Special Issue "Lean-Green DOE Methods for Improving Processes and Optimal Manufacturing Quality"
Deadline for manuscript submissions: 20 January 2024 | Viewed by 2983
Interests: mechanical processes; optimization; chemical engineering
Special Issues, Collections and Topics in MDPI journals
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Modern manufacturing uses data-centric engineering to improve production costs and product quality in a planned sustainable framework, aiming to instill future resilience in the host operations. Enhancing the process performance, off-line experimentation is required, because it is the task-specific trials that would aid in gaining new knowledge in relevance to the operations under study. Improvement opportunities rely on the data collection strategy to be fruitful. Sustainable sampling offers process-specific information by minimizing production downtime for trial work. Generally speaking, green sampling also minimizes materials usage, total machinery processing time, and manhours, while it generates less output waste. Therefore, designing experiments (DOE) with fractional factorial schemes (FFDs), for example, using orthogonal-array (OA) planners, may aid in accelerating process improvement studies by substantially shortening the volume of the industrial research work. Exploiting the leanness—greenness—of DOE methods, contributions to this Special Issue should showcase the 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 methods, or even on both frameworks, should complement the rapid experimental schemes in explaining the prediction outcomes. The main aim is to improve the performance of a real process by probing its multifactorial profile in cause-and-effect relationships to its one or more key process characteristics. Classical DOE and Taguchi methods are within the scope of this Special Issue.
We invite submissions that have the right balance of trial 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. Particularly welcomed are real case studies that relate to Manufacturing 4.0. New statistical and algorithmic techniques that solve complex production improvement problems by exploiting DOE-generated datasets are also desired.
Dr. George J. Besseris
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.
- design of experiments (DOE)
- orthogonal array (OA)
- manufacturing 4.0
- manufacturing processes and quality