Green Diagnostic Tools for Improving Processes and Optimal Manufacturing Quality
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".
Deadline for manuscript submissions: 15 December 2024 | Viewed by 90
Special Issue Editor
Interests: product development; product design and development; design engineering; mechanical processes; creativity and innovation; sustainability; optimization; production; production engineering; operations management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modern manufacturing uses data-centric engineering to improve production costs and product quality in a planned sustainable framework, aiming to instill future resilience in host operations. In order to enhance the performance of the process, offline experimentation is required, as it is the task-specific trials that aid in gaining new knowledge with regard to the operations under study. Opportunities for improvement rely on the data collection strategy being fruitful. Sustainable sampling offers process-specific information by minimizing production downtime for trial work. Generally speaking, green sampling also minimizes materials usage, the total machinery processing time, and manhours, while it generates less output waste. Contributions to this Special Issue should showcase data-driven results-oriented manufacturing research from all areas of specialization, with emphasis on green sampling and fast-track analysis to reach practical conclusions. Robust analysis methods that are founded on either statistical or algorithmic approaches, or even on both frameworks, but which are supported by machine intelligence methods, should complement rapid experimental schemes in explaining prediction outcomes. The main aim is to improve the green performance of a real process by probing its multi-parameter and multi-response profile, which in turn should lead to establishing cause-and-effect relationships with quantified uncertainty, while potentially incorporating fuzziness in the modeling scheme.
We invite submissions that have the right balance of experimental work in industrial operations that are assisted by empirical analysis. Selected studies should reflect high process complexity, which naturally attracts expert involvement to screen and optimize the process characteristics under uncertainty. We particularly welcome real case studies that relate to manufacturing 4.0. New statistical and algorithmic techniques based on artificial intelligence frameworks, which solve complex production improvement problems in order to make host operations greener, are particularly desirable.
Dr. George Besseris
Guest Editor
Manuscript Submission Information
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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
- green diagnostics
- statistical methods
- artificial intelligence
- manufacturing 4.0
- manufacturing processes and quality
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