Modeling and Simulation Methodologies for Digital Twin in Industry 4.0

The concept of Industry 4.0 represents an innovative vision of what will be the factory of the future. The principles of this new paradigm are based on interoperability and data exchange between dierent industrial equipment. In this context, Cyber- Physical Systems (CPSs) cover one of the main roles in this revolution. The combination of models and the integration of real data coming from the field allows to obtain the virtual copy of the real plant, also called Digital Twin. The entire factory can be seen as a set of CPSs and the resulting system is also called Cyber-Physical Production System (CPPS). This CPPS represents the Digital Twin of the factory with which it would be possible analyze the real factory. The interoperability between the real industrial equipment and the Digital Twin allows to make predictions concerning the quality of the products. More in details, these analyses are related to the variability of production quality, prediction of the maintenance cycle, the accurate estimation of energy consumption and other extra-functional properties of the system. Several tools [2] allow to model a production line, considering dierent aspects of the factory (i.e. geometrical properties, the information flows etc.) However, these simulators do not provide natively any solution for the design integration of CPSs, making impossible to have precise analysis concerning the real factory. Furthermore, for the best of our knowledge, there are no solution regarding a clear integration of data coming from real equipment into CPS models that composes the entire production line. In this context, the goal of this thesis aims to define an unified methodology to design and simulate the Digital Twin of a plant, integrating data coming from real equipment. In detail, the presented methodologies focus mainly on: integration of heterogeneous models in production line simulators; Integration of heterogeneous models with ad-hoc simulation strategies; Multi-level simulation approach of CPS and integration of real data coming from sensors into models. All the presented contributions produce an environment that allows to perform simulation of the plant based not only on synthetic data, but also on real data coming from equipments

A Mini Review on the utilization of Reinforcement Learning with OPC UA

Reinforcement Learning (RL) is a powerful machine learning paradigm that has been applied in various fields such as robotics, natural language processing and game playing achieving state-of-the-art results. Targeted to solve sequential decision making problems, it is by design able to learn from experience and therefore adapt to changing dynamic environments. These capabilities make it a prime candidate for controlling and optimizing complex processes in industry. The key to fully exploiting this potential is the seamless integration of RL into existing industrial systems. The industrial communication standard Open Platform Communications UnifiedArchitecture (OPC UA) could bridge this gap. However, since RL and OPC UA are from different fields,there is a need for researchers to bridge the gap between the two technologies. This work serves to bridge this gap by providing a brief technical overview of both technologies and carrying out a semi-exhaustive literature review to gain insights on how RL and OPC UA are applied in combination. With this survey, three main research topics have been identified, following the intersection of RL with OPC UA. The results of the literature review show that RL is a promising technology for the control and optimization of industrial processes, but does not yet have the necessary standardized interfaces to be deployed in real-world scenarios with reasonably low effort.Comment: submitted to INDIN'2

A cyber-physical machine tools platform using OPC UA and MTConnect

Cyber-Physical Machine Tools (CPMT) represent a new generation of machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous. Development of CPMT requires standardized information modelling method and communication protocols for machine tools. This paper proposes a CPMT Platform based on OPC UA and MTConnect that enables standardized, interoperable and efficient data communication among machine tools and various types of software applications. First, a development method for OPC UA-based CPMT is proposed based on a generic OPC UA information model for CNC machine tools. Second, to address the issue of interoperability between OPC UA and MTConnect, an MTConnect to OPC UA interface is developed to transform MTConnect information model and its data to their OPC UA counterparts. An OPC UA-based CPMT prototype is developed and further integrated with a previously developed MTConnect-based CPMT to establish a common CPMT Platform. Third, different applications are developed to demonstrate the advantages of the proposed CPMT Platform, including an OPC UA Client, an advanced AR-assisted wearable Human-Machine Interface and a conceptual framework for CPMT powered cloud manufacturing environment. Experimental results have proven that the proposed CPMT Platform can significantly improve the overall production efficiency and effectiveness in the shop floor

Industry 4.0 - Digital Twin Applied to Direct Digital Manufacturing

Acknowledgements: This work is supported by the Fundação para a Ciência e a Tecnologia (FCT) and Centro2020 through the Project references: UID/Multi/04044/2013 and PAMI - ROTEIRO/0328/2013 (Nº 022158). The authors gratefully acknowledge to OPTIMA and CADFLOW.Marinha Grande is a city in Leiria’s district, Portugal. Marinha Grande is known as the moulding city, influenced by the glass, plastic and rapid manufacturing industry. Its history comes from the 18th century with the first glass factory. In order to improve technological development in the local industry, Centre for rapid and sustainable product development (CDRsp) was established in 2007. With that historical know-how and data-based moulding manufacturing, this work goal is to link that data with today’s technology, implementing the Industry 4.0. That information would be stored in a Cloud-Based Design and Manufacturing (CBDM) as well as the real-time operational data. Accessing to that cloud, the design and production engineers can work together to digitally create a product without having to stop the machinery. To implement these concepts, this paper suggests a Digital Twin (DT) to take advantage of the historical information allied to the existent industrial machinery. It suggests a digital twin of a robotic arm with an additive or hybrid manufacturing tool, printing big parts (e.g. garden benches or urban furniture) with reused materials such as tire, cork, wood or stone pow loads.info:eu-repo/semantics/publishedVersio

Model-based data generation for the evaluation of functional reliability and resilience of distributed machine learning systems against abnormal cases

Future production technologies will comprise a multitude of systems whose core functionality is closely related to machine-learned models. Such systems require reliable components to ensure the safety of workers and their trust in the systems. The evaluation of the functional reliability and resilience of systems based on machine-learned models is generally challenging. For this purpose, appropriate test data must be available, which also includes abnormal cases. These abnormal cases can be unexpected usage scenarios, erroneous inputs, accidents during operation or even the failure of certain subcomponents. In this work, approaches to the model-based generation of an arbitrary abundance of data representing such abnormal cases are explored. Such computer-based generation requires domain-specific approaches, especially with respect to the nature and distribution of the data, protocols used, or domain-specific communication structures. In previous work, we found that different use cases impose different requirements on synthetic data, and the requirements in turn imply different generation methods [1]. Based on this, various use cases are identified and different methods for computer-based generation of realistic data, as well as for the quality assessment of such data, are explored. Ultimately we explore the use of Federated Learning (FL) to address data privacy and security challenges in Industrial Control Systems. FL enables local model training while keeping sensitive information decentralized and private to their owners. In detail, we investigate whether FL can benefit clients with limited knowledge by leveraging collaboratively trained models that aggregate client-specific knowledge distributions. We found that in such scenarios federated training results in a significant increase in classification accuracy by 31.3% compared to isolated local training. Furthermore, as we introduce Differential Privacy, the resulting model achieves on par accuracy of 99.62% to an idealized case where data is independent and identically distributed across clients

A framework concept for data visualization and structuring in a complex production process

This paper provides a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives. The concept is tested and validated on a smaller simulation of a paint booth with several interconnected and supporting control systems to prove the functionality and usefulness in this kind of production system.The paper presents the resulting five abstraction levels in the framework concept, from a production top view down to the signal exchange between the different resources in one production cell, together with additional features. The simulation proves the setup with Sequence Planner and the visual interface to work by extract and present process data from a running sequence