Education Sustainability for Intelligent Manufacturing in the Context of the New Generation of Artificial Intelligence

Abstract

With the continuous breakthrough and innovation of artificial intelligence technology, the demand for diversified and multi-level compound intelligent manufacturing talents keeps growing. However, the current pace of intelligent manufacturing talent education in colleges and universities is still difficult to keep up with the advances in science and technology in the context of the new generation of artificial intelligence. This work conducted visual research of the literature on artificial intelligence in the field of manufacturing. All the literature was retrieved from the Web of Science Core Collection and divided into three periods (1979–1994, 1995–2007 and 2008–2021) according to the fluctuation of literature volume. Bibliometric and content analysis of the related literature during these periods were conducted to track the hotspots and trend of artificial intelligence in the field of manufacturing. The results showed that the internet of things, deep learning, cyber physical systems and smart manufacturing have been the new research hotspots. Finally, a series of suggestions were given for the sustainable education of intelligent manufacturing talents in the context of the new generation of artificial intelligence. This work may provide references for the construction of sustainable education systems for intelligent manufacturing talents in the context of the new generation of artificial intelligence.

1. Introduction

Intelligent manufacturing (IM) is a human-machine integrated intelligent system composed of intelligent machines and expert intelligence which can carry out intelligent activities in the manufacturing process, such as analysis, reasoning, judgment, conception and decision-making [1,2,3]. New technologies such as cloud computing, big data analytics, virtual/augmented reality, Internet of Things (IoT), mobile devices and robots continue to emerge in the context of the new generation of artificial intelligence (AI 2.0) [4,5,6,7,8]. These technologies greatly promote further changes of IM technology and make modern industrial manufacturing automation flexible, intelligent and highly integrated. Therefore, IM technology has attracted worldwide attention from academia and industry. Governments around the world have issued a series of policies on the development of intelligent manufacturing technology. In 2011, the Advanced Manufacturing Partnership (AMP) was proposed by the United States aiming to connect industry, universities and research institutes with the government to jointly invest in advanced technologies and create high-quality products [9,10]. Germany launched the “Industry 4.0” plan in 2013, which sought a highly flexible production mode [11]. In 2015, The Chinese government implemented the “Made in China 2025” plan, which emphasized that the deep integration of information technology and manufacturing technology is the commanding point of the new round of industrial competition [12,13]. In the same year, the 17 objectives were adopted at the United Nations Sustainable Development Summit. The Sustainable Development Goals (SDGs) aim to address the social, economic and environmental dimensions of development. The ninth goal is industrial innovation, which aims to promote sustainable industries and drive innovation [14,15,16]. All the policies mention the importance of IM talent education, and colleges and universities are the main sources of talent to transfer to enterprises. However, the talent architecture and attribute requirements of IM systems in colleges and universities have not been clearly revealed due to the continuous change of AI 2.0. Obviously, this will lead to the talent education of IM in colleges and universities being divorced from the actual needs of social enterprises, which is detrimental to the sustainable education of IM talents.

Education for sustainable development (ESD) refers to an education that emphasizes the pursuit of harmonious balance among society, economy and environment [17,18,19]. This concept was proposed by the United Nations Educational, Scientific, and Cultural Organization (UNESCO). One of the main ideas is to make education always maintain the vigor and vitality of sustainable development and cultivate talents with sustainable development abilities [20,21]. Many efforts have been made in education sustainability [22,23]. A. Bieler et al. presented a content analysis for the strategic plans of Canadian higher education institutions (HEIs) and found three characteristic types of response for education sustainability [24]. K. Mintz et al. used a mixed-methods design method to integrate sustainability into the curriculum and improve students’ knowledge, skills and motivation [25]. J. J. Salovaara et al. explored educational programs and the representation of theory-based key competencies for sustainability through a qualitative content study of master’s programs [26]. Obviously, the core competencies of sustainable education are different for different fields and different levels of education. As new and innovative technologies continue to emerge, the development of artificial intelligence has entered a new stage, referred to as AI 2.0 [27,28,29,30]. Nowadays, the main difficulty of sustainable education for IM talents in the context of AI 2.0 is to keep pace with the times of the education mechanism and cultivate talents’ abilities of continuous innovation.

