Web service control of component-based agile manufacturing systems

Current global business competition has resulted in significant challenges for manufacturing and production sectors focused on shorter product lifecyc1es, more diverse and customized products as well as cost pressures from competitors and customers. To remain competitive, manufacturers, particularly in automotive industry, require the next generation of manufacturing paradigms supporting flexible and reconfigurable production systems that allow quick system changeovers for various types of products. In addition, closer integration of shop floor and business systems is required as indicated by the research efforts in investigating "Agile and Collaborative Manufacturing Systems" in supporting the production unit throughout the manufacturing lifecycles. The integration of a business enterprise with its shop-floor and lifecycle supply partners is currently only achieved through complex proprietary solutions due to differences in technology, particularly between automation and business systems. The situation is further complicated by the diverse types of automation control devices employed. Recently, the emerging technology of Service Oriented Architecture's (SOA's) and Web Services (WS) has been demonstrated and proved successful in linking business applications. The adoption of this Web Services approach at the automation level, that would enable a seamless integration of business enterprise and a shop-floor system, is an active research topic within the automotive domain. If successful, reconfigurable automation systems formed by a network of collaborative autonomous and open control platform in distributed, loosely coupled manufacturing environment can be realized through a unifying platform of WS interfaces for devices communication. The adoption of SOA- Web Services on embedded automation devices can be achieved employing Device Profile for Web Services (DPWS) protocols which encapsulate device control functionality as provided services (e.g. device I/O operation, device state notification, device discovery) and business application interfaces into physical control components of machining automation. This novel approach supports the possibility of integrating pervasive enterprise applications through unifying Web Services interfaces and neutral Simple Object Access Protocol (SOAP) message communication between control systems and business applications over standard Ethernet-Local Area Networks (LAN's). In addition, the re-configurability of the automation system is enhanced via the utilisation of Web Services throughout an automated control, build, installation, test, maintenance and reuse system lifecycle via device self-discovery provided by the DPWS protocol...cont'd

Artificial Intelligence Advancements for Digitising Industry

In the digital transformation era, when flexibility and know-how in manufacturing complex products become a critical competitive advantage, artificial intelligence (AI) is one of the technologies driving the digital transformation of industry and industrial products. These products with high complexity based on multi-dimensional requirements need flexible and adaptive manufacturing lines and novel components, e.g., dedicated CPUs, GPUs, FPGAs, TPUs and neuromorphic architectures that support AI operations at the edge with reliable sensors and specialised AI capabilities. The change towards AI-driven applications in industrial sectors enables new innovative industrial and manufacturing models. New process management approaches appear and become part of the core competence in the organizations and the network of manufacturing sites. In this context, bringing AI from the cloud to the edge and promoting the silicon-born AI components by advancing Moore’s law and accelerating edge processing adoption in different industries through reference implementations becomes a priority for digitising industry. This article gives an overview of the ECSEL AI4DI project that aims to apply at the edge AI-based technologies, methods, algorithms, and integration with Industrial Internet of Things (IIoT) and robotics to enhance industrial processes based on repetitive tasks, focusing on replacing process identification and validation methods with intelligent technologies across automotive, semiconductor, machinery, food and beverage, and transportation industries.publishedVersio

