into the world while addressing broader clean energy challenges and reducing material waste for the environment.
Industry 4.0 adds, among other things, aspects of business model development to this mostly technological perspective. The new value of data leads to new and changed business models and opportunities regarding the internal optimization of business processes. However, we have not yet fully understood how data can be managed as a central resource and how the full potential of data as a resource can be harnessed.
The aim of the edition “Industry 4.0-Based Smart Manufacturing Systems” is therefore to present new and innovative methods in which data can be better and more efficiently extracted, collected, processed, and finally used in Smart Manufacturing environments.
Prof. Dr. Tobias Meisen
Manuscript Submission Information
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- Smart Manufacturing
- Industry 4.0
- Artificial intelligence
- Data-driven production
- Data as an asset