Exploring the current applications and potential of extended reality for
environmental sustainability in manufacturing
- URL: http://arxiv.org/abs/2312.17595v1
- Date: Fri, 29 Dec 2023 13:18:01 GMT
- Title: Exploring the current applications and potential of extended reality for
environmental sustainability in manufacturing
- Authors: Huizhong Cao, Henrik S\"oderlund, M\'elanie Derspeisse and Bj\"orn
Johansson
- Abstract summary: Extended Reality (XR) is one of the technologies identified as an enabler for Industry 5.0.
XR could potentially be a driver for more sustainable manufacturing: however, its potential environmental benefits have received limited attention.
This paper aims to explore the current manufacturing applications and research within the field of XR technology connected to the environmental sustainability principle.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In response to the transformation towards Industry 5.0, there is a growing
call for manufacturing systems that prioritize environmental sustainability,
alongside the emerging application of digital tools. Extended Reality (XR) -
including Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) -
is one of the technologies identified as an enabler for Industry 5.0. XR could
potentially also be a driver for more sustainable manufacturing: however, its
potential environmental benefits have received limited attention. This paper
aims to explore the current manufacturing applications and research within the
field of XR technology connected to the environmental sustainability principle.
The objectives of this paper are two-fold: (1) Identify the currently explored
use cases of XR technology in literature and research, addressing environmental
sustainability in manufacturing; (2) Provide guidance and references for
industry and companies to use cases, toolboxes, methodologies, and workflows
for implementing XR in environmental sustainable manufacturing practices. Based
on the categorization of sustainability indicators, developed by the National
Institute of Standards and Technology (NIST), the authors analyzed and mapped
the current literature, with criteria of pragmatic XR use cases for
manufacturing. The exploration resulted in a mapping of the current
applications and use cases of XR technology within manufacturing that has the
potential to drive environmental sustainability. The results are presented as
stated use-cases with reference to the literature, contributing as guidance and
inspiration for future researchers or implementations in industry, using XR as
a driver for environmental sustainability. Furthermore, the authors open up the
discussion for future work and research to increase the attention of XR as a
driver for environmental sustainability.
Related papers
- Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning [53.3760591018817]
We propose a new benchmarking environment for aquatic navigation using recent advances in the integration between game engines and Deep Reinforcement Learning.
Specifically, we focus on PPO, one of the most widely accepted algorithms, and we propose advanced training techniques.
Our empirical evaluation shows that a well-designed combination of these ingredients can achieve promising results.
arXiv Detail & Related papers (2024-05-30T23:20:23Z) - Potentials of Green Coding -- Findings and Recommendations for Industry,
Education and Science -- Extended Paper [0.0]
We conduct an analysis to gather and present existing literature on three research questions relating to the production of ecologically sustainable software.
We compile the approaches to Green Coding and Green Software Engineering that have been published since 2010.
We consider ways to integrate the findings into existing industrial processes and higher education curricula to influence future development in an environmentally friendly way.
arXiv Detail & Related papers (2024-02-28T10:48:56Z) - On the Opportunities of Green Computing: A Survey [80.21955522431168]
Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades.
The needs for high computing power brings higher carbon emission and undermines research fairness.
To tackle the challenges of computing resources and environmental impact of AI, Green Computing has become a hot research topic.
arXiv Detail & Related papers (2023-11-01T11:16:41Z) - A Comparative Study of Machine Learning Algorithms for Anomaly Detection
in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
This study seeks to address the demands of high-performance machine learning models with environmental sustainability.
Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance.
However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
arXiv Detail & Related papers (2023-07-01T15:18:00Z) - The Technological Emergence of AutoML: A Survey of Performant Software
and Applications in the Context of Industry [72.10607978091492]
Automated/Autonomous Machine Learning (AutoML/AutonoML) is a relatively young field.
This review makes two primary contributions to knowledge around this topic.
It provides the most up-to-date and comprehensive survey of existing AutoML tools, both open-source and commercial.
arXiv Detail & Related papers (2022-11-08T10:42:08Z) - GreenDB -- A Dataset and Benchmark for Extraction of Sustainability
Information of Consumer Goods [58.31888171187044]
We present GreenDB, a database that collects products from European online shops on a weekly basis.
As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts.
We present initial results demonstrating that ML models trained with our data can reliably predict the sustainability label of products.
arXiv Detail & Related papers (2022-07-21T19:59:42Z) - Machine Learning and Artificial Intelligence in Circular Economy: A
Bibliometric Analysis and Systematic Literature Review [0.0]
Circular economy (CE) aims to complete the product life cycle loop by bringing out the highest values from raw materials in the design phase and later on by reusing, recycling, and remanufacturing.
This study explores the adoption and integration of applied AI techniques in CE.
arXiv Detail & Related papers (2022-04-01T07:05:13Z) - Augmented reality applications in manufacturing and its future scope in
Industry 4.0 [0.0]
Augmented reality technology is one of the leading technologies in the context of Industry 4.0.
This research demonstrates the influence of augmented reality in Industry 4.0 while critically reviewing the industrial augmented reality history.
The article investigates various areas of application for this technology and its impact on improving production conditions.
arXiv Detail & Related papers (2021-12-03T20:46:50Z) - A survey on applications of augmented, mixed and virtual reality for
nature and environment [114.4879749449579]
Augmented reality (AR), virtual reality (VR) and mixed reality (MR) are technologies of great potential due to the engaging and enriching experiences they are capable of providing.
However, the possibilities that AR, VR and MR offer in the area of environmental applications are not yet widely explored.
We present the outcome of a survey meant to discover and classify existing AR/VR/MR applications that can benefit the environment or increase awareness on environmental issues.
arXiv Detail & Related papers (2020-08-27T09:59:27Z) - A Survey Study to Understand Industry Vision for Virtual and Augmented
Reality Applications in Design and Construction [1.7499351967216341]
The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps between AEC research and industry practices.
The findings show a significant increase in AR/VR utilization in the AEC industry over the past year from 2017 to 2018.
The industry experts also anticipate strong growth in the use of AR/VR technologies over the next 5 to 10 years.
arXiv Detail & Related papers (2020-05-06T13:16:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.