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Application of Artificial Intelligence in Sustainable Manufacturing
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Application of Artificial Intelligence in Sustainable Manufacturing

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

Deadline for manuscript submissions: 30 April 2025 | Viewed by 14569

Special Issue Editors

Prof. Dr. Kumaran Kadirgama

E-Mail Website
Guest Editor
Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
Interests: heat transfer; thermal fluid
Special Issues, Collections and Topics in MDPI journals
Dr. Navid Aslfattahi

E-Mail Website
Guest Editor
Department of Fluid Mechanics and Thermodynamics, Faculty of Mechanical Engineering, Czech Technical University in Prague, Technická 4, 160 00 Prague, Czech Republic
Interests: nanofluids; MSBNFs; MXene; nanaocomposites; solar power systems; energy efficiency; advanced nanomaterials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The manufacturing sector is responsible for a significant part of the worldwide energy consumption. According to the International Energy Agency, energy demands keep rising. Yet, the latest report of Microsoft in association with PwC shows that artificial intelligence has an enormous potential to benefit environmental sustainability and pave the way to a more eco-friendly and energy-efficient manufacturing sector. Artificial intelligence can solve a number of issues that are critical for sustainable manufacturing. This includes excessive use of certain materials, redundant production of scrap waste, inefficient supply chain management, logistics and unequal distribution of energy resources. Most importantly, manufacturing entrepreneurs will not have to invest in numerous solutions, because AI alone can eradicate all of the aforementioned difficulties. AI is able to analyze specific data and accurately predict the expected output, thus eliminating exorbitant material use or waste. Additionally, AI algorithms may be set to make precise recommendations that will strike a balance in energy use. Lastly, artificial intelligence can be used to benefit the supply chain management and logistics with demand forecasting, improved communications, and real-time decision-making solutions. Artificial intelligence is an advanced technology that has the potential to fundamentally transform the manufacturing industry. Leveraging AI can create an efficient and transparent supply chain with significantly decreased operational friction. Moreover, AI is the backbone of efficient quality control systems that instantly identify even the slightest deviations and inform of possible failures in advance. By implementing AI-based technologies like digital twins or generative design, manufacturers are able to avoid expensive product prototyping and generate hundreds of feasible product design options. Most importantly, artificial intelligence will soon create unparalleled working opportunities within the “missing middle” and forge the path towards smart, efficient and sustainable manufacturing. This Special Issue is devoted to the development of advanced AI and data science solutions for a broad range of sustainable manufacturing, which requires simultaneous consideration of economic, environmental, and social aspects during the manufacturing process. Such efforts are importantly beneficial for the sake of deeper understanding of the optimal operational structure of manufacturing systems to achieve the goal of sustainable world. Our objective with this Special Issue is to collect papers that explore recent researches into the concepts, methods, tools, models and applications of artificial intelligence for sustainable manufacturing. Vast area of research topics are interest of this Special Issue including; life cycle engineering; cleaner production; green manufacturing; AI for sustainable solutions; sustainable manufacturing development; smart factories; high-efficiency manufacturing processes; industrial energy use and efficiency; renewable energy manufacturing; resources and energy management, and related topics.

Prof. Dr. Kumaran Kadirgama
Dr. Navid Aslfattahi
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

  • artificial intelligence
  • sustainable manufacturing
  • socio-ecological sustainability
  • green manufacturing
  • cleaner production
  • environmental sustainability
  • eco-friendly and energy-efficient manufacturing
  • energy resources
  • balance in energy consumption
  • renewable energy manufacturing

Published Papers (6 papers)

