scholarly journals Cognitive capabilities for the CAAI in cyber-physical production systems

2021 ◽  
Author(s):  
Jan Strohschein ◽  
Andreas Fischbach ◽  
Andreas Bunte ◽  
Heide Faeskorn-Woyke ◽  
Natalia Moriz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Production Systems ◽  
Cognitive Architecture ◽  
Use Case ◽  
Test Functions ◽  
Model Quality ◽  
Implementation Effort ◽  
Data Platform ◽  
Cyber Physical Production Systems ◽  
The Individual

AbstractThis paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case.

scholarly journals CAAI—a cognitive architecture to introduce artificial intelligence in cyber-physical production systems

2020 ◽  
Vol 111 (1-2) ◽  
pp. 609-626 ◽  
Author(s):  
Andreas Fischbach ◽  
Jan Strohschein ◽  
Andreas Bunte ◽  
Jörg Stork ◽  
Heide Faeskorn-Woyke ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Modular Design ◽  
Production Systems ◽  
Cognitive Architecture ◽  
Performance Criteria ◽  
Data Platform ◽  
Constant Observation ◽  
Cyber Physical Production Systems ◽  
The Individual

Abstract This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes the user’s declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration.

Integration of Cutting-Edge Interoperability Approaches in Cyber-Physical Production Systems and Industry 4.0

2021 ◽  
pp. 144-172
Author(s):  
Luis Alberto Estrada-Jimenez ◽  
Terrin Pulikottil ◽  
Nguyen Ngoc Hien ◽  
Agajan Torayev ◽  
Hamood Ur Rehman ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Production Systems ◽  
Heterogeneous Systems ◽  
Smart Manufacturing ◽  
Use Case ◽  
Integrative Framework ◽  
Data Formats ◽  
Exchange Information ◽  
Cyber Physical Production Systems ◽  
Physical Components

Interoperability in smart manufacturing refers to how interconnected cyber-physical components exchange information and interact. This is still an exploratory topic, and despite the increasing number of applications, many challenges remain open. This chapter presents an integrative framework to understand common practices, concepts, and technologies used in trending research to achieve interoperability in production systems. The chapter starts with the question of what interoperability is and provides an alternative answer based on influential works in the field, followed by the presentation of important reference models and their relation to smart manufacturing. It continues by discussing different types of interoperability, data formats, and common ontologies necessary for the integration of heterogeneous systems and the contribution of emerging technologies in achieving interoperability. This chapter ends with a discussion of a recent use case and final remarks.

scholarly journals Towards trustworthy Cyber-physical Production Systems: A dynamic agent accountability approach

2021 ◽  
Vol 13 (2) ◽  
pp. 157-180
Author(s):  
Richárd Beregi ◽  
Gianfranco Pedone ◽  
Davy Preuveneers
Keyword(s):  
Production Systems ◽  
Smart Manufacturing ◽  
Use Case ◽  
Multi Agent Systems ◽  
Hand Tools ◽  
Agent Based ◽  
Agent Systems ◽  
Multi Agent ◽  
Cyber Physical Production Systems ◽  
Dynamic Authorization

Smart manufacturing is a challenging trend being fostered by the Industry 4.0 paradigm. In this scenario Multi-Agent Systems (MAS) are particularly elected for modeling such types of intelligent, decentralised processes, thanks to their autonomy in pursuing collective and cooperative goals. From a human perspective, however, increasing the confidence in trustworthiness of MAS based Cyber-physical Production Systems (CPPS) remains a significant challenge. Manufacturing services must comply with strong requirements in terms of reliability, robustness and latency, and solution providers are expected to ensure that agents will operate within certain boundaries of the production, and mitigate unattended behaviours during the execution of manufacturing activities. To address this concern, a Manufacturing Agent Accountability Framework is proposed, a dynamic authorization framework that defines and enforces boundaries in which agents are freely permitted to exploit their intelligence to reach individual and collective objectives. The expected behaviour of agents is to adhere to CPPS workflows which implicitly define acceptable regions of behaviours and production feasibility. Core contributions of the proposed framework are: a manufacturing accountability model, the representation of the Leaf Diagrams for the governance of agent behavioural autonomy, and an ontology of declarative policies for the identification and avoidance of ill-intentioned behaviours in the execution of CPPS services. We outline the application of this enhanced trustworthiness framework to an agent-based manufacturing use-case for the production of a variety of hand tools.

scholarly journals Artificial Intelligence-Based Decision-Making Algorithms, Internet of Things Sensing Networks, and Deep Learning-Assisted Smart Process Management in Cyber-Physical Production Systems

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2497
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Mariana Iatagan ◽  
Cristian Uță ◽  
Roxana Ștefănescu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Internet Of Things ◽  
Real Time ◽  
Manufacturing Systems ◽  
Process Management ◽  
Production Systems ◽  
Big Data Analytics ◽  
Eligibility Criteria ◽  
Cyber Physical Production Systems

With growing evidence of deep learning-assisted smart process planning, there is an essential demand for comprehending whether cyber-physical production systems (CPPSs) are adequate in managing complexity and flexibility, configuring the smart factory. In this research, prior findings were cumulated indicating that the interoperability between Internet of Things-based real-time production logistics and cyber-physical process monitoring systems can decide upon the progression of operations advancing a system to the intended state in CPPSs. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout March and August 2021, with search terms including “cyber-physical production systems”, “cyber-physical manufacturing systems”, “smart process manufacturing”, “smart industrial manufacturing processes”, “networked manufacturing systems”, “industrial cyber-physical systems,” “smart industrial production processes”, and “sustainable Internet of Things-based manufacturing systems”. As we analyzed research published between 2017 and 2021, only 489 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, we decided on 164, chiefly empirical, sources. Subsequent analyses should develop on real-time sensor networks, so as to configure the importance of artificial intelligence-driven big data analytics by use of cyber-physical production networks.

scholarly journals Interdisciplinary engineering of cyber-physical production systems: highlighting the benefits of a combined interdisciplinary modelling approach on the basis of an industrial case

2020 ◽  
Vol 6 ◽  
Author(s):  
Birgit Vogel-Heuser ◽  
Markus Böhm ◽  
Felix Brodeck ◽  
Katharina Kugler ◽  
Sabine Maasen ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Production Systems ◽  
A Priori ◽  
Interdisciplinary Approach ◽  
Boundary Crossing ◽  
Use Case ◽  
Organizational Models ◽  
Organizational Conditions ◽  
Industrial Use ◽  
Cyber Physical Production Systems

In the context of cross-disciplinary and cross-company cooperation, several challenges in developing manufacturing systems are revealed through industrial use cases. To tackle these challenges, two propositions are used in parallel. First, coupling technical models representing different content areas facilitates the detection of boundary crossing consequences, either by using a posteriori or a priori connection. Second, it is necessary to enrich these coupled technical models with team and organizational models as interventions focusing on the collaboration between individuals and teams within broader organizational conditions. Accordingly, a combined interdisciplinary approach is proposed. The feasibility and benefits of the approach is proven with an industrial use case. The use case shows that inconsistencies among teams can be identified by coupling engineering models and that an integrated organizational model can release the modelling process from communication barriers.

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