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Advances in Digital Technology Assisted Industrial Design
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Advances in Digital Technology Assisted Industrial Design

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

Deadline for manuscript submissions: closed (20 April 2024) | Viewed by 16992

Special Issue Editors

Dr. Dong Zeng

E-Mail Website
Guest Editor
School of Architecture & Design, China University of Mining And Technology, Xuzhou 221116, China
Interests: industrial design; human–computer interaction; cognitive science; intelligent design
Dr. Chaogang Tang

E-Mail Website
Guest Editor
School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
Interests: mobile edge computing; vehicular edge computing; wireless networks; heterogeneous networks; edge intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few decades, with the aid of digital technologies, human society has undergone enormous changes, such as the evolution from the industrial era to the digital era. In particular, the influence of digital technology over industrial designs can be elaborated. On the one hand, designer-oriented intelligent design tools and platforms have been developing rapidly. For example, with the help of intelligent algorithms and new media technologies, designers are offered diverse options in design methods and design media, which greatly improves the efficiency of design work. On the other hand, digital technology has facilitated the interactions among multiple entities including people, products, environments, and society. These technologies have also affected people's behaviors, cultural forms, and aesthetic concepts, giving rise to the proposition of the harmonious coexistence of human, digital technology, and society. More importantly, these advanced technologies such as man–machine interactions and man–machine fusions are research hot spots in the industrial design field.

Potential topics include, but are not limited to:

  • Cutting-edge digital design theories, technologies, and applications;
  • Computer-aided industrial design tools;
  • Intelligent algorithms integrated applications in human–machine collaboration;
  • Digital technologies aided by innovative design;
  • Human–intelligent interaction.

Dr. Dong Zeng
Dr. Chaogang Tang
Guest Editors

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

  • industrial design
  • digital technology
  • intelligent design
  • human-machine interaction
  • collaborative design

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Published Papers (5 papers)

