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Sustainability
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Intellectual Manufacturing and Digital Decision
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Intellectual Manufacturing and Digital Decision

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Products and Services".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 2390

Special Issue Editors

Prof. Dr. Dejian Yu

E-Mail Website
Guest Editor
Business School, Nanjing Audit University, Nanjing 211815, China
Interests: digital decision
Prof. Dr. Shaohui Ma

E-Mail Website
Guest Editor
Business School, Nanjing Audit University, Nanjing 211815, China
Interests: business analysis and decision optimization; intellectual manufacturing

Special Issue Information

Dear Colleagues,

Intellectual manufacturing is considered the fourth revolution in manufacturing and a new paradigm for ICT and manufacturing technologies. It consists of a set of cutting-edge technologies that enable faster, more accurate, and more efficient digital decision-making by combining various ICT technologies with existing manufacturing technologies, playing a critical role in decreasing human intervention, improving production quality and sustainability, and reducing costs. Advanced technologies such as IoT, Industrial Internet, CPS, BDA, Cloud Manufacturing, Intellectual Sensors, Additive Manufacturing, CC, DM, AI, VR, AR, and MR are being applied in manufacturing sites, which illuminates advanced manufacturing and its importance. The achievement of Intellectual manufacturing goals depends on autonomous and analytics-based decision-making. This Special Issue aims to publish fundamental and applied research in the following broad areas to enhance Intellectual decision-making in manufacturing for sustainable production and consumption, not limited to the development, improvement, and management of materials, processes, and practices designed or adapted for use in digital manufacturing.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Predictive modeling and design;
  • Data-driven process monitoring, control, and optimization;
  • Physics-informed machine learning (Scientific machine learning/explicit AI);
  • Digital twins (process or product digital twins) and applications;
  • Sensor data fusion;
  • Novel sensing and monitoring systems;
  • Robot-assisted manufacturing processes and computational methods;
  • Industrial IoTs and applications;
  • Human–robot/machine interaction/collaboration;
  • AR/VR and other immersive techniques in advanced manufacturing;
  • Hybrid manufacturing (combination of additive and subtractive manufacturing);
  • Decision-making in Intellectual manufacturing environment.

We look forward to receiving your contributions.

Prof. Dr. Dejian Yu
Prof. Dr. Shaohui Ma
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. Sustainability 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

  • intellectual manufacturing
  • digital decision
  • digital twins
  • hybrid manufacturing

Published Papers (2 papers)

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Research

17 pages, 1219 KiB  
Article
Intelligent Manufacturing and Value Creation in Enterprises: Lessons from a Quasi-Natural Experiment in a Chinese Demonstration Project
by Zhao-Zhen Zhu, Yue Chen, Jiang Zhao and Zhu-Ying Yu
Sustainability 2023, 15(15), 11611; https://doi.org/10.3390/su151511611 - 27 Jul 2023
Cited by 1 | Viewed by 946
Abstract
With the rapid advancement of contemporary information technologies, intelligent manufacturing has emerged as a pivotal direction in the global technological transformation. To empirically examine the impact of intelligent manufacturing on enterprise value creation, this article conducts quasi-natural experiments using Chinese intelligent manufacturing demonstration [...] Read more.
With the rapid advancement of contemporary information technologies, intelligent manufacturing has emerged as a pivotal direction in the global technological transformation. To empirically examine the impact of intelligent manufacturing on enterprise value creation, this article conducts quasi-natural experiments using Chinese intelligent manufacturing demonstration projects as a sample. Specifically, it focuses on Chinese A-share-listed manufacturing enterprises in Shanghai and Shenzhen from 2011 to 2020. According to the report, the implementation of intelligent manufacturing has a positive influence on enterprise value production. This conclusion remains robust even after undergoing a rigorous testing procedure. Mechanism analysis further reveals that alleviating financial constraints and fostering technological innovation are the two primary avenues through which intelligent manufacturing enhances enterprise value creation. Moreover, the study indicates that regions with favorable business environments experience a more conspicuous boost in enterprise value generation due to intelligent manufacturing. Additionally, businesses in the growth stage are more significantly affected by this phenomenon. Overall, this research not only contributes to the existing body of knowledge on this subject but also offers empirical evidence to support businesses in their endeavors to enhance value creation. Full article
(This article belongs to the Special Issue Intellectual Manufacturing and Digital Decision)
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19 pages, 10736 KiB  
Article
Prediction of the Form of a Hardened Metal Workpiece during the Straightening Process
by Tadej Peršak, Jernej Hernavs, Tomaž Vuherer, Aleš Belšak and Simon Klančnik
Sustainability 2023, 15(8), 6408; https://doi.org/10.3390/su15086408 - 9 Apr 2023
Viewed by 1116
Abstract
In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure [...] Read more.
In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do. Full article
(This article belongs to the Special Issue Intellectual Manufacturing and Digital Decision)
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