An Agent-Based Industrial Cyber-Physical System Deployed in an Automobile Multi-stage Production System

Abstract

Industrial Cyber-Physical Systems (CPS) are promoting the development of smart machines and products, leading to the next generation of intelligent production systems. In this context, Artificial Intelligence (AI) is posed as a key enabler for the realization of CPS requirements, supporting the data analysis and the system dynamic adaptation. However, the centralized Cloud-based AI approaches are not suitable to handle many industrial scenarios, constrained by responsiveness and data sensitive. Edge Computing can address the new challenges, enabling the decentralization of data analysis along the cyber-physical components. In this context, distributed AI approaches, such those based on Multi-agent Systems (MAS), are essential to handle the distribution and interaction of the components. Based on that, this work uses a MAS approach to design cyber-physical agents that can embed different data analysis capabilities, supporting the decentralization of intelligence. These concepts were applied to an industrial automobile multi-stage production system, where different kinds of data analysis were performed in autonomous and cooperative agents disposed along Edge, Fog and Cloud computing layers.Industrial Cyber-Physical Systems (CPS) are enabling the next generation of intelligent production systems, mainly based on the concepts of smart machines and products. Driven by the needs to attend the ever-changing market trends, such digital transformation is mainly based on the use of Internet of Things (IoT), Cloud Computing and Artificial Intelligence (AI) technologies [12]. While the first enables the interconnection of equipment and consequently the digitization of the industrial environment [22], the second provides on demand high processing and storage resources [15]. On the other hand, AI provides advanced data analysis algorithms, such those based on Machine-Learning (ML), that can take advantage of the huge amounts of IoT data and the power of Cloud Computing, in order to provide actionable information and support data-driven decision-making [20,8].Although Cloud manufacturing [15] has been seen as a new paradigm in the realization of the 4th industrial revolution (4IR) [12], the traditional Cloud-based approaches, where IoT devices send all the data to be processed by centralized applications, present some drawbacks. Indeed, besides information security and privacy concerns [21], this approach is not suitable for many real-time, data-sensitive and constrained network applications [2]. In this context, Fog Computing emerged to cover the Cloud limitations, promoting the deployment of data processing capabilities closer to the data sources [4]. It defines an intermediate computing layer between Cloud applications and IoT devices that besides providing a more direct, reliable, secure and fast link between them, also promotes the decentralization of data analysis, decision-making and control, increasing local components autonomy.Besides Fog, which considers equipment at the local network, CPS also considers processing ca...

Collective Intelligence in Self-Organized Industrial Cyber-Physical Systems

“…For this purpose, according to the bibliographic search performed in the Scopus repository and considering the use of collective intelligence approaches to achieve the desired levels of autonomy and dynamic adaptation of industrial CPS, the main research works that directly propose collective-intelligence-based industrial CPS approaches were identified and analyzed, as summarized in Table 2. Framework that enables one to test the self-coordination of a swarm of controllers, aiming to solve the distributed optimal power flow algorithm in a DC network Queiroz et al, 2020 [26] Manufacturing system (tested in a real case study-automotive assembly line)…”

“…Multi-agent systems-agents to manage the manufacturing line and report problems related to unexpected events In this analysis, most of the works use MAS-based approaches to provide the required collective intelligence in industrial CPS, particularly using distributed interaction strategies (e.g., negotiation mechanisms) to achieve collective intelligence, as presented in [23,24,30,32,33]. Complementary emergent digital technologies were also identified (e.g., IoT, edge, fog and cloud computing, and AI [26,28,31,34]), usually embedded in MAS solutions to empower data analysis and intelligence capabilities. It is interesting to note that these distributed intelligence capabilities are usually balanced among the edge/fog/cloud computational layers, exploring the benefits they provide to address the particularities of the different data analysis demands.…”

“…It is also noticed that the most common application domain is manufacturing (e.g., [23,24,26,27,30,33]), where collective intelligence solutions are adopted to deal with the current trends for highly customized, flexible, and reconfigurable processes to meet fast-changing market conditions. However, some works propose solutions for the energy domain (e.g., see [25,32,36]) and IoT-based smart applications (e.g., see [31,34]).…”

Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges

“…Cooperation among multiple agents is necessary to dynamically construct collaborative groups for the completion of customized production tasks [118]. As discussed in Section IV, multiple agents with edge computing provide a better option than a single device to build a collaborative operation to realize CM [78], [119]. Therefore, by combining the edge computingassisted intelligent agents and different AI algorithms, a novel cooperative operation can be constructed as shown in Fig.…”

Intelligent Products through SOHOMA Prism

“…Some industries are willing to use these techniques and some SOHOMA articles describe applications of PDS via some case studies in the manufacturing industry [25], [26].…”