Energy-Efficient Technologies for High-Performance Manufacturing Industries

Ph.DDOCTOR OF PHILOSOPH

Modelling uncertainties in human-robot industrial collaborations

With the rise of Industry 4.0 technological trends, there is a growing tendency in manufacturing automation towards collaborative robots. Human-robot collaboration (HRC) is motivated by the combination of complementary human and robot skills and intelligence, which can increase productivity, flexibility and adaptability. However, it is still challenging to achieve safe and efficient human-robot collaborative systems due to the dynamics of human presence, uncertainties in the dynamic environment, and the need for adaptability. Such uncertainties could relate to the human-robot capabilities and availability, parts positioning, unexpected obstacles, etc. This paper develops time-based simulations and event-based simulations to model and analyse the dynamic factors in human-robot collaboration systems. The novelty of this work is the systematic modelling and analysis of dynamic factors in HRC manufacturing scenarios through the development of digital simulations of human-robot collaboration scenarios while considering the dynamic nature of humans and environments. A real-world industrial case study was redesigned into a collaborative workstation. The simulated scenario is developed using the software called Tecnomatix Process Simulate, which can help to visualise the dynamic factors and analyse the impact of the factors on the HRC. The simulation illustrates and analyses possible uncertainties in human-robot industrial collaborative workstations, which can contribute to the future design of HRC industrial workstations and the optimisation of productivity

Adapter module for self-learning production systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e ComputadoresThe dissertation presents the work done under the scope of the NP7 Self-Learning project regarding the design and development of the Adapter component as a foundation for the Self-Learning Production Systems (SLPS). This component is responsible to confer additional proprieties to production systems such as lifecycle learning, optimization of process parameters and, above all, adaptation to different production contexts. Therefore, the SLPS will be an evolvable system capable to self-adapt and learn in response to dynamic contextual changes in manufacturing production process in which it operates. The key assumption is that a deeper use of data mining and machine learning techniques to process the huge amount of data generated during the production activities will allow adaptation and enhancement of control and other manufacturing production activities such as energy use optimization and maintenance. In this scenario, the SLPS Adapter acts as a doer and is responsible for dynamically adapting the manufacturing production system parameters according to changing manufacturing production contexts and, most important, according to the history of the manufacturing production process acquired during SLPS run time.To do this, a Learning Module has been also developed and embedded into the SLPS Adapter. The SLPS Learning Module represents the processing unit of the SLPS Adapter and is responsible to deliver Self-learning capabilities relying on data mining and operator’s feedback to up-date the execution of adaptation and context extraction at run time. The designed and implemented SLPS Adapter architecture is assessed and validated into several application scenario provided by three industrial partners to assure industrial relevant self-learning production systems. Experimental results derived by the application of the SLPS prototype into real industrial environment are also presented

DEVELOPMENT OF A CONCEPTUAL FRAMEWORK FOR INTEGRATING INTELLIGENT-PRODUCT STRUCTURES INTO A FLEXIBLE MANUFACTURING SYSTEM

Industrial Engineerin

A framework to offer high value manufacturing through self-reconfigurable manufacturing systems

The High Value Manufacturing (HVM) sector is vital for developed countries due to the creation of innovative products with advanced technology that cannot be reproduced at the same cost and time with traditional technology. The main challenge for HVM is to rapidly increase production volume from one-off products to low production volume. This requires highly flexible manufacturing systems that can produce new products at variable production volumes. Current manufacturing systems, classified as dedicated, flexible and reconfigurable systems, are limited to produce one type of product(s), within a production volume range and have fixed layouts of machines. Thus, there is a need for highly flexible systems that can rapidly adjust their production volume according to the production demand (i.e. main HVM challenge). Therefore, a novel manufacturing framework, called INTelligent REconfiguration for a raPID production change (INTREPID), is presented in this thesis. INTREPID consists of a user interface and communications platform, a job allocation system, a globally distributed network of Reconfigurable Manufacturing Centres (RMCs), consisting of interconnected factories, and Self-Reconfigurable Manufacturing Systems (S-RMSs). The highly flexible S-RMS consists of movable machines and Mobile Manufacturing Robots (MMRs). The novelty of the S-RMS is its capability of forming layouts bespoke to the current production needs. The vision of INTREPID is to offer global HVM services through the network of RMCs. The job allocation system determines the best possible RMCs or factories to perform a job by considering the complexity of the production requirements and the status of the available S-RMSs at each factory. The planning of the production with S-RMS is challenging due to its high flexibility. The main example of this flexibility is the possibility to create layouts bespoke to current production needs. Yet, this flexibility involves the challenges of determining allocations and schedules of tasks to robots and machines, positions to manufacture, and routes to reach those positions. In manufacturing systems with fixed layouts, production plans are determined by solving a sequence of problems. However, for the S-RMS, it is proposed to determine production plans with a single problem that covers the scheduling, machine layout and vehicle routing problems simultaneously. This novel problem is called the Scheduling, positions Assigning and Routing problem (SAR) problem. In order to determine the best possible production plan(s) for the S-RMS, it is necessary to use optimisation methods. Dozens of elements, characteristics and assumptions from the constituent problems might be included in the formulation of the SAR problem. Elements, characteristics and assumptions can be considered as decision variables on whether to include or not the elements and characteristics and under which assumptions in the formulation. There are two types of decision variables. Fundamental variables are natural to the SAR problem (e.g. manufacturing resources, factory design and operation), whilst auxiliary variables arise from the aim to simplify the formulation of the optimisation problem (i.e. time formulated as discrete or continuous). Due to the large number of decision variables, there might be millions of possible ways to formulate the SAR problem (i.e. the SAR problem space). Some of these variants are intractable to be solved with optimisation methods. Hence, before formulating the SAR problem, it is necessary to select a problem(s) that is realistic to industrial scenarios but solvable with optimisation methods. Existing selection methods work with pairwise comparisons of alternatives. However, for a space of millions of SAR problems, pairwise comparisons are intractable. Hence, in this thesis, a novel Decision Making Methodology (DMM) based on the controlled convergence method is presented. The DMM helps down-selecting one or a few SAR problems from millions of possible SAR problems. The DMM is demonstrated with a case study of the SAR problem and the results show a significant reduction of the reviewed SAR problems and the time to select them

