An Approach to Automatically Distribute and Access Knowledge within Networked Embedded Systems in Factory Automation

This thesis presents a novel approach for automatically distribute and access knowledge within factory automation systems built by networked embedded systems. Developments on information, communication and computational technologies are making possible the distribution of tasks within different control resources, resources which are networked and working towards a common objective optimizing desired parameters. A fundamental task for introducing autonomy to these systems, is the option for represent knowledge, distributed within the automation network and to ensure its access by providing access mechanisms. This research work focuses on the processes for automatically distribute and access the knowledge.Recently, the industrial world has embraced service-oriented as architectural (SOA) patterns for relaxing the software integration costs of factory automation systems. This pattern defines a services provider offering a particular functionality, and service requesters which are entities looking for getting their needs satisfied. Currently, there are a few technologies allowing to implement a SOA solution, among those, Web Technologies are gaining special attention for their solid presence in other application fields. Providers and services using Web technologies for expressing their needs and skills are called Web Services. One of the main advantage of services is the no need for the service requester to know how the service provider is accomplishing the functionality or where the execution of the service is taking place. This benefit is recently stressed by the irruption of Cloud Computing, allowing the execution of certain process by the cloud resources.The caption of human knowledge and the representation of that knowledge in a machine interpretable manner has been an interesting research topic for the last decades. A well stablished mechanism for the representation of knowledge is the utilization of Ontologies. This mechanism allows machines to access that knowledge and use reasoning engines in order to create reasoning machines. The presence of a knowledge base allows as clearly the better identification of the web services, which is achievable by adding semantic notations to the service descriptors. The resulting services are called semantic web services.With the latest advances on computational resources, system can be built by a large number of constrained devices, yet easily connected, building a network of computational nodes, nodes that will be dedicated to execute control and communication tasks for the systems. These tasks are commanded by high level commanding systems, such as Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) modules. The aforementioned technologies allow a vertical approach for communicating commanding options from MES and ERP directly to the control nodes. This scenario allows to break down monolithic MES systems into small distributed functionalities, if these functionalities use Web standards for interacting and a knowledge base as main input for information, then we are arriving to the concept of Open KnowledgeDriven MES Systems (OKD-MES).The automatic distribution of the knowledge base in an OKD-MES mechanism and the accomplishment of the reasoning process in a distributed manner are the main objectives for this research. Thus, this research work describes the decentralization and management of knowledge descriptions which are currently handled by the Representation Layer (RPL) of the OKD-MES framework. This is achieved within the encapsulation of ontology modules which may be integrated by a distributed reasoning process on incoming requests. Furthermore, this dissertation presents the concept, principles and architecture for implementing Private Local Automation Clouds (PLACs), built by CPS.The thesis is an article thesis and is composed by 9 original and referred articles and supported by 7 other articles presented by the author

Building University-Industry Co-Innovation Networks in Transnational Innovation Ecosystems : Towards a Transdisciplinary Approach of Integrating Social Sciences and Artificial Intelligence

This paper presents a potential solution to fill a gap in both research and practice that there are few interactions between transnational industry cooperation (TIC) and transnational university cooperation (TUC) in transnational innovation ecosystems. To strengthen the synergies between TIC and TUC for innovation, the first step is to match suitable industrial firms from two countries for collaboration through their common connections to transnational university/academic partnerships. Our proposed matching solution is based on the integration of social science theories and specific artificial intelligence (AI) techniques. While the insights of social sciences, e.g., innovation studies and social network theory, have potential to answer the question of why TIC and TUC should be looked at as synergetic entities with elaborated conceptualization, the method of machine learning, as one specific technic off AI, can help answer the question of how to realize that synergy. On the way towards a transdisciplinary approach to TIC and TUC synergy building, or creating transnational university-industry co-innovation networks, the paper takes an initial step by examining what the supports and gaps of existing studies on the topic are, and using the context of EU-China science, technology and innovation cooperation as a testbed. This is followed by the introduction of our proposed approach and our suggestions for future research.publishedVersionPeer reviewe

Encapsulation of MES Functionalities as RESTful Web Services for Knowledge-Driven Manufacturing Systems

Computer and network technologies are growing rapidly nowadays. For this reason, many doors are opened for implementing some of these technologies in different areas. Currently, manufacturing systems is considered as main consumer of new technologies such as web services and knowledge-based systems. therefore, new terms came to the surface like industry 4.0 and IoT devices. An example could be seen in the eScop pro-ject. The concept idea is addressed as designing a knowledge driven manufacturing system which is capable to be applied on various industrial facilities. In this regards, this thesis aims to define MES functions for the Open Knowledge-Driven Manufacturing Execution System (OKD-MES). The study took place on FASTory line located at FAST Lab. In Tampere University of Technology. It is used as a validation case study to proof the implementation of the functions. The objective of this thesis focuses on employing the MES functions as web services to suit the OKD-MES concept. In this research, the state of art reviews MES functions which were defined at a general level by MESA. Then, these MES functions are specifically defined to work with the layer concept of the OKD-MES which presented by the eScop project. The approach which has been developed for MES functions is a general platform that can be used for all MES functions even though the industry type is different. This approach tends to maximize the flexibility of configuring MES functions in the manufacturing system. At the same time, it minimizes the dependencies in the OKD-MES. With this approach, the user is responsible for defining the description of web services in ontology form as configuration and providing the functionality that suits the manufacturing system as functional scripts. These features of the presented approach allow the user to implement new logic for the MES functions or even use ready-made tools. Finally, the developed approach is tested with a production scenario. The results of the test show the advantage of using this approach in terms of configurability and simplicity of installation. Nevertheless, the presented approach holds chances for future development