Intelligent Management System for Driverless Vehicles

This research addresses concerns related to driverless vehicles by proposing the development of an Intelligent Management System (IMS). Emphasised in 'The Pathway to Driverless Cars Summary report and action plan' by the UK Department of Transport, key areas for improvement lie in vehicle reliability, maintenance, and passenger safety. The study targets compliance with Society of Automotive Engineers (SAE) Level 5 automation, concentrating on fully autonomous vehicles to enhance commuter satisfaction and overall vehicle performance. Despite advancements, challenges such as on-road safety and integration persist. The research unfolds through a two-stage development process aimed at achieving an Intelligent Management System for Driverless Vehicles (IMSDV). The initial stage, described in chapter 3 involves the creation of a 'Single Seat Driverless Pod' as a test apparatus, simulating various features found in existing driverless vehicles. This includes the development of mechanical steering components and a control system incorporating electronic hardware, sensors, actuators, controllers, wireless remote access, and software. The subsequent phase, described in chapter 4 focuses on autonomous navigation using Google Maps, intelligent motion control, localisation, and tracking algorithms within the driverless pod. The latter chapters of the thesis present the investigation of possible improvements in steering system components. A novel encapsulated vehicle wheel condition monitoring system, integrating the Internet of Things (IoT), is proposed to enhance maintainability, reliability, and passenger safety for driverless vehicles. Testing and validation are conducted in two segments. The driverless pod undergoes initial testing to validate its features and generate data for further sub-system development. Separately, the IoT-based monitoring system undergoes individual testing. The final step involves integrating the IoT capabilities into the driverless pod, testing the sub-system, and capturing relevant data. The thesis outlines the research scope, emphasising significant contributions, with a particular focus on the monitoring system for steering components in driverless vehicles, employing embedded IoT technology. This augmentation, alongside other original contribution, is strategically poised to enhance the maintainability, reliability, and safety of driverless vehicles at SAE Level 5. The concluding chapter succinctly revisits these distinctive contributions and additionally provides recommendations for advancing intelligent management systems for driverless vehicles

Safety-Critical Communication in Avionics

The aircraft of today use electrical fly-by-wire systems for manoeuvring. These safety-critical distributed systems are called flight control systems and put high requirements on the communication networks that interconnect the parts of the systems. Reliability, predictability, flexibility, low weight and cost are important factors that all need to be taken in to consideration when designing a safety-critical communication system. In this thesis certification issues, requirements in avionics, fault management, protocols and topologies for safety-critical communication systems in avionics are discussed and investigated. The protocols that are investigated in this thesis are: TTP/C, FlexRay and AFDX, as a reference protocol MIL-STD-1553 is used. As reference architecture analogue point-to-point is used. The protocols are described and evaluated regarding features such as services, maturity, supported physical layers and topologies.Pros and cons with each protocol are then illustrated by a theoretical implementation of a flight control system that uses each protocol for the highly critical communication between sensors, actuators and flight computers.The results show that from a theoretical point of view TTP/C could be used as a replacement for a point-to-point flight control system. However, there are a number of issues regarding the physical layer that needs to be examined. Finally a TTP/C cluster has been implemented and basic functionality tests have been conducted. The plan was to perform tests on delays, start-up time and reintegration time but the time to acquire the proper hardware for these tests exceeded the time for the thesis work. More advanced testing will be continued here at Saab beyond the time frame of this thesis

Advancements, challenges, and implications for navigating the autonomous vehicle revolution

The deployment of self-driving cars has significantly impacted society, offering several benefits such as better passenger safety, convenience, reduced fuel consumption, minimised traffic congestion and accidents, cost savings, and improved reliability. With the development of automated driving systems, autonomous vehicle technology has progressed from human-operated vehicles to conditional automation, utilising an array of sensors to constantly observe their surroundings for potential hazards. However, it is crucial to highlight that full autonomy has not yet been reached, and there are several problems involved with this revolutionary technology. This article explores the advancements, challenges, and implications inherent in the widespread adoption of autonomous vehicles. Recent advancements in sensor technology (cameras, RADARs, LiDARs, and ultrasonic sensors), artificial intelligence, the Internet of Things, and blockchain are just some of the topics covered in this article regarding autonomous vehicle development. These advancements are critical to evolving and incorporating autonomous vehicles into the transportation ecosystem. Furthermore, the analysis emphasises the considerable problems that must be overcome before self-driving cars can be widely adopted. These difficulties include security, safety, design, performance, and accuracy. Focused solutions such as increasing cybersecurity protections, refining safety standards, optimising vehicle design, enhancing performance capabilities, and assuring correct perception and decision-making are proposed to tackle these challenges. Lastly, autonomous cars have great promise for transforming transportation systems and improving a wide range of areas of our lives. Nevertheless, successful implementation requires overcoming existing difficulties and pushing technological innovation’s limits. By solving these problems and capitalising on artificial intelligence, the Internet of Things, and blockchain breakthroughs, we can navigate the autonomous car revolution and realise its full potential for a safer, more efficient, and sustainable future

