Improving transferability between different engineering stages in the development of automated material flow modules

For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from the electrical, mechanical or software engineering, based on a modular architecture. Considering the development of these modules, there are different stages, e.g. module planning or functional engineering, which have to be completed. A reference model that addresses the different stages for the engineering of aPS is proposed by AutomationML. Due to these different stages and the integration of several engineering disciplines, e.g. mechanical, electrical/electronic or software engineering, information not limited to one discipline are stored redundantly increasing the effort to transfer information and the risk of inconsistency. Although, data formats for the storage and exchange of plant engineering information exist, e.g. AutomationML, fixed domain specific structures and relations of the information, e.g. for automated material flow systems (aMFS), are missing. This paper presents the integration of a meta model into the development of modules for aMFS to improve the transferability and consistency of information between the different engineering stages and the increasing level of detail from the coarse-grained plant planning to the fine-grained functional engineering.Comment: 11 pages, https://ieeexplore.ieee.org/abstract/document/7499821

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

Model-Driven Design and Development of Flexible Automated Production Control Configurations for Industry 4.0

The continuous changes of the market and customer demands have forced modern automation systems to provide stricter Quality of service (QoS) requirements. This work is centered in automation production system flexibility, understood as the ability to shift from one controller configuration to a different one, in the most quick and cost-effective way, without disrupting its normal operation. In the manufacturing field, this allows to deal with non-functional requirements such as assuring control system availability or workload balancing, even in the case of failure of a machine, components, network or controllers. Concretely, this work focuses on flexible applications at production level, using Programmable Logic Controllers (PLCs) as primary controllers. The reconfiguration of the control system is not always possible as it depends on the process state. Thus, an analysis of the system state is necessary to make a decision. In this sense, architectures based on industrial Multi Agent Systems (MAS) have been used to provide this support at runtime. Additionally, the introduction of these mechanisms makes the design and the implementation of the control system more complex. This work aims at supporting the design and development of such flexible automation production systems, through the proposed model-based framework. The framework consists of a set of tools that, based on models, automate the generation of control code extensions that add flexibility to the automation production system, according to industry 4.0 paradigm.This work was financed by MCIU/AEI/FEDER, UE (grant number RTI2018-096116-B-I00) and by GV/EJ (grant number IT1324-19)

Methods for Semantic Interoperability in AutomationML-based Engineering

Industrial engineering is an interdisciplinary activity that involves human experts from various technical backgrounds working with different engineering tools. In the era of digitization, the engineering process generates a vast amount of data. To store and exchange such data, dedicated international standards are developed, including the XML-based data format AutomationML (AML). While AML provides a harmonized syntax among engineering tools, the semantics of engineering data remains highly heterogeneous. More specifically, the AML models of the same domain or entity can vary dramatically among different tools that give rise to the so-called semantic interoperability problem. In practice, manual implementation is often required for the correct data interpretation, which is usually limited in reusability. Efforts have been made for tackling the semantic interoperability problem. One mainstream research direction has been focused on the semantic lifting of engineering data using Semantic Web technologies. However, current results in this field lack the study of building complex domain knowledge that requires a profound understanding of the domain and sufficient skills in ontology building. This thesis contributes to this research field in two aspects. First, machine learning algorithms are developed for deriving complex ontological concepts from engineering data. The induced concepts encode the relations between primitive ones and bridge the semantic gap between engineering tools. Second, to involve domain experts more tightly into the process of ontology building, this thesis proposes the AML concept model (ACM) for representing ontological concepts in a native AML syntax, i.e., providing an AML-frontend for the formal ontological semantics. ACM supports the bidirectional information flow between the user and the learner, based on which the interactive machine learning framework AMLLEARNER is developed. Another rapidly growing research field devotes to develop methods and systems for facilitating data access and exchange based on database theories and techniques. In particular, the so-called Query By Example (QBE) allows the user to construct queries using data examples. This thesis adopts the idea of QBE in AML-based engineering by introducing the AML Query Template (AQT). The design of AQT has been focused on a native AML syntax, which allows constructing queries with conventional AML tools. This thesis studies the theoretical foundation of AQT and presents algorithms for the automated generation of query programs. Comprehensive requirement analysis shows that the proposed approach can solve the problem of semantic interoperability in AutomationML-based engineering to a great extent