The present work aims to identify the hotspots and research trends in AI. The purpose of the paper is to provide an informative overview of the past and present of research on AI and prospective future research directions. The main research questions to be addressed are:

• ✧ What were the research hotspots and trends related to artificial intelligence before and during AI 2.0?
• ✧ What fields in the context of AI 2.0 should the sustainable development education of intelligent manufacturing talents focus on in the future?

In this work, the AI literature retrieved from the Web of Science Core Collection (WoSCC) was used to study the progress of AI in the field of manufacturing. The literature from 1979 to 2021 was divided into three periods according to the corresponding the periods of rising AI literature volume. The work used the bibliometric method to analyze the annual distribution of publications, countries, institutes, authors, journals and categories of this literature. The publication trend from 1979 to 2021 was tracked to indicate the heat volatility of AI. Key topics and highly cited papers were analyzed to show the main concern of researchers and influences of specific topics. Furthermore, high frequency terms and clusters were found and cluster analysis was conducted to demonstrate the theme turnover in the three different periods. Finally, suggestions for the cultivation of AI were given according to the analysis results.

2. Data and Method

The AI literature retrieved from the WoSCC were used to conduct the research on the progress of AI in the field of manufacturing. The data for the study were retrieved in November 2021 from the WoSCC. In order to find literature suitable for the topic, the data retrieval strategies were set as:

Topic = “artificial intelligence” and “manufacturing”. It means that these words located in title, abstracts or keywords will be collected.

Timespan = 1979–2021. All data about AI in the WoSCC from 1979 to 2021 were collected.

A total of 10,154 results were retrieved from the database. The rise of published literature year by year means the rise of research heat. When the volume of literature reaches a peak, it means that the current technology has reached a bottleneck and it also means that a stage has ended. The retrieved results were classified as three periods according to the annual peak volume of literature, which are 1979–1994 (n = 424), 1995–2007 (n = 2102) and 2008–2021 (n = 7628). Bibliometric methods were used to analyze the geographic distribution, topic terms, highly cited articles and sources and categories of AI research. Topic terms were utilized to conduct keyword cluster analysis, which can indicate the hotspots of different periods. The highly cited articles were also classified as hot topics. By this means, the hotspots of three periods were revealed and the changes of different hotspots in these periods also give a reference for the final decision. VOSviewer was utilized as a tool for bibliometric analysis and visualization of the results. The advantage of VOSviewer is the ability to display graphics for analyzed literature. In this way, we can obtain the research topics and hotspots in a certain field. The overall methodology is shown in Figure 1.

3. Results and Discussion

3.1. Time Distribution

The publications trend of annual papers in AI research from 1979 to 2021 (the time of data retrieval) is shown in Figure 2. We chose the WoSCC as the source of data for the research. We used the topic search terms “artificial intelligence” and “manufacturing” to search the literature, and the time span of the literature was selected from 1979 to 2021. A line chart of ten countries with the most publications about AI is shown in Figure 3 from 1979 to 1993. The annual number of publications is lower than 100. The number of publications exceeded 100 for the first time in 1994. From 1995 to 2007, the annual publication volume of literature fluctuated slightly, all of which were less than 300. From 2014, the annual publication volume of literature increased steadily from 232 in 2014 to 472 in 2017. After 2018, the annual publication volume of literature increased significantly. It grew 75.14 percent in 2019. In 2021, it reached 1831.

3.2. Geographic Distribution

Publication performances of all the retrieved papers are from 122 countries/territories, and the top ten countries with the most publications are shown in Figure 3. China has contributed with 3174 (38.75%) papers on AI, which makes China top ranking country in this list, followed by the USA (1509, 18.42%), the UK (830, 10.13%), Germany (563, 6.87%), India (431, 5.26%), Singapore (361, 4.41%), France (355, 4.34%), Australia (338, 4.13%), Spain (321, 3.92%) and South Korea (308, 3.76%).