Boundary Value Exploration for Software Analysis

For software to be reliable and resilient, it is widely accepted that tests must be created and maintained alongside the software itself. One safeguard from vulnerabilities and failures in code is to ensure correct behavior on the boundaries between sub-domains of the input space. So-called boundary value analysis (BVA) and boundary value testing (BVT) techniques aim to exercise those boundaries and increase test effectiveness. However, the concepts of BVA and BVT themselves are not clearly defined and it is not clear how to identify relevant sub-domains, and thus the boundaries delineating them, given a specification. This has limited adoption and hindered automation. We clarify BVA and BVT and introduce Boundary Value Exploration (BVE) to describe techniques that support them by helping to detect and identify boundary inputs. Additionally, we propose two concrete BVE techniques based on information-theoretic distance functions: (i) an algorithm for boundary detection and (ii) the usage of software visualization to explore the behavior of the software under test and identify its boundary behavior. As an initial evaluation, we apply these techniques on a much used and well-tested date handling library. Our results reveal questionable behavior at boundaries highlighted by our techniques. In conclusion, we argue that the boundary value exploration that our techniques enable is a step towards automated boundary value analysis and testing which can foster their wider use and improve test effectiveness and efficiency

A service-oriented approach to embedded component-based manufacturing automation

This thesis is focused on the application of Component-Based (CB) technology to shop oor devices using a Service Oriented Architecture (SOA) and Web Services (WS) for the purpose of realising future generation agile manufacturing systems. The environment of manufacturing enterprises is now characterised by frequently changing market demands, time-to-market pressure, continuously emerging new technologies and global competition. Under these circumstances, manufacturing systems need to be agile and automation systems need to support this agility. More speci cally, an open, exible automation environment with plug and play connectivity is needed. Technically, this requires the easy connectivity of hardware devices and software components from di erent vendors. Functionally, there is a need of interoperability and integration of control functions on di erent hierarchical levels ranging from eld level to various higher level applications such as process control and operations management services. [Continues.

Enhancing remanufacturing automation using deep learning approach

In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces.In recent years, remanufacturing has significant interest from researchers and practitioners to improve efficiency through maximum value recovery of products at end-of-life (EoL). It is a process of returning used products, known as EoL products, to as-new condition with matching or higher warranty than the new products. However, these remanufacturing processes are complex and time-consuming to implement manually, causing reduced productivity and posing dangers to personnel. These challenges require automating the various remanufacturing process stages to achieve higher throughput, reduced lead time, cost and environmental impact while maximising economic gains. Besides, as highlighted by various research groups, there is currently a shortage of adequate remanufacturing-specific technologies to achieve full automation. -- This research explores automating remanufacturing processes to improve competitiveness by analysing and developing deep learning-based models for automating different stages of the remanufacturing processes. Analysing deep learning algorithms represents a viable option to investigate and develop technologies with capabilities to overcome the outlined challenges. Deep learning involves using artificial neural networks to learn high-level abstractions in data. Deep learning (DL) models are inspired by human brains and have produced state-of-the-art results in pattern recognition, object detection and other applications. The research further investigates the empirical data of torque converter components recorded from a remanufacturing facility in Glasgow, UK, using the in-case and cross-case analysis to evaluate the remanufacturing inspection, sorting, and process control applications. -- Nevertheless, the developed algorithm helped capture, pre-process, train, deploy and evaluate the performance of the respective processes. The experimental evaluation of the in-case and cross-case analysis using model prediction accuracy, misclassification rate, and model loss highlights that the developed models achieved a high prediction accuracy of above 99.9% across the sorting, inspection and process control applications. Furthermore, a low model loss between 3x10-3 and 1.3x10-5 was obtained alongside a misclassification rate that lies between 0.01% to 0.08% across the three applications investigated, thereby highlighting the capability of the developed deep learning algorithms to perform the sorting, process control and inspection in remanufacturing. The results demonstrate the viability of adopting deep learning-based algorithms in automating remanufacturing processes, achieving safer and more efficient remanufacturing. -- Finally, this research is unique because it is the first to investigate using deep learning and qualitative torque-converter image data for modelling remanufacturing sorting, inspection and process control applications. It also delivers a custom computational model that has the potential to enhance remanufacturing automation when utilised. The findings and publications also benefit both academics and industrial practitioners. Furthermore, the model is easily adaptable to other remanufacturing applications with minor modifications to enhance process efficiency in today's workplaces

