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Smart Design and Advanced Manufacturing: Integrating Emerging Technologies for Improved Production Processes

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

Deadline for manuscript submissions: 30 May 2025 | Viewed by 1656

Special Issue Editor

Dr. Kouroush Jenab

E-Mail Website
Guest Editor
Department of Engineering Sciences, Morehead State University, 150 University Blvd, Morehead, KY 40351, USA
Interests: intelligent fault detection and recovery; condition based monitoring, reliability; manufacturing systems; robotics; VR/RL based failure analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart Design, emphasizing connectivity, virtualization, and data utilization, is a strategic design that would bring optimal utilization and efficiency to users while reducing environmental impacts. On the other hand, Advanced Manufacturing focuses on manufacturing process technologies such as automation, robotics, and additive manufacturing. Using Smart Design and Advanced Manufacturing Concepts, the organizations continue to re-design products, processes, and customer support at optimal growth and costs while challenging the market by leveraging technology as a game changer. Industry 4.0 would prepare the organization for these challenges by integrating advanced control systems with Internet technology, enabling communication.

As a result, further development in Smart Design (Connectivity, Virtualization, and Data Utilization), Advanced Manufacturing (Automation, Robotics, Process Sustainability, Integration of Technologies, Computational Methods, Artificial Intelligence, 3D Printing), and Industry 4.0 (Development of Cyber-Physical Systems, Integration of the Internet of things, Implementation for safety, security, privacy, and knowledge protection) becomes important.

Therefore, this Special Issue will bring together papers that particularly describe recent advances in these three areas. Papers that include practical experimental results are particularly encouraged.

Dr. Kouroush Jenab
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

  • smart design
  • smart manufacturing
  • Industry 4.0
  • automation
  • robotics
  • process sustainability
  • integration of technologies
  • computational methods
  • artificial intelligence
  • 3D printing

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Published Papers (2 papers)

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Research

23 pages, 5887 KiB  
Article
Exploring Multiple Pathways of Product Design Elements Using the fsQCA Method
by Yi Wang, Lijuan Sang, Weiwei Wang, Jian Chen, Xiaoyan Yang, Jun Liu, Zhiqiang Wen and Qizhao Peng
Appl. Sci. 2024, 14(20), 9435; https://doi.org/10.3390/app14209435 - 16 Oct 2024
Viewed by 568
Abstract
To address current product styling design issues, such as ignoring the joint effects of multiple styling elements when constructing perceptual imagery fitting models and thus failing to effectively identify the relationships between styling elements, a product styling design method based on fuzzy set [...] Read more.
To address current product styling design issues, such as ignoring the joint effects of multiple styling elements when constructing perceptual imagery fitting models and thus failing to effectively identify the relationships between styling elements, a product styling design method based on fuzzy set qualitative comparative analysis (fsQCA) is proposed. This method first uses semantic differential and statistical methods to obtain users’ evaluative vocabulary for the product’s perceptual imagery. Then, morphological analysis and cluster analysis are employed to establish typical product samples and extract styling elements to create a styling feature library. Perceptual imagery ratings of these styling features are obtained through expert evaluation. fsQCA is then used to analyze the different grouping relationships between styling elements and their influence on product styling imagery, aiming to match user intentions through different element combination paths. The results show that this method achieves a consistency value of 0.9 for the most optimal styling configurations, demonstrating that fsQCA can effectively identify the multiple paths of product styling elements that meet users’ needs. The contributions of this study to the related fields are: (1) providing a new perspective on the relationship between user perceptual imagery and predicted product styling elements, and (2) advancing the theoretical basis for studying multiple paths of product styling elements. The research results demonstrate that using the fsQCA-based product styling design method can accurately portray the multiple paths of product styling elements that meet users’ needs, thereby effectively improving design efficiency. Finally, a teapot styling design study is used as an example to further verify the method’s feasibility. Full article
(This article belongs to the Special Issue Smart Design and Advanced Manufacturing: Integrating Emerging Technologies for Improved Production Processes)
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15 pages, 1045 KiB  
Article
Adaptive Imputation of Irregular Truncated Signals with Machine Learning
by Tyler Ward, Kouroush Jenab and Jorge Ortega-Moody
Appl. Sci. 2024, 14(15), 6828; https://doi.org/10.3390/app14156828 - 5 Aug 2024
Viewed by 722
Abstract
In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems [...] Read more.
In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue. Full article
(This article belongs to the Special Issue Smart Design and Advanced Manufacturing: Integrating Emerging Technologies for Improved Production Processes)
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