Human-Centered Intelligent Manufacturing: Overview and Perspectives

Baicun Wang , Yuan Xue , JianlinYang Xiaoying Yan , Yuan Zhou

Strategic Study of CAE ›› 2020, Vol. 22 ›› Issue (4) : 139 -146.

PDF (645KB)
Strategic Study of CAE ›› 2020, Vol. 22 ›› Issue (4) :139 -146. DOI: 10.15302/J-SSCAE-2020.04.020
Engineering Management
Orginal Article
Human-Centered Intelligent Manufacturing: Overview and Perspectives
Author information +
History +
PDF (645KB)

Abstract

Human is the most dynamic factor in a manufacturing system; no matter how advanced intelligent manufacturing would be, it should meet humans’ needs and serve for a better life. Based on the theory of human–cyber–physical systems (HCPS) in the context of intelligent manufacturing, the concept of human-centered intelligent manufacturing (HCIM) is firstly proposed in this work. HCIM is discussed from the aspects of background, connotation, human factors, technical system, and practical applications. It clarifies that HCIM not only reflects an important perspective, but also represents one of the significant research directions of intelligent manufacturing. On this basis, several suggestions are recommended from policy decision-making, enterprise development, and scientific research levels, including linking HCIM with relevant national strategies, regarding HCIM as a key concept for enterprises’development, and enhancing research on human factors/ergonomics in intelligent manufacturing systems. It’s expected that this work can provide a reference to promote HCIM development and applications in China.

Keywords

以人为本 / 新一代智能制造 / 人– 信息– 物理系统 / 人本智造 / human-centered / new-generation intelligent/smart manufacturing / human–cyber–physical systems (HCPS) / humancentered intelligent manufacturing (HCIM)

Cite this article

Download citation ▾
Baicun Wang, Yuan Xue, JianlinYang Xiaoying Yan, Yuan Zhou. Human-Centered Intelligent Manufacturing: Overview and Perspectives. Strategic Study of CAE, 2020, 22(4): 139-146 DOI:10.15302/J-SSCAE-2020.04.020

登录浏览全文

4963

注册一个新账户 忘记密码

1 Introduction

The world experiences changes unseen over the past century. In particular, the deep integration of the new generation of information and manufacturing technologies profoundly reshapes the development pattern of the global manufacturing industry. In the face of the new round of scientific and technological revolution and industrial revolution with intelligent manufacturing technologies at the core, countries and regions worldwide employ active actions [1,2] to promote the transformation and upgrading of the manufacturing industry, to secure a favorable position for their manufacturing industry in the industrial development in the future (Table 1). In this process, intelligent manufacturing has become a major option for them to build competitive edges in the manufacturing industry. The academic and industrial communities in various countries have also carried out relevant studies to provide a theoretical basis to promote strategic plans related to intelligent manufacturing [1–5].

Table 1. Industry/manufacturing strategic plans issued by countries/regions.

In recent years, China has accelerated the pace of intelligent manufacturing development. China Manufacturing 2025 has proposed a clear goal of completing the historical transition of China from a large to a strong manufacturing industry, by accelerating the deep integration of new-generation information technologies and manufacturing industry as the main theme and advancing intelligent manufacturing as the main direction, in accordance with the principles of “innovation-driven, quality first, green development, optimized structure, and talent-based development” [1]. In 2017, the State Council issued Development Plan for the New Generation of Artificial Intelligence, detailing the new features of artificial intelligence (AI) and defining intelligent manufacturing as an important direction for the applications of the new-generation AI. Simultaneously, the China’s academic community have proposed the human–cyber–physical system (HCPS)-based theory on intelligent manufacturing development, analyzed the paradigm evolution of intelligent manufacturing, and proposed the development strategy and technology roadmap of intelligent manufacturing in China for the coming 20 years [1–3].

Based on the HCPS theory in the context of intelligent manufacturing, human-centered intelligent manufacturing (HCIM) attracts increasing attention among the academic and industrial communities. It is expected to become a key direction in the intelligent manufacturing development. On this basis, in this study, the development background of HCIM was analyzed, its contents are detailed, and its technical system with examples of practical applications is described. Finally, several recommendations for the future HCIM development are proposed.

2 Development background of HCIM

Manufacturing is a process in which humans use tools to transform raw materials into products and services that can meet people’s needs in production and life. Intelligent manufacturing is a means to improve the efficiency and quality of such transformation. In manufacturing and production activities, humans are always the most dynamic and energetic factor.