Factories of the Future

Engineering; Industrial engineering; Production engineerin

An Application of Context-sensitive Computing for Flexible Manufacturing System Optimization

Recent advancements in embedded systems, computing, networking, WS and SOA have opened the door for seamless integration of plant floor devices to higher enterprise level applications. Semantic web technologies, knowledge-based systems, context-sensitive computing and associated application development are widely explored in this regard. Ubiquitous and pervasive computing are the main domains of interest among many researchers so far. However, context-sensitive computing in manufacturing, particularly, relevant research and development in a production environment like FMS is relatively new and growing.Dynamic job (re)scheduling and dispatching are becoming an essential part of modern FMS controls. The foremost drive is to deal with the chaotic nature of the production environment while keeping plant performance indicators unaffected. Process plans in FMS need to consider several dynamic factors, like demand fluctuations, extreme product customizations and run time priority changes. To meet this plant level dynamism, complex control architectures are used to provide an automatic response to the unexpected events. These runtime responses deal with final moment change of the control parameters that eventually influences the key performance indicators (KPIs) like machine utilization rate and overall equipment effectiveness (OEE). In response, plant controls are moving towards more decentralized and adaptive architectures, promoting integration of different support applications. The applications aim to optimize the plant operations in terms of autonomous decision making, adaptation to sudden failure, system (re) configuration and response to unexpected events for global factory optimization.The research work documented in this thesis presents the advantages of bridging the mentioned two domains of context-sensitive computing and FMS optimization, mainly to facilitate context management at factory floor for improved transparency and to better respond for real time optimization through context-based optimization support system.This manuscript presents a context-sensitive optimization approach for FMS, considering machine utilization rate and overall equipment effectiveness (OEE) as the KPIs. Runtime contextual entities are used to monitor KPIs continuously to update an ontology-based context model, and subsequently convert it into business relevant information via context management. The delivered high level knowledge is further utilized by an optimization support system (OSS) to infer: optimal job (re) scheduling and dispatching, keeping a higher machine utilization rate at runtime. The proposed solution is presented as add-on functionality for FMS control, where a modular development of the overall approach provides the solution generic and extendable across other domains. The key components are functionally implemented to a practical FMS use-case within SOA and WS-based control architecture, resulting improvement of the machine utilization rate and the enhancement of the OEE at runtime

Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

An Application of Context-sensitive Computing for Flexible Manufacturing System Optimization

Recent advancements in embedded systems, computing, networking, WS and SOA have opened the door for seamless integration of plant floor devices to higher enterprise level applications. Semantic web technologies, knowledge-based systems, context-sensitive computing and associated application development are widely explored in this regard. Ubiquitous and pervasive computing are the main domains of interest among many researchers so far. However, context-sensitive computing in manufacturing, particularly, relevant research and development in a production environment like FMS is relatively new and growing.Dynamic job (re)scheduling and dispatching are becoming an essential part of modern FMS controls. The foremost drive is to deal with the chaotic nature of the production environment while keeping plant performance indicators unaffected. Process plans in FMS need to consider several dynamic factors, like demand fluctuations, extreme product customizations and run time priority changes. To meet this plant level dynamism, complex control architectures are used to provide an automatic response to the unexpected events. These runtime responses deal with final moment change of the control parameters that eventually influences the key performance indicators (KPIs) like machine utilization rate and overall equipment effectiveness (OEE). In response, plant controls are moving towards more decentralized and adaptive architectures, promoting integration of different support applications. The applications aim to optimize the plant operations in terms of autonomous decision making, adaptation to sudden failure, system (re) configuration and response to unexpected events for global factory optimization.The research work documented in this thesis presents the advantages of bridging the mentioned two domains of context-sensitive computing and FMS optimization, mainly to facilitate context management at factory floor for improved transparency and to better respond for real time optimization through context-based optimization support system.This manuscript presents a context-sensitive optimization approach for FMS, considering machine utilization rate and overall equipment effectiveness (OEE) as the KPIs. Runtime contextual entities are used to monitor KPIs continuously to update an ontology-based context model, and subsequently convert it into business relevant information via context management. The delivered high level knowledge is further utilized by an optimization support system (OSS) to infer: optimal job (re) scheduling and dispatching, keeping a higher machine utilization rate at runtime. The proposed solution is presented as add-on functionality for FMS control, where a modular development of the overall approach provides the solution generic and extendable across other domains. The key components are functionally implemented to a practical FMS use-case within SOA and WS-based control architecture, resulting improvement of the machine utilization rate and the enhancement of the OEE at runtime

Factories of the Future

Engineering; Industrial engineering; Production engineerin