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Research

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23 pages, 4332 KiB  
Article
Machine Learning-Based Fatigue Level Prediction for Exoskeleton-Assisted Trunk Flexion Tasks Using Wearable Sensors
by Pranav Madhav Kuber, Abhineet Rajendra Kulkarni and Ehsan Rashedi
Appl. Sci. 2024, 14(11), 4563; https://doi.org/10.3390/app14114563 - 26 May 2024
Cited by 2 | Viewed by 1008
Abstract
Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent [...] Read more.
Monitoring physical demands during task execution with exoskeletons can be instrumental in understanding their suitability for industrial tasks. This study aimed at developing a fatigue level prediction model for Back-Support Industrial Exoskeletons (BSIEs) using wearable sensors. Fourteen participants performed a set of intermittent trunk-flexion task cycles consisting of static, sustained, and dynamic activities, until they reached medium-high fatigue levels, while wearing BSIEs. Three classification algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB), were implemented to predict perceived fatigue level in the back and leg regions using features from four wearable wireless Electromyography (EMG) sensors with integrated Inertial Measurement Units (IMUs). We examined the best grouping and sensor combinations by comparing prediction performance. The findings showed best performance in binary classification of leg and back fatigue with 95% (2 EMG + IMU sensors) and 82% (single IMU sensor) accuracy, respectively. Tertiary classification for back and leg fatigue level prediction required four sensor setups with both EMG and IMU measures to perform at 79% and 67% accuracy, respectively. The efforts presented in our article demonstrate the feasibility of an accessible fatigue level detection system, which can be beneficial for objective fatigue assessment, design selection, and implementation of BSIEs in real-world scenarios. Full article
(This article belongs to the Special Issue Advances in Digital Technology Assisted Industrial Design)
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20 pages, 4372 KiB  
Article
Study on Imagery Modeling of Electric Recliner Chair: Based on Combined GRA and Kansei Engineering
by Chengmin Zhou, Lansong Jiang and Jake Kaner
Appl. Sci. 2023, 13(24), 13345; https://doi.org/10.3390/app132413345 - 18 Dec 2023
Cited by 3 | Viewed by 1312
Abstract
This study aims to integrate data-driven methodologies with user perception to establish a robust design paradigm. The study consists of five steps: (1) theoretical research—a review of the subject background and applications of Kansei engineering and gray relational analysis (GRA); (2) algorithmic framework [...] Read more.
This study aims to integrate data-driven methodologies with user perception to establish a robust design paradigm. The study consists of five steps: (1) theoretical research—a review of the subject background and applications of Kansei engineering and gray relational analysis (GRA); (2) algorithmic framework research—the discussion delves into the intricate realm of Kansei engineering theory, accompanied by a thorough elucidation of the gray relational analysis (GRA) algorithmic framework, a crucial component in constructing a fuzzy logic model for product image modeling; (3) Kansei data collection—18 groups of perceptual words and six classic samples are selected, and the electric recliner chair samples are scored by the Kansei words; (4) Kansei data analysis—morphological analysis categorizes the electric recliner chair into four variables. followed by the ranking and key consideration areas of each area; (5) GRA fuzzy logic model verification—the GRA fuzzy logic model performs simple–complex (S-C) imagery output on 3D models of three modeling instances. By calculating the RMSE value of the seat image modeling design GRA fuzzy logic model, it is proven that the seat image modeling design GRA fuzzy logic model performs well in predicting S-C imagery. The subsequent experimental study results also show that the GRA fuzzy logic model consistently produces lower root mean square error (RMSE) values. These results indicate the efficacy of the GRA fuzzy logic approach in forecasting the visual representation of the electric recliner chair shape’s 3D model design. In summary, this research underscores the practical utility of the GRA model, harmoniously merged with perceptual engineering, in the realm of image recognition for product design. This synergy could fuel the extensive exploration of product design, examining perceptual engineering nuances in product modeling design. Full article
(This article belongs to the Special Issue Advances in Digital Technology Assisted Industrial Design)
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22 pages, 4153 KiB  
Article
A Novel Process Recommendation Method That Integrates Disjoint Paths and Sequential Patterns
by Danni Han, Chaoxue Wang, Genqing Bian, Bilin Shao and Tengteng Shi
Appl. Sci. 2023, 13(6), 3894; https://doi.org/10.3390/app13063894 - 19 Mar 2023
Cited by 1 | Viewed by 1469
Abstract
As the primary means of modern enterprise management, business process management (BPM) technology has become the mainstream development trend of modern enterprise management. The efficient and accurate establishment of business processes is essential for effective BPM. However, the traditional manual-based modeling approach is [...] Read more.
As the primary means of modern enterprise management, business process management (BPM) technology has become the mainstream development trend of modern enterprise management. The efficient and accurate establishment of business processes is essential for effective BPM. However, the traditional manual-based modeling approach is time-consuming and error-prone. To overcome this, process recommendation technology can improve the intelligence and efficiency of modeling to a certain extent. However, existing process modeling recommendation methods suffer from the problem of low accuracy and neglecting short-process models. Therefore, a novel process modeling recommendation method that integrates disjoint paths and sequential patterns was proposed. This method uses edge-disjoint paths for the first time to represent the behavioral semantics of processes, and an improved contiguous sequential pattern mining algorithm was proposed to mine the contiguous path sequential patterns (CPSPs) of edge-disjoint paths. In the process modeling recommendation stage, the k CPSPs with the highest matching degree with the current reference model process were calculated, and the last node in these CPSPs was used as the set of recommendation nodes. In cases with CPSPs with the same matching degree, the one with the higher value was recommended according to their corresponding lift, confidence, and support degrees. Through experimental evaluation and comparison, it was shown that the proposed method effectively improved the accuracy of the recommendation of both short-process and long-process models while ensuring effectiveness and time efficiency. Full article
(This article belongs to the Special Issue Advances in Digital Technology Assisted Industrial Design)
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13 pages, 2284 KiB  
Article
Evaluation and Optimization of In-Vehicle HUD Design by Applying an Entropy Weight-VIKOR Hybrid Method
by Xia Zhong, Yunuo Cheng, Jiahao Yang and Liwei Tian
Appl. Sci. 2023, 13(6), 3789; https://doi.org/10.3390/app13063789 - 16 Mar 2023
Cited by 4 | Viewed by 2035
Abstract
Background: The interface design of in-vehicle head-up display (HUD) is an enlarging research area with interface usability as its core; usability reflects all perspectives of human—machine interaction and thus the evaluation and optimization of usability have multiple objectives. The evaluation and optimization of [...] Read more.
Background: The interface design of in-vehicle head-up display (HUD) is an enlarging research area with interface usability as its core; usability reflects all perspectives of human—machine interaction and thus the evaluation and optimization of usability have multiple objectives. The evaluation and optimization of interface quality involved in usability are subjective and subconscious. Nevertheless, very little attention has been paid to these issues in optimizing usability across multiple objectives. Methods: In this paper, a hybrid scheme evaluation and optimization method based on entropy weight and VIKOR is proposed. First, according to the content of PSSUQ (Post Study System Usability Question), we have established a new usability evaluation system based on the characteristics of HUD. The entropy weight method was used to reduce the subjective factors of the decision-makers and to achieve the objective weight of each indicator. The VIKOR method was used for obtaining the order of alternate schemes and then the optimal interface design scheme was selected. Results: A case study was carried out to illustrate the applicability of the developed model in the usability evaluation of the HUD interface design. The results showed that scheme 1 was the optimized scheme, with minimal value of Si (0.141), Ri (0.119) and Qi (0.000) among the three schemes. When other decision-making methods were applied, the results showed that the optimized scheme was scheme 1, respectively, which verified the feasibility of the proposed method. The entropy—VIKOR model can be used to evaluate and optimize the HUD interface design effectively, which may serve as a reference for designers to achieve insights during the design process and scheme decision-making. Full article
(This article belongs to the Special Issue Advances in Digital Technology Assisted Industrial Design)
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Review

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33 pages, 1613 KiB  
Review
Big Data and AI-Driven Product Design: A Survey
by Huafeng Quan, Shaobo Li, Changchang Zeng, Hongjing Wei and Jianjun Hu
Appl. Sci. 2023, 13(16), 9433; https://doi.org/10.3390/app13169433 - 20 Aug 2023
Cited by 12 | Viewed by 10025
Abstract
As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data [...] Read more.
As living standards improve, modern products need to meet increasingly diversified and personalized user requirements. Traditional product design methods fall short due to their strong subjectivity, limited survey scope, lack of real-time data, and poor visual display. However, recent progress in big data and artificial intelligence (AI) are bringing a transformative big data and AI-driven product design methodology with a significant impact on many industries. Big data in the product lifecycle contains valuable information, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements, while product images contain shape, color, and texture information that can inspire designers to quickly generate initial design schemes or even new product images. This survey provides a comprehensive review of big data and AI-driven product design, focusing on how big data of various modalities can be processed, analyzed, and exploited to aid product design using AI algorithms. It identifies the limitations of traditional product design methods and shows how textual, image, audio, and video data in product design cycles can be utilized to achieve much more intelligent product design. We finally discuss the major deficiencies of existing data-driven product design studies and outline promising future research directions and opportunities, aiming to draw increasing attention to modern AI-driven product design. Full article
(This article belongs to the Special Issue Advances in Digital Technology Assisted Industrial Design)
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