Dear Colleagues,

Big data analytics, together with emerging technologies such as cyberphysical systems (CPS), the Internet of Things (IoT), and artificial intelligence (AI), are core elements of smart manufacturing (SM). While IoT and CPS pave the infrastructural foundation of a smart factory, the aims of big data and AI tools are to turn a large amount of industrial big data (i.e., gathered from multiple sources such as machinery sensors, integrated enterprise systems, and external/Internet platforms) into useful insights, patterns, and predictions to allow machine-to-machine collaboration, self-awareness, self-optimization, and automated decision making. In light of this, big data and AI tools are the key to realize symmetries and simulations in digital twins and smart factories. 

However, due to severe data quality issues, manufacturing firms worldwide are facing substantial challenges and difficulties when developing, implementing, and utilizing big data and AI tools in their smart manufacturing initiatives. This phenomenon has become a major obstacle affecting digital symmetry of physical manufacturing entities in a virtual world and slowing down the progress of enterprise digital transformation in the Industry 4.0 era. This Special Issue provides a platform for researchers to share their latest research that investigates data quality issues in the SM context, as well as to propose and validate adequate technologies and solutions to deal with these data quality issues, and so ultimately facilitating the utilization of big data analytics and AI in smart manufacturing. We also welcome contributions and applied solutions using innovative algorithms, models, and methods to develop big data analytics and AI applications for smart factories.  

Potential topics include but are not limited to the following:

• Data quality affecting symmetries in digital twins;
• Data quality issues and challenges affecting digital symmetry in smart manufacturing;
• Computational and automatic methods (e.g., data imputation and clustering analysis) to improve data quality in smart manufacturing;
• Emerging solutions and technologies to address data quality issues in smart factories;
• Big data management in smart factories;
• Data governance strategies, models, and solutions for smart manufacturing;
• Data security in smart factories;
• Cyberspace security in an IoT-based smart manufacturing environment;
• Big data, pattern behavior, and data analytics in smart manufacturing;
• Artificial intelligence and machine learning techniques for smart manufacturing;
• Industrial AI applications, prototypes, and testbeds.

All submissions should fit into the scope of journal Symmetry.

Prof. Dr. Guochao Peng
Dr. Caihua Liu
Guest Editors

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. Symmetry is an international peer-reviewed open access monthly 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.