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Applied Sciences
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Intelligent Manufacturing Systems: Monitoring, Optimization and Control
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Intelligent Manufacturing Systems: Monitoring, Optimization and Control

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 3172

Special Issue Editor

Dr. James M. Griffin

E-Mail Website
Guest Editor
Non Destructive Testing and Manufacturing Engineering, Coventry University, Coventry CV1 5FB, UK
Interests: manufacturing mechatronics; nondestructive testing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current manufacturing systems are required to produce both extreme tight tolerances and anomaly free safety-critical parts, but this is becoming very difficult to maintain and causes huge bottlenecks within the current supply lines. In addition, current methods rely on destructive tests on coupons which often have nonreplicated machine processes and are also not to scale, which does not represent the true nature of the material characteristics as experienced by the actual part during actual machining. It is not possible, however, to make destructive tests on the actual part due to no damage tolerance being present for in-service part(s). By using advanced methods of intelligent NDT, such measurements can be made to the part whilst also adhering to noninvasive compliance. This is one method that promises to address such bottlenecks; however, there are other aspects of intelligent manufacturing that also address such bottlenecks, such as improving efficiency through optimization, minimum wastage through intelligent control, as well as in situ monitoring, where parts are inspected during the manufacturing process to give further information in terms of machining conformance. In-situ monitoring also works in tandem with NDT methods. Such monitoring technologies can be made to function with autonomy and intelligence; such integrated systems act as advanced tools used to improve manufacturing.

All four areas within intelligent manufacturing systems can be applied to recycling materials where there is a huge need to reuse and remanufacture products or even, extend their life through remanufacturing/refurbishment. This reuse of materials is less energy-intensive than manufacturing a raw product from scratch. Whilst on the topic of recycling, the use of multipurpose 3D printing with CNC under intelligent manufacturing systems will also be considered for this Special Issue.

Dr. James M. Griffin
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • fundamental investigations for understanding intelligent manufacturing systems
  • manufacturing-monitoring technologies
  • NDT/monitoring applied to difficult-to-cut materials
  • high removal rates and tool condition monitoring
  • sensors condition monitoring
  • intelligent control of coolants and lubricants
  • flexible control systems
  • intelligent preparation and maintenance
  • micro and precise intelligent machining technologies
  • using intelligent manufacturing systems towards achieving zero-carbon manufacturing
  • optimized algorithms for optimized design for CNC machining or 3D printing

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Published Papers (3 papers)

