Industry 4.0 Based Smart Manufacturing Systems 2

Dear Colleagues,

Traditionally speaking, 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 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.

After a very successful special issue on "Industry 4.0 Based Smart Manufacturing Systems", we decided to run a second volume addressing this evolving and exciting topic. Hence, we invite you (again) 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
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• smart manufacturing
• Industry 4.0
• artificial intelligence
• data-driven production
• interoperability
• data as an asset