(1) Humans are the ultimate target of intelligent manufacturing services. With the help of new production technologies and changes in production methods, intelligent manufacturing can provide consumers with various types of quality products and services with higher speed, flexibility, and efficiency. The rapid development of the new generation of information technologies, particularly mobile Internet, sensors, big data, supercomputing, industrial Internet, Internet of Things, AI, machine learning, collaborative robots, virtual reality/augmented reality (VR/AR), and other digital, networked, and intelligent technologies, provides an important technological support for the development of HCIM. As consumer demands become increasingly personalized, enterprises must adopt a usercentric approach to gain a larger market share and improve the market competitiveness and continue to meet personalized consumer demands through the application of advanced technologies and changes in the model of organizational management. Therefore, to meet diversified market demands, considering the economic viability of technologies, employment, and other factors, the “human-centered” concept must be a guiding principle in promoting intelligent manufacturing.

(2) Humans have a critical role in the implementation of intelligent manufacturing. Industrial robots are one of the important components of intelligent manufacturing, while traditional industrial robots, owing to certain weaknesses, are yet to fully meet the new demands in the market. For example, the deployment of traditional robots is very expensive, and individual robots, which still require the support of numerous peripheral devices, cannot be used directly in the production line of a factory. Although robots are highly flexible, the flexibility of the entire production line is generally low. In addition, small and medium enterprises (SMEs), owing to financial constraints, are incapable of transforming substantially to their production lines. They are also more sensitive to the return on investment of their products. The robots are required to have a low overall cost, rapid deployment, and convenient operation. However, traditional robots are not yet able to provide a satisfactory solution at a manageable cost. If humans are responsible for the work processes that require flexibility, tactility, and agility, while robots use their advantage of speed and accuracy for repetitive and standardized work processes, such type of human–machine collaboration would provide a fairly good solution for SMEs. In addition, if robotic technologies are used to strengthen the workforce, which would in turn lead to reduced costs and better competitiveness, it can also create more jobs for our society.

(3) Humans will have a more important role in the future development of intelligent manufacturing. The actual demands for intelligent manufacturing vary between industries and enterprises. Notably, not all industries and factories need to be completely automated or completely unmanned. Therefore, the technical and economic viability should also be considered in promoting intelligent manufacturing. For example, unlike the automotive industry, the aviation, aerospace, shipping, and construction industries, owing to the complexity of their tasks and processes, have not yet realized full automation and unmanned production. They are still mostly dependent on human–machine cooperation, accumulation of human knowledge and experience, as well as self-motivation of humans. Therefore, the future of manufacturing is not to build purely unmanned factories, but to allow people to engage in more valuable and enjoyable work with the support of advanced technologies and simultaneously generate higher economic returns for the enterprises.

3 Contents and technology system of HCIM

HCIM applies the human-centered principle in the whole lifecycle of an intelligent manufacturing system (including design, manufacturing, management, sales, and services), fully considers the various factors (physiological, cognitive, organizational, cultural, social, etc.) concerning humans (designers, producers, managers, users, etc.), and uses advanced digital, networked, and intelligent technologies to promote the human–machine collaboration in a manner that leverages their comparative advantages in completing various tasks, thus achieving the best results in improving the production efficiency and quality, ensuring the physical and mental health of the involved personnel, satisfying user demands, and promoting a sustainable social development.

HCIM represents an important development approach and key direction for intelligent manufacturing in the future. HCIM is not limited to any single manufacturing model or paradigm. In the process of its development, numerous models and forms, such as shared manufacturing, social manufacturing, and sustainable manufacturing, have emerged or will emerge. The research on HCIM is still at its infant phase, but it can be expected that its definition, contents, and characteristics will continue to evolve and expand.

3.1 Human factors in intelligent manufacturing

From the perspective of the full life cycle of intelligent manufacturing [6], human factors in intelligent manufacturing include human roles, human–machine relationships, physical ergonomics, cognitive ergonomics, and organizational ergonomics (Fig. 1), which are described below.

Fig. 1. Intelligent manufacturing system and human factors.

3.1.1 Role of humans

The role of humans is mainly reflected in their different roles, functions, and types of work in intelligent manufacturing systems. From an intelligent perspective, the role of humans is centered on the creation of knowledge and processes, through the continuous accumulation and practice of human experience, talent, and knowledge so that the intelligence of manufacturing can be constantly optimized and improved.