Role-Based data visualization for Industrial IoT

The competition among manufacturers in the global markets calls for the enhancement of the agility and performance of the production process and the quality of products. As a result, the production systems should be designed in a way to provide decision-makers with visibility and analytics. To fulfill these objectives, the development of factory information systems in manufacturing industries has been introduced as a practical solution in the past few years. On the other hand, the volume of data generated on the factory floor is rising. To improve the efficiency of manufacturing process, this amount of data should be analyzed by decision-makers. To cope with this challenge, visualization assists decision-makers to gain insight into data. To give a better perspective of collected data to decision-makers, effective visualization techniques should be employed. Adequate data visualization allows the end user to have better understanding of data and make effective decisions faster. Meanwhile, the adoption of the Service-Oriented Architecture (SOA) and Internet of Things (IoT) as state-of-the-art technologies are among the most prominent trends in industrial automation. IoT technology is expected to generate and collect data from various sensors and devices within the production system, and enables enterprises to have real-time visibility into the flow of production process. Moreover, data received from factory floor should be transmitted from back-end side to the front-end side for future analysis. To implement the exchange of data efficiently, the solution should support different communication protocols to make interoperability among heterogeneous devices on shop floor. This study describes an approach for building a role-based visualization of industrial IoT. An extensible architecture was provided by which the future growth of data and emerging new protocols has been anticipated. By using the IoT platform introduced in this thesis, selected KPIs can be monitored by different levels of enterprise. Three prototype IoT dashboards have been implemented for a pilot production line, “Festo didactic training line” located in Seinäjoki University of Applied Sciences (SeAMK) and results have been validated

Design and implementation of a human-robot collaborative assembly workstation in a modular robotized production line

Over the last decades, the Industrial Automation domain at factory shop floors experienced an exponential growth in the use of robots. The objective of such change aims to increase the efficiency at reasonable cost. However, not all the tasks formerly performed by humans in factories, are fully substituted by robots nowadays, specially the ones requiring high-level of dexterity. In fact, Europe is moving towards implementing efficient work spaces were humans can work safely, aided by robots. In this context, industrial and research sectors have ambitious plans to achieve solutions that involve coexistence and simultaneity at work between humans and collaborative robots, a.k.a. “cobots” or co-robots, for permitting a safe interaction for the same or interrelated manufacturing processes. Many cobot producers started to present their products, but those arrived before the industry have clear and several needs of this particular technology. This work presents an approach about how to demonstrate human-robot collaborative manufacturing? How to implement a dual-arm human-robot collaborative workstation? How to integrate a human-robot collaborative workstation into a modular interconnected production line? and What are the advantages and challenges of current HRC technologies at the shop floor? by documenting the formulation of a human-robot collaborative assembly process, implemented by designing and building an assembly workstation that exemplifies a scenario of interaction between a dual arm cobot and a human operator, in order to assembly a product box, as a part of a large-scale modular robotized production line. The model produced by this work is part of the research facilities at the Future Automation Systems and Technologies Laboratory in Tampere University

A Framework for Adoption of Open Knowledge Driven-Manufacturing Execution System

Open Knowledge Driven – Manufacturing Execution System (OKD-MES) is a comprehensive software solution for all activities in shop floor level. Like any other software system, it comes with packages of code. Adoption of OKD-MES means adoption of technologies like web services as a standard of communication, controllers with web capabilities, Service Oriented Architecture (SOA), Ontologies in manufacturing etc. OKD-MES adoption in whole and part inspires the modern industries to move towards a digital world. Wide spread adoption of OKD-MES is possible when the user can identify the value in the system with clear metrics. The actors of this system come from different sections of the industry hence, information delivery plays a crucial role in the adoption process. OKD-MES has a layered architecture hence, a layer-by-layer adoption methodology is proposed. For every layer, guidelines, manuals and organized training contents are prepared to provide the user a working knowledge of the architecture, communication between layers, devices, etc. It is believed that training of every aspect of the system is essential for the adoption process. A use case approach is followed to provide developers, engineers and students a chance to understand working of the system. Tutorials are prepared from the obstacle faced in developing such a system. Pedagogical techniques are identified to facilitate the adoption process in both technical and commercial groups. The guideline and training materials are validated with participants from different groups and their concerns are addressed in the training materials. An attempt has been made to capture the users verbatim with the help of a feedback procedure. This framework strives to initiate and maintain the process of adoption of OKD-MES