A Knowledge Graph Based Integration Approach for Industry 4.0

The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories

A Systematic Mapping Study on Modeling for Industry 4.0

International audienceIndustry 4.0 is a vision of manufacturing in which smart, interconnected production systems optimize the complete value-added chain to reduce cost and time-to-market. At the core of Industry 4.0 is the smart factory of the future, whose successful deployment requires solving challenges from many domains. Model-based systems engineering (MBSE) is a key enabler for such complex systems of systems as can be seen by the increased number of related publications in key conferences and journals. This paper aims to characterize the state of the art of MBSE for the smart factory through a systematic mapping study on this topic. Adopting a detailed search strategy, 1466 papers were initially identified. Of these, 222 papers were selected and categorized using a particular classification scheme. Hence, we present the concerns addressed by the modeling community for Industry 4.0, how these are investigated, where these are published, and by whom. The resulting research landscape can help to understand, guide, and compare research in this field. In particular, this paper identifies the Industry 4.0 challenges addressed by the modeling community, but also the challenges that seem to be less investigated

A Model-based Approach for Designing Cyber-Physical Production Systems

The most recent development trend related to manufacturing is called "Industry 4.0". It proposes to transition from "blind" mechatronics systems to Cyber-Physical Production Systems (CPPSs). Such systems are capable of communicating with each other, acquiring and transmitting real-time production data. Their management and control require a structured software architecture, which is tipically referred to as the "Automation Pyramid". The design of both the software architecture and the components (i.e., the CPPSs) is a complex task, where the complexity is induced by the heterogeneity of the required functionalities. In such a context, the target of this thesis is to propose a model-based framework for the analysis and the design of production lines, compliant with the Industry 4.0 paradigm. In particular, this framework exploits the Systems Modeling Language (SysML) as a unified representation for the different viewpoints of a manufacturing system. At the components level, the structural and behavioral diagrams provided by SysML are used to produce a set of logical propositions about the system and components under design. Such an approach is specifically tailored towards constructing Assume-Guarantee contracts. By exploiting reactive synthesis techniques, contracts are used to prototype portions of components' behaviors and to verify whether implementations are consistent with the requirements. At the software level, the framework proposes a particular architecture based on the concept of "service". Such an architecture facilitates the reconfiguration of components and integrates an advanced scheduling technique, taking advantage of the production recipe SysML model. The proposed framework has been built coupled with the construction of the ICE Laboratory, a research facility consisting of a full-fledged production line. Such an approach has been adopted to construct models of the laboratory, to virtual prototype parts of the system and to manage the physical system through the proposed software architecture

Ontology-Based Data Integration in Multi-Disciplinary Engineering Environments: A Review

Today's industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontologybased data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE

Formal Digital Description of Production Equipment Modules for supporting System Design and Deployment