Scholars have analyzed the critical status and defining role of humans and importance of human factors in intelligent manufacturing and concluded that, by integrating and synergizing advanced technologies, humans and organizations can truly implement their role and generate benefits [7,8]. Zhou et al. [1,2] proposed the concept of HCPS and underscored the dominant role of humans in HCPS: both physical and cyber systems are designed and created by humans. The models, methods, and guidelines for analysis, calculation, and control are determined and embedded into the cyber system by research and development (R&D) personnel. The purpose of the whole system is to serve humans. Humans are both designers, operators, supervisors, and beneficiaries of the services provided by the intelligent manufacturing system [6]. In its report on the Industrial Internet, General Electric defines humans as a key factor in the Industrial Internet [4]. Nunes et al. [9] argued that the role of humans in the cyber–physical system (CPS) includes data acquisition, state inference, drive, control, and monitoring. Madni et al. [10,11] reported that the role of humans in HCPS includes monitoring of nonhuman-in-the-loop, monitoring guidance of nonhuman-inthe-loop, and control of human-in-the-loop. Jin et al. [1].

4.2 Human-centered intelligent production

The development of digitized, networked, and intelligent manufacturing industries is an enabler for both product and technology innovation. It turns the manufacturing industry into an intelligent integrated manufacturing system. In this process, it is necessary to follow a human-centered approach, comprehensively improve the design, manufacturing, and management of products, and promote the building of intelligent enterprises. The application of human-centered intelligent production includes human–machine design cooperation, human–machine collaborative assembly, and human-centered production management. Intelligent optimized design, intelligent collaborative design, and “crowd design” based on group intelligence are important elements of the humancentered intelligent design. The development of an intelligent design system based on HCPS is also an important element in the development of HCIM.

Case 2 : Human-machine design cooperation based on deep learning. Raina et al. [3].

5 Reflection and suggestions

5.1 Policy level

In recent years, the European Union, the United States, Japan, and other countries and regions have attributed a high importance to the research on HCIM. For example, the United States have launched a forward-looking program in this field referred to as Future of Work at the Human–Technology Frontier (FW-HTF), which brings both challenges and inspiration for HCIM development in China. We hereby suggest incorporating HCIM into relevant national strategies in a timely manner and strengthening the top-level design. In the pilot demonstration, application promotion, publicity and implementation, education, and training of intelligent manufacturing, the human factors should be fully considered and the human-centered concept should be integrated into the standard setting and maturity evaluation of intelligent manufacturing systems. In the meantime, more attention should be devoted to the standardization of human–machine collaboration, human–machine task division, and maturity evaluation of intelligent manufacturing personnel. This will help concepts such as HCPS and human factor engineering take root in the practice of intelligent manufacturing and promote HCIM’s development in China.

5.2 Enterprise level

From a human-centered perspective, it is imperative for intelligent manufacturing companies to consider and address the following two issues: (1) how to use advanced and appropriate technologies to prolong the career of employees, so that those employees whose physical strength is declining while still at the peak of their intelligence and experience can continue to create values for the company with the support of technology and (2) how to use technology to create an environment where the younger generation is willing to work in the manufacturing industry and feel the joy of working and creating value through intelligent manufacturing. We suggest that manufacturing companies employ the “human-centered” approach as an important principle in the development of intelligent manufacturing, devote more attention to employee training, education, and management, and regard it as a strategic investment for the company. Simultaneously, companies can better use collaborative robots to meet their demands, rather than replacing all humans with traditional robots. Through repeated trials and adjustments, they will be able to find the most appropriate manner of human–machine collaboration for their companies to increase the productivity and profits.

5.3 Research level

At the research level, more research efforts are needed on HCPS, human-centered intelligent manufacturing, human factor engineering for intelligent manufacturing, collaborative robotics, and other subjects. It is important to devote more attention to the construction and improvement in the science and technology system of HCPS and promote the application of HCPS in the field of intelligent manufacturing, i.e., human-centered intelligent manufacturing. The research on HCIM theories and applications should include human-centered design, products, automation, AI, production, factories, and services. Simultaneously, more emphasis should be placed on the studies on human ergonomics, cognitive ergonomics, organizational ergonomics, and other subjects of human factor engineering to achieve a positive interaction between natural and social sciences. In addition, collaborative robots and tri-co robots (i.e., coexisting, cooperative, and cognitive robots) are important directions for future research. The interactions within the HCPS, human digital twin, and human-in-the-loop are research topics that need to be strengthened.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]