The countries with most of the contributions are China, the USA and the UK, whereas Huazhong University of Science and Technology, Chinese Academy of Science and Nanyang Technological University were the three institutions with the most publications in AI research. Huazhong University of Science and Technology ranked first with 322 papers, followed by Chinese Academy of Sciences with 246 papers. Other leading institutions in the area are Nanyang Technological University (221), University of Chinese Academy of Sciences (126), Massachusetts Institute of Technology (111), Zhejiang University (110), Xi’an Jiaotong University (109), Tsinghua University (105), National University of Singapore (97) and Shanghai Jiaotong University (96).

Publication trends and cumulative numbers of publications for the main countries are shown in Figure 4. The number of publications in the USA is larger than that in China and the UK in the period from 1979 to 1994. The records of the USA have grown rapidly recently and rank in the first place now. China also performed well in 1995. The amount of published literature once surpassed the USA and the UK. After 2015, the number of publications from China grew rapidly, far surpassing those in the USA and UK. Until 2021, the number of publications in China was far ahead in the world. The cumulative numbers of publications in all three countries have shown a trend of rapid growth since 2015. The cumulative number of publications in the three countries and the world shows the same trend.

The cooperation network of authors in AI with ≥70 papers are shown in Figure 5. The size of labels and nodes show the number of papers that the authors published, and the link means a cooperation between two authors. The major groups are marked and show that organizations from China, the USA and the UK play an important role in the international cooperation in AI research. On the other side, the organizations from China collaborate actively with other countries in the network.

3.3. Sources and Categories Distribution

Web of Science supplies the category of each journal or paper. We used the “Analyze” function from the results page to get the categories that AI typically belongs to. There are plenty of categories of publications in Web of Science. We selected the top 10 categories shown in Figure 6a. The AI research was mainly from Computer Science Artificial intelligence (4797), followed by Engineering Manufacturing (2835), Engineering Electrical Electronic (2478), Automation Control Systems (2132), Computer Science Interdisciplinary Applications (1970), Engineering Multidisciplinary (1745), Computer Science Information Systems (1184), Telecommunications (5429), Computer Science Software Engineering (3596), Computer Science Theory Methods (1077), Robotics (1072) and Engineering Industrial (893). The top 10 of journals with the most publications are shown in Figure 6b. AI Edam Artificial Intelligence for Engineering Design Analysis and Manufacturing has published 946 papers in AI, ranking the first among the journals, followed by Lecture Notes in Artificial Intelligence with 904 papers.

3.4. High Frequency Topic Terms

According to literature on AI published in Web of Science from 1979 to 2021, VOSviewer software was used to perform cluster visualization analysis of terms based on keywords in a co-occurrence relation. The network visualization of topic terms with a frequency ≥ 100 are shown in Figure 7. The figure shows multiple circles of different sizes and colors. The size of the font and circles reflects the frequency (weight) of each term, and the color of the circles represents the cluster of the topic terms. The distance between two circles indicates the relatedness between two circles. A short distance means a strong connection, while a long distance means a weak connection.

The results of keyword analysis in the period 1979–2021 are shown in

Table 1. Results of keyword analysis in the period 1979–2021.

Cluster 1	Occurrences	TLS	Cluster 2	Occurrences	TLS
algorithm	233	401	architecture	109	175
artificial neural network	120	177	artificial intelligence	1247	1687
artificial neural networks	106	151	artificial- intelligence	155	318
classification	205	308	big data	166	445
fuzzy logic	106	167	deep learning	233	404
genetic algorithm	280	409	digital twin	103	268
genetic algorithms	135	225	framework	191	495
model	407	730	industry 4.0	248	483
neural network	123	146	internet	137	330
neural networks	187	279	internet of things	110	251
neural-network	130	264	machine learning	435	838
neural-networks	152	335	management	175	349
optimization	585	1030	manufacturing	166	283
particle swarm optimization	100	152	networks	112	204
performance	204	362	smart manufacturing	122	270
prediction	265	567	system	335	513
selection	117	275
system	387	647
Cluster 3	Occurrences	TLS
design	711	1050
knowledge	106	150
manufacturing system	132	233
models	109	161
scheduling	127	169
simulation	272	431

. We set the threshold of frequency as 100. The keywords with a frequency ≥ 100 are listed in the table below. The total link strength (TLS) is also shown in the table. It indicates total co-occurrence times of keywords and other keywords The keywords with high occurrence in the database are classified by VOSviewer in three cluster. The table below shows the occurrences and TLS of keywords in each cluster.

