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Processes
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Lean-Green DOE Methods for Improving Processes and Optimal Manufacturing Quality
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Special Issue "Lean-Green DOE Methods 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: 20 January 2024 | Viewed by 2983

Special Issue Editor

Dr. George J. Besseris

E-Mail Website
Guest Editor
Mechanical Engineering Department, The University of West Attica, 12241 Egaleo, Greece
Interests: mechanical processes; optimization; chemical engineering
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 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
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. 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

  • design of experiments (DOE)
  • orthogonal array (OA)
  • manufacturing 4.0
  • manufacturing processes and quality

Published Papers (2 papers)

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Article
Using Lean-and-Green Supersaturated Poly-Factorial Mini Datasets to Profile Energy Consumption Performance for an Apartment Unit
by
Spyridon Zarkadas
and
George Besseris
Processes 2023, 11(6), 1825; https://doi.org/10.3390/pr11061825 - 15 Jun 2023
Viewed by 489
Abstract
The Renovation Wave for Europe initiative aspires to materialize the progressive greening of 85–95% of the continental older building stock as part of the European Green Deal objectives to reduce emissions and energy use. To realistically predict the energy performance even for a [...] Read more.
The Renovation Wave for Europe initiative aspires to materialize the progressive greening of 85–95% of the continental older building stock as part of the European Green Deal objectives to reduce emissions and energy use. To realistically predict the energy performance even for a single apartment building is a difficult problem. This is because an apartment unit is inherently a customized construction which is subject to year-round occupant use. We use a standardized energy consumption response approach to accelerate the setting-up of the problem in pertinent energy engineering terms. Nationally instituted Energy Performance Certification databases provide validated energy consumption information by taking into account an apartment unit’s specific shell characteristics along with its installed electromechanical system configuration. Such a pre-engineered framework facilitates the effect evaluation of any proposed modifications on the energy performance of a building. Treating a vast building stock requires a mass-customization approach. Therefore, a lean-and-green, industrial-level problem-solving strategy is pursued. The TEE-KENAK Energy Certification database platform is used to parametrize a real standalone apartment. A supersaturated mini dataset was planned and collected to screen as many as 24 controlling factors, which included apartment shell layout details in association with the electromechanical systems arrangements. Main effects plots, best-subsets partial least squares, and entropic (Shannon) mutual information predictions—supplemented with optimal shrinkage estimations—formed the recommended profiler toolset. Four leading modifications were found to be statistically significant: (1) the thermal insulation of the roof, (2) the gas-sourced heating systems, (3) the automatic control category type ‘A’, and (4) the thermal insulation of the walls. The optimal profiling delivered an energy consumption projection of 110.4 kWh/m2 (energy status ‘B’) for the apartment—an almost 20% reduction in energy consumption while also achieving upgrading from the original ‘C’ energy status. The proposed approach may aid energy engineers to make general empirical screening predictions in an expedient manner by simultaneously considering the apartment unit’s structural configuration as well as its installed electromechanical systems arrangement. Full article
(This article belongs to the Special Issue Lean-Green DOE Methods for Improving Processes and Optimal Manufacturing Quality)
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Article
Lean-and-Green Strength Performance Optimization of a Tube-to-Tubesheet Joint for a Shell-and-Tube Heat Exchanger Using Taguchi Methods and Random Forests
by
Panagiotis Boulougouras
and
George Besseris
Processes 2023, 11(4), 1211; https://doi.org/10.3390/pr11041211 - 14 Apr 2023
Viewed by 2012
Abstract
The failing tube-to-tubesheet joint is identified as a primary quality defect in the fabrication of a shell-and-tube heat exchanger. Operating in conditions of high pressure and temperature, a shell-and-tube heat exchanger may be susceptible to leakage around faulty joints. Owing to the ongoing [...] Read more.
The failing tube-to-tubesheet joint is identified as a primary quality defect in the fabrication of a shell-and-tube heat exchanger. Operating in conditions of high pressure and temperature, a shell-and-tube heat exchanger may be susceptible to leakage around faulty joints. Owing to the ongoing low performance of the adjacent tube-to-tubesheet expansion, the heat exchanger eventually experiences malfunction. A quality improvement study on the assembly process is necessary in order to delve into the tight-fitting of the tube-to-tubesheet joint. We present a non-linear screening and optimization study of the tight-fitting process of P215NL (EN 10216-4) tube samples on P265GH (EN 10028-2) tubesheet specimens. A saturated fractional factorial scheme was implemented to screen and optimize the tube-to-tubesheet expanded-joint performance by examining the four controlling factors: (1) the clearance, (2) the number of grooves, (3) the groove depth, and (4) the tube wall thickness reduction. The adopted ‘green’ experimental tactic required duplicated tube-push-out test trials to form the ‘lean’ joint strength response dataset. Analysis of variance (ANOVA) and regression analysis were subsequently employed in implementing the Taguchi approach to accomplish the multifactorial non-linear screening classification and the optimal setting adjustment of the four investigated controlling factors. It was found that the tube-wall thickness reduction had the highest influence on joint strength (55.17%) and was followed in the screening hierarchy by the number of grooves (at 30.47%). The groove depth (at 7.20%) and the clearance (at 6.84%) were rather weaker contributors, in spite of being evaluated to be statistically significant. A confirmation run showed that the optimal joint strength prediction was adequately estimated. Besides exploring the factorial hierarchy with statistical methods, an algorithmic (Random Forest) approach agreed with the leading effects line-up (the tube wall thickness and the number of grooves) and offered an improved overall prediction for the confirmation-run test dataset. Full article
(This article belongs to the Special Issue Lean-Green DOE Methods for Improving Processes and Optimal Manufacturing Quality)
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