Zhou J. Intelligent manufacturing―Main direction of “Made in China 2025” [J]. China Mechanical Engineering, 2015, 26(17): 2273-2284. Chinese.
[2]

Zhou J, Li P G, Zhou Y H, et al. Toward new-generation intelligent manufacturing [J]. Engineering, 2018, 4(1): 11–20.
[3]

Zhou J, Zhou Y H, Wang B C, et al. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing [J]. Engineering, 2019, 5(4): 624–636.
[4]

Wang B C, Hu S J, Sun L, et al. Intelligent welding system technologies: State-of-the-art review and perspectives [J]. Journal of Manufacturing Systems, 2020, 56: 373–391.
[5]

Wang B C, Zang J Y, Qu X M, et al. Research on new-generation intelligent manufacturing based on human–cyber–physical systems [J]. Strategic Study of CAE, 2018, 20(4): 29–34. Chinese.
[6]

Li Q, Tang Q L, Chen Y T, et al. Smart manufacturing standardization: Reference model and standards framework [J]. Computer Integrated Manufacturing Systems, 2018, 24(3): 539–549. Chinese.
[7]

Zhang B P, Wang J S. Knowledge information and human function in manufacturing systems [J]. Journal of Mechanical Engineering, 1994, 30(5): 61–65. Chinese.
[8]

Chen G Q. Human factors—The key to the research, development and application of advanced manufacturing technology system [J]. China Mechanical Engineering, 1996, 7(1): 12–14. Chinese.
[9]

Nunes D, Sá Silva J, Boavida F. A practical introduction to human in-the-loop cyber–physical systems [M]. Hoboken: John Wiley & Sons Ltd., 2018.
[10]

Madni A M, Sievers M, Madni C C. Adaptive cyber–physical– human systems: Exploiting cognitive modeling and machine learning in the control loop [J]. Insight, 2018, 21(3): 87–93.
[11]

Madni A M. Exploiting augmented intelligence in systems engineering and engineered systems [J]. Insight, 2020, 23(1): 31– 36.
[12]

Jin M. Data-efficient analytics for optimal human–cyber–physical systems [D]. Berkeley: University of California, Berkeley(Doctoral dissertation), 2017.
[13]

Romero D, Bernus P, Noran O, et al. The operator 4.0: Human– cyber–physical systems & adaptive automation towards human-automation symbiosis work systems [C]. Iguassu Falls: International Conference on Advances in Production Management Systems, 2016.
[14]

Ruppert T, Jaskó S, Holczinger T, et al. Enabling technologies for operator 4.0: A survey [J]. Applied Sciences, 2018, 8(9): 1–19.
[15]

Sun L Y. Human factors engineering [M]. Beijing: China Science Publishing & Media Ltd., 2011. Chinese.
[16]

Dannapfel M, Burggräf P, Bertram S, et al. Systematic planning approach for heavy-duty human–robot cooperation in automotive flow assembly [J]. International Journal of Electrical and Electronic Engineering and Telecommunications, 2018, 7: 51–57.
[17]

Ma M, Lin W, Pan D, et al. Data and decision intelligence for human-in-the-loop cyber–physical systems: Reference model, recent progresses and challenges [J]. Journal of Signal Processing Systems, 2017, 90(8): 1167–1178.
[18]

Fantini P, Pinzone M, Taisch M. Placing the operator at the centre of Industry 4.0 design: Modelling and assessing human activities within cyber–physical systems [J]. Computers & Industrial Engineering, 2018, 139: 1–11.
[19]

Pacaux-Lemoine M P, Trentesaux D, Zambrano Rey G, et al. Designing intelligent manufacturing systems through human– machine cooperation principles: A human-centered approach [J]. Computers & Industrial Engineering, 2017, 111: 581–595.
[20]

Raina A, McComb C, Cagan J. Learning to design from humans: Imitating human designers through deep learning [J]. Journal of Mechanical Design, 2019, 141(11): 111102.
[21]

Raina A, Cagan J, McComb C. Transferring design strategies from human to computer and across design problems [J]. Journal of Mechanical Design, 2019, 141(11): 114501.
[22]

Wang L, Gao R, Váncza J, et al. Symbiotic human–robot collaborative assembly [J]. CIRP Annals Manufacturing Technology, 2019, 68(2): 701–726.

Funding

()

PDF (645KB)

15751

Accesses

0

Citation

Detail
Sections
Recommended

/

〈 〉