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Research

33 pages, 3983 KiB  
Article
AI-Driven Random Forest Model and the Six Sigma Approach for Enhancing Offset Printing Process and Product Quality
by Diana Bratić, Petar Miljković, Denis Jurečić and Tvrtko Grabarić
Appl. Sci. 2025, 15(5), 2266; https://doi.org/10.3390/app15052266 - 20 Feb 2025
Viewed by 652
Abstract
The Six Sigma methodology for quality improvement enabled a high degree of process compliance and enhanced process capability. This research develops a new model for optimizing the offset printing process based on the Six Sigma approach, with the aim of reducing process variability [...] Read more.
The Six Sigma methodology for quality improvement enabled a high degree of process compliance and enhanced process capability. This research develops a new model for optimizing the offset printing process based on the Six Sigma approach, with the aim of reducing process variability and achieving stable, predictable production outcomes. Special focus was placed on defining Critical Product Characteristics (CPCs) and Critical to Quality (CTQs) points and analysing their impact on process output quality, defined by the sigma level. Based on the research, variability limits of production parameters were defined to ensure consistency and high product quality. The integration of Artificial Intelligence (AI) within the Six Sigma framework allowed for additional automation and model adaptation to changing production conditions. The use of the Random Forest model enabled efficient analysis of critical variability points, prediction of potential deviations, and real-time process adjustment. AI is utilized to improve precision and efficiency in quality management, which further enhances process stability and optimization in line with the dynamic demands of modern production. The proposed model represents an innovative approach that facilitates maintaining stable production results and provides a sustainable foundation for future process optimizations in the printing industry. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems: Monitoring, Optimization and Control)
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15 pages, 3999 KiB  
Article
Multi-Objective Optimization of Injection Parameters and Energy Consumption Based on ANN-Differential Evolution
by Devic Oktora, Yu-Hung Ting and Sukoyo
Appl. Sci. 2025, 15(2), 826; https://doi.org/10.3390/app15020826 - 16 Jan 2025
Cited by 1 | Viewed by 836
Abstract
Injection molding (IM) is one complex manufacturing process characterized by nonlinear behavior. Unlike classic linear modeling techniques like simple regression, many machine learning (ML) models have the ability to adjust to the nonlinear behaviors and interactions between input and output parameters. Artificial neural [...] Read more.
Injection molding (IM) is one complex manufacturing process characterized by nonlinear behavior. Unlike classic linear modeling techniques like simple regression, many machine learning (ML) models have the ability to adjust to the nonlinear behaviors and interactions between input and output parameters. Artificial neural networks (ANNs) specifically have demonstrated exceptional performance in problems involving nonlinear modeling. This work will employ complete factorial design of experiments (DoE) to acquire a dataset which is both resilient and suitable for training, validation, and testing purposes. The predictive model demonstrated outstanding performance throughout the training, validation, and test sets. The aggregate R2 values for the training, validation, and tests datasets were 97.58%, 93.76%, and 91.31%, respectively, demonstrating a strong ability to accurately foresee outcomes. Differential evolution (DE) successfully achieved a 2% decrease in weight and a notable 14% decrease in energy consumption. The results indicate that combining an ANN with DE is a viable approach for enhancing injection molding parameters, especially in scenarios with multiple objectives. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems: Monitoring, Optimization and Control)
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17 pages, 6040 KiB  
Article
3D Printed Microfluidic Separators for Solid/Liquid Suspensions
by Marijan-Pere Marković, Krunoslav Žižek, Ksenija Soldo, Vjeran Sunko, Julijan Zrno and Domagoj Vrsaljko
Appl. Sci. 2024, 14(17), 7856; https://doi.org/10.3390/app14177856 - 4 Sep 2024
Cited by 1 | Viewed by 1249
Abstract
This study investigates the fabrication of 3D-printed microfluidic devices for solid/liquid separation, focusing on additive manufacturing technologies. Stereolithography (SLA) and fused filament fabrication (FFF) were used to create microseparators with intricate designs optimized for separation efficiency. Model suspensions containing quartz sand, nano-calcium carbonate, [...] Read more.
This study investigates the fabrication of 3D-printed microfluidic devices for solid/liquid separation, focusing on additive manufacturing technologies. Stereolithography (SLA) and fused filament fabrication (FFF) were used to create microseparators with intricate designs optimized for separation efficiency. Model suspensions containing quartz sand, nano-calcium carbonate, and talc-based baby powder in water were prepared using an electric magnetic stirrer and conveyed into the microseparator via a peristaltic pump. Different flow rates were tested to evaluate their influence on the separation efficiency. The highest separation efficiency for the model systems was observed at a flow rate of 200 mL min−1. This was due to the increased turbulence at higher flow rates, which hindered the secondary flow perpendicular to the primary flow direction. The particle size distribution before and after separation was analyzed using sieve and laser diffraction, and particle morphology was inspected by scanning electron microscopy. The laser diffraction analysis revealed post-separation particle size distributions, indicating that Outlet 1 (external stream) consistently captured larger particles more effectively than Outlet 2 (internal stream). This work highlights the potential of additive manufacturing to produce customized microfluidic devices, enabling rapid prototyping and fine-tuning of complex geometries, thus enhancing separation efficiency across various industrial applications. Full article
(This article belongs to the Special Issue Intelligent Manufacturing Systems: Monitoring, Optimization and Control)
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