The topic of publications and keywords with occurrences and total link strength (TLS) in three periods are shown in

Table 2. Results of keyword analysis in the period 1979–1994.

Cluster 1	Occurrences	TLS	Cluster 2	Occurrences	TLS
artificial intelligence	73	93	knowledge	6	13
diagnosis	6	9	knowledge-based systems	9	22
expert systems	33	52	learning	6	13
flexible manufacturing	6	10	manufacturing	5	12
machine learning	8	15	manufacturing systems	8	19
models	6	9	scheduling	5	14
neural networks	8	13	system	5	1
process control	5	5
Cluster 3	Occurrences	TLS	Cluster 4	Occurrences	TLS
artificial intelligence, application and expert systems	9	8	artificial-intelligence systems	8	19
computer-aided engineering	7	6	flexible manufacturing	6	16
distributed artificial intelligence	8	9	object-oriented programming	8	18
knowledge representation formalisms and methods	6	6	simulation	15	38
process planning	5	6	systems	6	6
simulation and modeling, applications	5	4
Cluster 5	Occurrences	TLS
CAD	8	11
design	15	23
expert system	20	34
knowledge base	5	10

,

Table 3. Results of keyword analysis in the period 1995–2007.

Cluster 1	Occurrences	TLS	Cluster 2	Occurrences	TLS
algorithm	24	48	artificial intelligence	199	194
case-based reasoning	31	34	artificial neural networks	25	28
classification	22	35	expert system	29	21
conceptual	22	11	fault diagnosis	22	14
design	119	154	fuzzy logic	37	65
knowledge	36	49	genetic algorithm	48	52
manufacturing	41	55	genetic algorithms	57	81
model	31	42	neural network	39	35
neural networks	84	105	optimization	63	83
representation	21	23
system	56	69
Cluster 3	Occurrences	TLS	Cluster 4	Occurrences	TLS
architecture	22	20	expert systems	46	72
concurrent engineering	28	14	flexible manufacturing	26	44
distributed artificial intelligence	27	24	fms	20	41
manufacturing	26	24	machine learning	28	37
multi-agent systems	20	7	scheduling	40	60
process planning	24	26	simulation	68	89
systems	71	72

and

Table 4. Results of keyword analysis in the period 2008–2021.

Cluster 1	Occurrences	TLS	Cluster 2	Occurrences	TLS
algorithm	207	297	artificial-intelligence	129	250
artificial neural network	107	142	big data	166	419
classification	182	233	digital twin	103	246
design	577	780	framework	174	428
genetic algorithm	232	275	industry 4.0	248	455
model	373	598	internet	129	294
neural-network	118	215	Internet of things	110	235
neural-networks	145	278	management	159	296
optimization	520	810	manufacturing	135	221
performance	194	313	smart manufacturing	121	259
prediction	255	486	systems	258	393
selection	102	214
simulation	189	283
system	326	519
Cluster 3	Occurrences	TLS
artificial intelligence	975	1314
deep learning	233	363
machine learning	399	730

. We conducted a cluster analysis of keywords from 1979 to 1994. The threshold number of minimum keyword occurrence is set as 5, and 30 words meet the threshold of the 618 words. The results of keyword analysis are shown in Table 2. The keywords occurring the most in the articles from 1979 to 1994 are ‘artificial intelligence’, ‘expert systems’, ‘simulation’, ‘expert system’ and ‘design’.

For the articles in the 1995–2007 period, the threshold number of minimum keyword occurrences is set as 20, and 33 words meet the threshold of the 4614 words. The results of keywords analysis are shown in Table 3. The keywords occurring the most in the articles from 1994 to 2007 are ‘artificial intelligence’, ‘design’, ‘neural networks’, ‘simulation’ and ‘optimization’.

