Big Data Enabled Intelligent Immune System for Energy Efficient Manufacturing Management

Wang, S.; Liang, Yuchen; Li, W. D.

doi:10.1007/978-3-030-66849-5_3

Cited by 6 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, BDA, CPS and DT technologies have recently been applied to improve energy efficiency (electrical consumption in industrial manufacturing represents 42.6% of worldwide consumption 123 ). In that area, Wang et al 124 created a novel Intelligent Immune System based on CPS and BDA that can monitor, evaluate and optimize machining processes. Liang et al 125 used CPS, BDA and intelligent learning algorithms to develop an energy model capable of scheduling, monitoring, learning and rescheduling.…”

Section: Hardnessmentioning

confidence: 99%

Epoxy tooling: Technologies, developments, sustainability and future interest to industry 4.0

Díez-Barcenilla

Gómez-Alonso

Gondra

et al. 2021

Polymers and Polymer Composites

View full text Add to dashboard Cite

The technology of epoxy tooling, at present under continuous development, is used for the rapid manufacture of cost-effective tools for small batch production. It is a valid alternative with no need for expensive investment in metallic moulds for the development of new products. Current investigations are focused on improvements to the production system, improved tool performance, the cost reduction of moulds and tool manufacturing sustainability. In this paper, both the advantages and the disadvantages of epoxy tooling in injection moulding, wax injection, metal stamping and hot embossing are compared with conventional techniques. Following a brief introduction of rapid tooling technologies, the latest advances of epoxy tooling and their implementation in different manufacturing processes are all analysed. These developments refer to the production of new ad-hoc epoxy composites, increased productivity using conformal cooling channels, the reduction of the tooling manufacturing costs through waste reuse and the emerging industry 4.0 technologies for smart manufacturing and tooling. The main objective is to identify both the challenges facing epoxy tooling techniques and future research directions.

show abstract

Section: Hardnessmentioning

confidence: 99%

Epoxy tooling: Technologies, developments, sustainability and future interest to industry 4.0

Díez-Barcenilla

Gómez-Alonso

Gondra

et al. 2021

Polymers and Polymer Composites

View full text Add to dashboard Cite

show abstract

“…(3) Cloud layer: The cloud layer has databases to store reference signals for each machined components (called reference signals in the paper) and typical abnormal conditions for such components. The reference signals for machined components are built based on historical data [31], [32], [35]. The CNN is re-trained when new components are arranged and reference signals are received.…”

Section: System Framework and Workflowmentioning

confidence: 99%

“…Faulty conditions are obtained through machining life-cycles and accumulated historical data). Fault features are generated based on the reference signals according to pre-defined rules [32], [45], [46]. The CNN will be re-trained for update when there are new reference signals for new component machining.…”

Section: Fog Layermentioning

confidence: 99%

Fog computing and convolutional neural network enabled prognosis for machining process optimization

Liang

et al. 2019

Journal of Manufacturing Systems

View full text Add to dashboard Cite

“…China's manufacturing industry's industrial structure level, innovation capability, overall quality, and competitiveness are obviously behind the developed countries and are at the low end of the global value chain [7]. Service industry will be the great potential of future economic growth, and the trend of replacing traditional technology service mode with high-tech and practical public information knowledge as the main content of technical services is becoming more and more obvious [8]. With the rapid development of big data and manufacturing industry, knowledge services have gradually developed into multirole collaborative design activities.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence and Collaborative Knowledge for Manufacturing Design

Luo

Yu³

2021

Scientific Programming

View full text Add to dashboard Cite

With the rapid development of science and technology, digital technology has brought the world economy and management into a new stage. Collaborative design can realize product design process by cross-regional and cross-industry designers and share and exchange product information through network. With the rapid development of big data and artificial intelligence, knowledge services have gradually developed into multirole collaborative design activities based on artificial intelligence decision support. Traditional manufacturing industry has gradually transformed into modern manufacturing service industry after integrating information technology means such as Internet, communication, computer, and modern management methods. This article focuses on artificial intelligence decision support systems and the complex product manufacturing industry. We present a detailed analysis of how to integrate the knowledge generated by the product life cycle in the era of big data. We calculate the influence coefficient and sensitivity index of four different industries and propose a metadata architecture to improve the value of products as a whole. The findings of the research study imply that a knowledge-based collaborative platform should be designed by the enterprises and industries and a platform-based construction approach for economical, practical, and reliable production. We also present a detailed discussion about other factors such as the network cost of symmetric services, raw data and forecast data, and the number of nodes and the processing complexity.

show abstract

Big Data Enabled Intelligent Immune System for Energy Efficient Manufacturing Management

Cited by 6 publications

References 29 publications

Epoxy tooling: Technologies, developments, sustainability and future interest to industry 4.0

Epoxy tooling: Technologies, developments, sustainability and future interest to industry 4.0

Fog computing and convolutional neural network enabled prognosis for machining process optimization

Application of Artificial Intelligence and Collaborative Knowledge for Manufacturing Design

Contact Info

Product

Resources

About