5G-PPP Technology Board:Delivery of 5G Services Indoors - the wireless wire challenge and solutions

The 5G Public Private Partnership (5G PPP) has focused its research and innovation activities mainly on outdoor use cases and supporting the user and its applications while on the move. However, many use cases inherently apply in indoor environments whereas their requirements are not always properly reflected by the requirements eminent for outdoor applications. The best example for indoor applications can be found is the Industry 4.0 vertical, in which most described use cases are occurring in a manufacturing hall. Other environments exhibit similar characteristics such as commercial spaces in offices, shopping malls and commercial buildings. We can find further similar environments in the media & entertainment sector, culture sector with museums and the transportation sector with metro tunnels. Finally in the residential space we can observe a strong trend for wireless connectivity of appliances and devices in the home. Some of these spaces are exhibiting very high requirements among others in terms of device density, high-accuracy localisation, reliability, latency, time sensitivity, coverage and service continuity. The delivery of 5G services to these spaces has to consider the specificities of the indoor environments, in which the radio propagation characteristics are different and in the case of deep indoor scenarios, external radio signals cannot penetrate building construction materials. Furthermore, these spaces are usually “polluted” by existing wireless technologies, causing a multitude of interreference issues with 5G radio technologies. Nevertheless, there exist cases in which the co-existence of 5G new radio and other radio technologies may be sensible, such as for offloading local traffic. In any case the deployment of networks indoors is advised to consider and be planned along existing infrastructure, like powerlines and available shafts for other utilities. Finally indoor environments expose administrative cross-domain issues, and in some cases so called non-public networks, foreseen by 3GPP, could be an attractive deployment model for the owner/tenant of a private space and for the mobile network operators serving the area. Technology-wise there exist a number of solutions for indoor RAN deployment, ranging from small cell architectures, optical wireless/visual light communication, and THz communication utilising reconfigurable intelligent surfaces. For service delivery the concept of multi-access edge computing is well tailored to host virtual network functions needed in the indoor environment, including but not limited to functions supporting localisation, security, load balancing, video optimisation and multi-source streaming. Measurements of key performance indicators in indoor environments indicate that with proper planning and consideration of the environment characteristics, available solutions can deliver on the expectations. Measurements have been conducted regarding throughput and reliability in the mmWave and optical wireless communication cases, electric and magnetic field measurements, round trip latency measurements, as well as high-accuracy positioning in laboratory environment. Overall, the results so far are encouraging and indicate that 5G and beyond networks must advance further in order to meet the demands of future emerging intelligent automation systems in the next 10 years. Highly advanced industrial environments present challenges for 5G specifications, spanning congestion, interference, security and safety concerns, high power consumption, restricted propagation and poor location accuracy within the radio and core backbone communication networks for the massive IoT use cases, especially inside buildings. 6G and beyond 5G deployments for industrial networks will be increasingly denser, heterogeneous and dynamic, posing stricter performance requirements on the network. The large volume of data generated by future connected devices will put a strain on networks. It is therefore fundamental to discriminate the value of information to maximize the utility for the end users with limited network resources