In the last period, the threshold number of minimum keyword occurrences is set as 100, and 29 articles meet the threshold of the 23,515 words. The results of keyword analysis from 2008 to 2021 are shown in Table 4. The keywords occurring the most in the articles from 2008 to 2021 are ‘artificial intelligence’, ‘optimization’, ‘design’, ‘machine learning’ and ‘model’.

The number of topic terms increased over the years. There are 618 keywords in the articles published in the 1979–1995 period, and there are 4614 keywords in the 1995–2007 period. After that, the number of keywords increases rapidly. In the 2008–2021 period, the number of keywords was about four times that of the previous period. The increase of keywords indicates the focus on AI and manufacturing research greatly increased with time. After that, we extracted and classified the topic of keywords to clarify the development trend of AI.

The keywords of the literature from 1979 to 1994 were classified via VOSviewer. We set the threshold of keyword occurrences as 5, and the 30 words that meet the threshold were classified via VOSviewer in five clusters. Each cluster includes the similar keywords, and their occurrences and TLS are also listed in Table 2.

The keywords with high frequency in the literature from 1995 to 2007 are displayed in the above table. We set the threshold of keyword occurrences as 20, and 33 words meet the threshold and were classified via VOSviewer in five clusters. Each cluster includes the similar keywords, and their occurrences and TLS are also listed in Table 3.

Table 4 indicates the occurrences and TLS of the keywords with high frequency. We set the occurrences threshold of the keywords as 100, and 29 keywords meet the threshold and were classified via the VOSviewer.

The analysis consisted of the clustering of keywords with occurrences in

Table 5. Topics extracted in the period 1979–1994.

Topic	Keywords	Occurrences
Machine learning	artificial intelligence; machine learning; learning; distributed artificial intelligence.	100
Data system	artificial intelligence; applications and expert systems; knowledge-based systems; expert system.	59
Programming design	scheduling; CAD; design; object-oriented programming; simulation.	41
Flexible manufacturing	flexible manufacturing; manufacturing systems.	11
Neural networks	neural networks.	8

,

Table 6. Topics extracted in the period 1995–2007.

Topic	Keywords	Occurrences
Machine learning	distributed artificial intelligence; machine learning; artificial intelligence.	254
Genetic algorithms	case-based reasoning; fuzzy logic; genetic algorithm; scheduling; genetic algorithms.	191
Expert systems	expert system; expert systems; design; optimization; simulation.	176
Neural networks	neural network; neural networks.	110
Concurrent engineering	concurrent engineering; manufacturing.	54

and

Table 7. Topics extracted in the period 2008–2021.

Topic	Keywords	Occurrences
Internet of things	internet of things; artificial intelligence.	1040
Smart manufacturing	industry 4.0; additive manufacturing; smart manufacturing; manufacturing.	631
Deep learning	deep learning; machine learning.	602
Digital twin	digital twin; simulation; genetic algorithm; optimization.	476
Neural networks	neural network; neural networks; artificial neural network.	266

. The corresponding topic maps are shown in Figure 8. The results of the extracted topics showed that the research in the first period (1979–1994) mainly focused on ‘machine learning’, ‘data system’, ‘programming design’, ‘flexible manufacturing’ and ‘neural networks’. The topics extracted are shown in Table 5. In Table 6, the keywords in the publications mostly focused on ‘machine learning’, ‘genetic algorithms’, ‘expert systems’, ‘neural networks’ and ‘concurrent engineering’ in the second period. Between 2009 and 2021, the keywords in the publications mostly focused on the topics ‘internet of things’, ‘smart manufacturing’, ‘deep learning’, ‘digital twin’ and ‘neural networks’. The topics extracted are shown in Table 7. Each topic corresponds to multiple keywords. The sums of their occurrences are listed in the adjacent column.

We grouped the similar keywords into the same topic in the period 1979–1994. These keywords with high frequency were divided into five topics as shown in Table 5. We summed the occurrences of the keywords in the same topic and listed them in the above table.

As shown in Table 6, the similar keywords are divided into the same topic from 1995 to 2007. We summed the occurrences of the keywords in the same topic and listed them in the above table. ‘Machine learning’ is the research hotspot in the period of 1995–2007.