The requirements for production systems are moving towards higher flexibility, adaptability and reactivity. Increasing volatility in global and local economies, shorter product life cycles and the ever-increasing number of product variants arising from product customization have led to a demand for production systems which can respond more rapidly to these changing requirements. Therefore, whenever a new product, or product variant, enters production, the production system designer must be able to create an easily-reconfigurable production system which not only meets the User Requirements (UR) but is quick and cost-efficient to set up. Modern production systems must be able to integrate new product variants with minimum effort. In the event of a product changeover or an unforeseen incident, such as the mechanical failure of a production resource, it must be possible to reconfigure the production system smoothly and seamlessly by adding, removing or altering the resources. Ideally, auto-configuration should obviate the need to manually re-programme the system once it has been reconfigured.The cornerstone of any solution to the above-mentioned challenges is the concept of being able to create formalised, comprehensive descriptions of all production resources. Providing universally-recognised digital representations of all the multifarious resources used in a production system would enable a standardised exchange of information between the different actors involved in building a new production system. Such freely available and machine-readable information could also be utilised by the wide variety of software tools that come into play during the different life cycle phases of a production system, thus considerably extending its useful life. These digital descriptions would also offer a multi-faceted foundation for the reconfiguration of production systems. The production paradigms presented here would support state-of-the-art production systems, such as Reconfigurable Manufacturing Systems (RMSs), Holonic Manufacturing Systems (HMSs) and Evolvable Production Systems (EPSs).The methodological framework for this research is Design Research Methodology (DRM) supported with Systems Engineering, Action Research, and case-based research. The first two were used to develop the concept and data models for the resource descriptions, through a process of iterative development. The case-based research was used for verification, through the modelling and analysis of two separate production systems used in this research. The concept, on which this thesis is based, is itself based on the triplicity of production system design, i.e. Product, Process and Resource. The processes, are implemented through the capabilities of the resources, which are thus directly linked to the product requirements. The driving force behind this new approach to production system design is its strong emphasis on making production systems that can be reconfigured easily. Successful system reconfiguration can only be achieved, however, if all the required production resources can be quickly and easily compared to all the available production resources in one unified, and universally accepted form. These descriptions must not only be able to capture all of a production system’s capabilities, but must also include information about its interfaces, kinematics, technical properties and its control and communication abilities.The answer to this lies in the Emplacement Concept, which is described and developed in this thesis. The Emplacement Concept proposes the creation of a multi-layered Generic Model containing information about production resources in three different layers. These are the Abstract Module Description (AMD), the Module Description (MD), and the Module Instance Description (MID). Each of these layers has unique characteristics which can be utilised in the different phases of designing, commissioning and reconfiguring a production system. The AMD is the most abstract (general) descriptive layer and can be used for initial system design iterations. It ensures that the proposed resources for the production system are exchangeable and interchangeable, and thus guides the selection of production resources and the implementation (or reconfiguration) of a production system. The MD is the next level down, and provides a more detailed description of the type of production resource, providing ’finer granularity’ for the descriptions. The MID provides the finest level of granularity and contains invaluable information about the individual instances of a particular production resource. This research involves two practical implementations of the Generic Model. These are used to model and digitally represent all the production resources used in the two use-case environments. All the modules in the production systems (25 in all) were modelled and described with the data models developed here. In fact, we were able to freeze the data models after the first case study, as they didn’t need any major changes in order to model the production resources of the second use-case environment. This demonstrates the general applicability of the proposed approach for modelling modular production resources.The advantages of being able to describe production resources in a unified digital form are many and varied. For example, production systems which are described in this way are much more agile. They can react faster to changes in demand and can be reconfigured easily and quickly. The resource descriptions also improve the sustainability of production systems because they provide detailed information about the exact capabilities and characteristics of all the available resources. This means that production system designers are better placed to utilise ready-made modules, (design by re-use). Being able to use readily available production modules means that the Time to Market and Time to Volume are improved, as new production systems can be built or reconfigured using tested and fully operational modules, which can easily be integrated into an already operational production system. Finally, the resource descriptions are an essential source of information for auto-configuration tools, allowing automated, or semi-automated production system design. However, harvesting the full benefits of all these outcomes requires that the tools used to create new production systems can understand and utilise the modular descriptions proposed by this concept. This, in turn, presupposes that the all the formalised descriptions of the production modules provided here will be made publicly available, and will form the basis for an ever-expanding library of such descriptions

Adaptive Robot Framework: Providing Versatility and Autonomy to Manufacturing Robots Through FSM, Skills and Agents

207 p.The main conclusions that can be extracted from an analysis of the current situation and future trends of the industry,in particular manufacturing plants, are the following: there is a growing need to provide customization of products, ahigh variation of production volumes and a downward trend in the availability of skilled operators due to the ageingof the population. Adapting to this new scenario is a challenge for companies, especially small and medium-sizedenterprises (SMEs) that are suffering first-hand how their specialization is turning against them.The objective of this work is to provide a tool that can serve as a basis to face these challenges in an effective way.Therefore the presented framework, thanks to its modular architecture, allows focusing on the different needs of eachparticular company and offers the possibility of scaling the system for future requirements. The presented platform isdivided into three layers, namely: interface with robot systems, the execution engine and the application developmentlayer.Taking advantage of the provided ecosystem by this framework, different modules have been developed in order toface the mentioned challenges of the industry. On the one hand, to address the need of product customization, theintegration of tools that increase the versatility of the cell are proposed. An example of such tools is skill basedprogramming. By applying this technique a process can be intuitively adapted to the variations or customizations thateach product requires. The use of skills favours the reuse and generalization of developed robot programs.Regarding the variation of the production volumes, a system which permits a greater mobility and a faster reconfigurationis necessary. If in a certain situation a line has a production peak, mechanisms for balancing the loadwith a reasonable cost are required. In this respect, the architecture allows an easy integration of different roboticsystems, actuators, sensors, etc. In addition, thanks to the developed calibration and set-up techniques, the system canbe adapted to new workspaces at an effective time/cost.With respect to the third mentioned topic, an agent-based monitoring system is proposed. This module opens up amultitude of possibilities for the integration of auxiliary modules of protection and security for collaboration andinteraction between people and robots, something that will be necessary in the not so distant future.For demonstrating the advantages and adaptability improvement of the developed framework, a series of real usecases have been presented. In each of them different problematic has been resolved using developed skills,demonstrating how are adapted easily to the different casuistic