Knowledge-driven process industry smart manufacturing

Gui, Weihua; Zeng, Zhiwei; Chen, Xiaofang; Xie, Yongfang; Sun, Yubo

doi:10.1360/ssi-2020-0211

Cited by 20 publications

(5 citation statements)

References 35 publications

(35 reference statements)

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“…Knowledge is the core element of intelligent manufacturing in process industry(Gui Weihua, Zeng Zhaohui, Chen Xiaofang, Xie Yongfang & Sun Yubo, 2020) . Although we have regular mining method, statistical learning method, deep learning method, evolutionary algorithm, example-based reasoning method, symbolic intelligence or computational intelligence based acquisition method (Gui et al, 2020) etc already in the early stage, industry process involves various mechanism changes as well as association associated representation, dynamic uncertainty cause-effect graph, fuzzy awareness graph representation, Petri net representation, semantic Different knowledge representation methods such as network representation. Therefore, we can build a knowledge-driven intelligent manufacturing system for the process industry to automatically complete processing knowledge (Gui et al, 2020) , achieve seamless information exchange at all levels in each enterprise, and finally meet the requirement of efficient and green intelligent manufacturing in the process industry.…”

Section: The Intelligent Manufacturing Technology and Application Of ...mentioning

confidence: 99%

From Zero to Infinity: Research on the Development Process of Intelligent Manufacturing

Chen¹

2023

CIS

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Intelligent manufacturing is flourishing in the 21st century. This paper takes the brief history of exploring the development of intelligent manufacturing as a staring point, explains the current intelligent and informative technologies and achievements of intelligent manufacturing by describing the impact of intelligent manufacturing on traditional manufacturing, proposes reflections on the shortcomings and limitations of intelligent manufacturing in China. And our country advocates human-centered intelligent manufacturing, continuously improves the system and innovates technology, while reasonably plans the three directions. Finally, we extend to digital twin technology and explore it in the intelligent manufacturing to promote the further upgrade. Besides, the study shows that the country will continue to use intelligent manufacturing as a traction and the industrial internet as a means to seize new opportunities and create a new ecology of production.

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Section: The Intelligent Manufacturing Technology and Application Of ...mentioning

confidence: 99%

From Zero to Infinity: Research on the Development Process of Intelligent Manufacturing

Chen¹

2023

CIS

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“…Intelligent manufacturing has become a core technology to enhance the competitiveness of manufacturing enterprises. Digital transformation and intelligent upgrading are essential to achieve higher levels of safety, sustainability, and profitability [2]. The technologies of human-cyber-physical system (HCPS) and autonomous intelligence are critical enablers of this evolution, becoming an important trend [3].…”

Section: Introductionmentioning

confidence: 99%

Industrial‐generative pre‐trained transformer for intelligent manufacturing systems

Han

Liu

Shen

2023

IET Collab Intel Manufact

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Manufacturing enterprises are facing how to utilise industrial knowledge and continuously accumulating massive unlabelled data to achieve human-cyber-physical collaborative and autonomous intelligence. Recently, artificial intelligence-generative content has achieved great performance in several domains and scenarios. A new concept of industrial generative pre-trained Transformer (Industrial-GPT) for intelligent manufacturing systems is introduced to solve various scenario tasks. It refers to pre-training with industrial datasets, fine-tuning with industrial scenarios, and reinforcement learning with domain knowledge. To enable Industrial-GPT to better empower the manufacturing industry, Model as a Service is introduced to cloud computing as a new service mode, which provides a more efficient and flexible service approach by directly invoking the general model of the upper layer and customising it for specific businesses. Then, the operation mechanism of the Industrial-GPT driven intelligent manufacturing system is described. Finally, the challenges and prospects of applying the Industrial-GPT in the manufacturing industry are discussed.

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“…Artificial general intelligence [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] (AGI) for language and vision includes the wellknown Chat GPT and ViT, respectively. Both Chat GPT and ViT are empowered by Transformers.…”

Section: Introductionmentioning

confidence: 99%

Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence

Xu,

Wang,

Guo

et al. 2023

Mathematics

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As a new abstract computational model in evolutionary transfer optimization (ETO), single-objective to multi-objective optimization (SMO) is conducted at the macroscopic level rather than the intermediate level for specific algorithms or the microscopic level for specific operators; this method aims to develop systems with a profound grasp of evolutionary dynamic and learning mechanism similar to human intelligence via a “decomposition” style (in the abstract of the well-known “Transformer” article “Attention is All You Need”, they use “attention” instead). To the best of our knowledge, it is the first work of SMO for discrete cases because we extend our conference paper and inherit its originality status. In this paper, by implementing the abstract SMO in specialized memetic algorithms, key knowledge from single-objective problems/tasks to the multi-objective core problem/task can be transferred or “gathered” for permutation flow shop scheduling problems, which will reduce the notorious complexity in combinatorial spaces for multi-objective settings in a straight method; this is because single-objective tasks are easier to complete than their multi-objective versions. Extensive experimental studies and theoretical results on benchmarks (1) emphasize our decomposition root in mathematical programming, such as Lagrangian relaxation and column generation; (2) provide two “where to go” strategies for both SMO and ETO; and (3) contribute to the mission of building safe and beneficial artificial general intelligence for manufacturing via evolutionary computation.

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Knowledge-driven process industry smart manufacturing

Abstract: Functional knowledge integration of the design process SCIENCE CHINA Technological Sciences 60, 209 (2017); Energy consumption diagnosis in the iron and steel industry via the Kalman filtering algorithm with a data-driven model SCIENCE CHINA Information Sciences 61, 110204 (2018);

Cited by 20 publications

References 35 publications

From Zero to Infinity: Research on the Development Process of Intelligent Manufacturing

From Zero to Infinity: Research on the Development Process of Intelligent Manufacturing

Industrial‐generative pre‐trained transformer for intelligent manufacturing systems

Decomposition Is All You Need: Single-Objective to Multi-Objective Optimization towards Artificial General Intelligence

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