As shown in Table 7, the similar keywords are divided into the same topic in the period of 2008–2021. We summed the occurrences of the keywords in the same topic and listed them in the Table 7. The topic with the highest occurrence is ‘internet of things’. It indicates the trend of artificial intelligence in recent years.

3.5. Highly Cited Articles

We used VOSviewer software to conduct bibliographic coupling analysis. The documents were selected as the unit of analysis. The counting method is full counting. We chose the minimum number of citations of a document and set the threshold as 300, and 28 articles meet the threshold. The overlap visualization is shown in Figure 9. The retrieved articles cited more than 600 times and the corresponding total citations (TC) are listed in

Table 8. Total citation of highly cited articles.

Articles	Topic	TC
Jordan et al. (2015) [31]	Deep learning	2189
Jia et al. (2016) [32]	Deep learning	987
Jovicich et al. (2006) [33]	Deep learning; Big data	891
Liu et al. (2018) [34]	Big data	766
Lei et al. (2016) [35]	Big data	648

. The most cited paper is “Machine learning: Trends, perspectives, and prospects” written by M. I. Jordan et al. in 2015, and it has been cited 2189 times since it was published in Science [31]. On the other hand, “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data” written by F. Jia et al. in 2017 ranks the second, which has been cited 987 times [32]. The aforementioned articles are all in the field of deep learning. The next three highly cited papers mainly deal with big data processing techniques [33,34,35]. It represents an interest shift in AI from simple logic operations to data mining and analysis, reasoning, judgment, conception and decision-making driven by big data.

We selected the first five articles of the highly cited articles in Figure 9. We extracted the topics of the highly cited articles. The topics and TC of these articles are listed in Table 8. The topics of these articles are focus on ‘Deep learning’ and ‘Big data’. This is in line with the trend of AI we discussed in the previous sections.

3.6. Suggestions for IM Sustainable Education

Colleges and universities are the main bases for cultivating IM talents to society and enterprises. In order to cultivate students who are competitive in society and enterprises, colleges and universities should keep up with the latest developments and reform the training strategy. According to the analysis results, it is a shortcut to understand the future trends of IM by paying attention to the policy orientation of China, the United States, the United Kingdom, and Germany. The talents training systems of some organizations with advanced AI technology, such as Huazhong University of Science and Technology, Chinese Academy of Science and Nanyang Technological University, can also be used for references. IoT, deep learning, cyber physical system and smart manufacturing have become new themes for IM [36,37,38]. IM represents an indepth integration of the new generation AI technology and advanced manufacturing technology [39,40]. Colleges and universities should fully investigate the attributes of talent demand in these fields. The reform of courses and the establishment of a practice platform should meet the requirements of students to contact, understand and master the knowledge of related fields.

4. Conclusions

It is one of the main principles of sustainable education to keep up with the latest trends and continuously reform the education strategy of the cultivation of intelligent manufacturing talents, especially in the context of the new generation of AI. The sustainable development of industry promotes the reform and innovation of higher education. The talents cultivated by education are also sent to promote the further development of industry. It will guide the direction of development for decades. This work has utilized a bibliometric method to investigate the emerging fields and track the growing trends. The analysis looks at research trends, countries and institutes, authors, sources of publication, categories of publications, terms and citations and maps this knowledge in terms of main topics (clusters of papers looking at same or similar issues). According to the time distribution of the research literature, the development trend of AI has shown a rapid growth trend in the past two decades. The results show that the countries which contribute most are China, the USA and UK. Meanwhile, the Chinese Academy of Sciences, City University of Hong Kong and Nanyang Technological University are the three organizations with most co-authors in AI research.

IoT, deep learning, smart manufacturing and digital twin have gradually become the new development themes of IM in the new era. IM represents an indepth integration of the new generation AI technology and advanced manufacturing technology. It not only promotes industrial innovation but also accelerates the transformation and innovation of education. Colleges and universities should follow the frontier trends, investigate the actual needs of related fields, adjust the curriculum system and establish experimental platforms, so as to achieve the sustainable development education of talents in the field of IM. This work may provide references for sustainable development education to cultivate talents in the field of IM in the context of the new generation of AI.