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Research

14 pages, 2380 KiB  
Article
Deep Reinforcement Learning-Based Scheduler on Parallel Dedicated Machine Scheduling Problem towards Minimizing Total Tardiness
by Donghun Lee, Hyeongwon Kang, Dongjin Lee, Jeonwoo Lee and Kwanho Kim
Sustainability 2023, 15(4), 2920; https://0-doi-org.brum.beds.ac.uk/10.3390/su15042920 - 06 Feb 2023
Cited by 1 | Viewed by 1814
Abstract
This study considers a parallel dedicated machine scheduling problem towards minimizing the total tardiness of allocated jobs on machines. In addition, this problem comes under the category of NP-hard. Unlike classical parallel machine scheduling, a job is processed by only one of the [...] Read more.
This study considers a parallel dedicated machine scheduling problem towards minimizing the total tardiness of allocated jobs on machines. In addition, this problem comes under the category of NP-hard. Unlike classical parallel machine scheduling, a job is processed by only one of the dedicated machines according to its job type defined in advance, and a machine is able to process at most one job at a time. To obtain a high-quality schedule in terms of total tardiness for the considered scheduling problem, we suggest a machine scheduler based on double deep Q-learning. In the training phase, the considered scheduling problem is redesigned to fit into the reinforcement learning framework and suggest the concepts of state, action, and reward to understand the occurrences of setup, tardiness, and the statuses of allocated job types. The proposed scheduler, repeatedly finds better Q-values towards minimizing tardiness of allocated jobs by updating the weights in a neural network. Then, the scheduling performances of the proposed scheduler are evaluated by comparing it with the conventional ones. The results show that the proposed scheduler outperforms the conventional ones. In particular, for two datasets presenting extra-large scheduling problems, our model performs better compared to existing genetic algorithm by 12.32% and 29.69%. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Manufacturing)
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14 pages, 4492 KiB  
Article
Application of YOLO and ResNet in Heat Staking Process Inspection
by Hail Jung and Jeongjin Rhee
Sustainability 2022, 14(23), 15892; https://0-doi-org.brum.beds.ac.uk/10.3390/su142315892 - 29 Nov 2022
Cited by 9 | Viewed by 1946
Abstract
In the automobile manufacturing industry, inspecting the quality of heat staking points in a door trim involves significant labor, leading to human errors and increased costs. Artificial intelligence has provided the industry some aid, and studies have explored using deep learning models for [...] Read more.
In the automobile manufacturing industry, inspecting the quality of heat staking points in a door trim involves significant labor, leading to human errors and increased costs. Artificial intelligence has provided the industry some aid, and studies have explored using deep learning models for object detection and image classification. However, their application to the heat staking process has been limited. This study applied an object detection algorithm, the You Only Look Once (YOLO) framework, and a classification algorithm, residual network (ResNet), to a real heat staking process image dataset. The study leverages the advantages of YOLO models and ResNet to increase the overall efficiency and accuracy of detecting heat staking points from door trim images and classify whether the detected heat staking points are defected or not. The proposed model achieved high accuracy in both object detection (mAP of 95.1%) and classification (F1-score of 98%). These results show that the developed deep learning models can be applied to the real-time inspection of the heat staking process. The models can increase productivity and quality while decreasing human labor cost, ultimately improving a firm’s competitiveness. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Manufacturing)
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19 pages, 969 KiB  
Article
Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers
by Henry Ekwaro-Osire, Dennis Bode, Klaus-Dieter Thoben and Jan-Hendrik Ohlendorf
Sustainability 2022, 14(23), 15618; https://0-doi-org.brum.beds.ac.uk/10.3390/su142315618 - 24 Nov 2022
Cited by 2 | Viewed by 1461
Abstract
Machine learning (ML) can be a valuable tool for discovering opportunities to save energy and resources in manufacturing systems. However, the hype around ML in the context of Industry 4.0 in the past few years has led to blind usage of the approach, [...] Read more.
Machine learning (ML) can be a valuable tool for discovering opportunities to save energy and resources in manufacturing systems. However, the hype around ML in the context of Industry 4.0 in the past few years has led to blind usage of the approach, occasionally resulting in usage when another analysis approach would be better suited. The research presented here uses a novel matrix approach to address this lack of differentiation of when to best use ML for improving energy and resource efficiency in manufacturing, by systematically identifying situations in which ML is well suited. Seventeen generic levers for improving manufacturing energy and resource efficiency are defined. Next, a generic list of six manufacturing data scenarios for when ML is a good method of choice for analysis is created. This results in a comprehensive matrix in which each lever is evaluated along each ML scenario and given a point, providing a quantitative ML suitability score for each lever. The evaluation is conducted by drawing on past studies demonstrating whether ML is appropriate. Specifically, operation parameter and input material optimization, as well as intelligent maintenance, are the levers that score the highest and are thus identified to be most suitable for machine learning. The majority of the remaining levers is deemed to have low suitability for machine learning. This simple yet informative matrix can be used as a guideline in data-driven manufacturing energy and resource efficiency projects to provide an appraisal on the applicability of ML as the initial analysis tool of choice. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Manufacturing)
