Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".
Deadline for manuscript submissions: 20 May 2024 | Viewed by 119
Special Issue Editors
Interests: intelligent manufacturing; machine learning; deep learning; data-driven modeling
Interests: intelligent manufacturing; green manufacturing; manufacturing system engineering; 3D printing and additive manufacturing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Given the focus on green and efficiency-enhancing data modeling coupled with analysis technology derived from big data and artificial intelligence, there exists a pressing need for intensive research in the domain of data-driven digital workshop operations and intelligent decision support technology. Said research is crucial for achieving enhanced green practices and heightened efficiency within discrete manufacturing enterprises' production processes. For this Special Issue on "Green Manufacturing Processes: Data Modelling and Fusion-Driven Optimization Control", we invite the submission of high-quality works focusing on the latest novel advances in the optimization of manufacturing processes.
Topics include, but are not limited to:
- New optimization control techniques to investigate the multi-axis machining processes of complex parts.
- Investigations of energy efficiency involving electricity, heat, gas, waste, and mass transfer in multi-axis machining systems, considering multi-source heterogeneous data.
- New model approaches to describing multi-axis machining energy efficiency, including both local phenomena (such as the energy and other information flow of each axis) and the total calculation of multi-axis integrated energy consumption.
- Application of advanced computer science techniques, such as machine learning and deep learning, to explore the energy efficiency optimization behavior of multi-axis processing.
Prof. Dr. Li Li
Dr. Wei Cai
Dr. Lingling Li
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. Processes 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.
Keywords
- intelligent manufacturing
- machine learning
- deep learning
- data-driven modeling
- sustainable machining