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20 pages, 8522 KiB  
Article
Operation-Driven Power Analysis of Discrete Process in a Cyber-Physical System Based on a Modularized Factory
by Jumyung Um, Taebyeong Park, Hae-Won Cho and Seung-Jun Shin
Sustainability 2022, 14(7), 3816; https://0-doi-org.brum.beds.ac.uk/10.3390/su14073816 - 24 Mar 2022
Cited by 1 | Viewed by 1444
Abstract
As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In previous studies, a machine, conducting a single continuous operation, has been mainly observed for power estimation. However, a [...] Read more.
As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In previous studies, a machine, conducting a single continuous operation, has been mainly observed for power estimation. However, a modularized production line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of such production lines, it is important to interpret and distinguish mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline from data collection from different sources to pre-processing, data conversion, synchronization, and deep learning classification to estimate the total power use of the future process plan is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building power estimation models without manual data pre-processing. The proposed system is applied to a modular factory connected with machine controllers using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline with the result of the power profile synchronized with the robot program. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Manufacturing)
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22 pages, 8404 KiB  
Article
Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing
by Sobhan Sheykhivand, Tohid Yousefi Rezaii, Saeed Meshgini, Somaye Makoui and Ali Farzamnia
Sustainability 2022, 14(5), 2941; https://0-doi-org.brum.beds.ac.uk/10.3390/su14052941 - 03 Mar 2022
Cited by 22 | Viewed by 3274
Abstract
In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for [...] Read more.
In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study presents an automated system for a two-stage classification of driver fatigue, using a combination of compressed sensing (CS) theory and deep neural networks (DNNs), that is based on EEG signals. First, CS theory is used to compress the recorded EEG data in order to reduce the computational load. Then, the compressed EEG data is fed into the proposed deep convolutional neural network for automatic feature extraction/selection and classification purposes. The proposed network architecture includes seven convolutional layers together with three long short-term memory (LSTM) layers. For compression rates of 40, 50, 60, 70, 80, and 90, the simulation results for a single-channel recording show accuracies of 95, 94.8, 94.6, 94.4, 94.4, and 92%, respectively. Furthermore, by comparing the results to previous methods, the accuracy of the proposed method for the two-stage classification of driver fatigue has been improved and can be used to effectively detect driver fatigue. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Manufacturing)
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19 pages, 3063 KiB  
Article
Sustainable Circular Micro Index for Evaluating Virtual Substitution Using Machine Learning with the Path Planning Problem as a Case Study
by Javier Maldonado-Romo and Mario Aldape-Pérez
Sustainability 2021, 13(23), 13436; https://0-doi-org.brum.beds.ac.uk/10.3390/su132313436 - 04 Dec 2021
Cited by 4 | Viewed by 2255
Abstract
Due to the problems resulting from the COVID-19 pandemic, for example, semiconductor supply shortages impacting the technology industry, micro-, small-, and medium-sized enterprises have been affected because the profitability of their business models depends on market stability. Therefore, it is essential to propose [...] Read more.
Due to the problems resulting from the COVID-19 pandemic, for example, semiconductor supply shortages impacting the technology industry, micro-, small-, and medium-sized enterprises have been affected because the profitability of their business models depends on market stability. Therefore, it is essential to propose alternatives to mitigate the various consequences, such as the high costs. One attractive alternative is to replace the physical elements using resource-limited devices powered by machine learning. Novel features can improve the embedded devices’ (such as old smartphones) ability to perceive an environment and be incorporated in a circular model. However, it is essential to measure the impact of substituting the physical elements employing an approach of a sustainable circular economy. For this reason, this paper proposes a sustainable circular index to measure the impact of the substitution of a physical element by virtualization. The index is composed of five dimensions: economic, social, environmental, circular, and performance. In order to describe this index, a case study was employed to measure the path-planning generator for micro aerial vehicles developed using virtual simulation using machine-learning methods. The proposed index allows considering virtualization to extend the life cycle of devices with limited resources based on suggested criteria. Thus, a smartphone and the Jetson nano board were analyzed as replacements of specialized sensors in controlled environments. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Sustainable Manufacturing)
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