Development of New Model-based Methods in ASIC Requirements Engineering

Requirements in the development of application-specific integrated circuits (ASICs) continue to increase. This leads to more complexities in handling and processing the requirements, which often causes inconsistencies in the requirments. To better manage the resulting complexities, ASIC development is evolving into a model-based process. This thesis is part of a continuing research into the application and evolution of a model-based process for ASIC development at the Robert Bosch GmbH. It focuses on providing methologies that enable tracing of ASIC requirements and specifications as part of a model-based development process to eliminate inconsistencies in the requirements. The question of what requirements are and, what their traceability means, is defined and analysed in the context of their relationships to models. This thesis applies requirements engineering (RE) practices to the processing of ASIC requirements in a development environment. This environment is defined by availability of tools which are compliant with some standards and technologies. Relying on semi-formal interviews to understand the process in this environment and what stakeholders expect, this thesis applies the standards and technologies with which these tools are compliant to provide methodologies that ensures requirements traceability. Effective traceability methods were proven to be matrices and tables, but for cases of fewer requirements (ten or below), requirement diagrams are also efficient and effective. Furthermore, the development process as a collaborative effort was shown to be enhanced by using the resulting tool-chain, when the defined methodologies are properly followed. This solution was tested on an ASIC concept development project as a case study

Exploiting building information modeling throughout the whole lifecycle of construction projects

Over the past few years, construction industry has encountered numerous problems such as rework, design errors, accidents and building failure, time and economic losses, poor work efficiency, and low standard level of cooperation amongst team members of different sectors. As such, information communication technology (ICT) has been evolved to minimize all the aforementioned setbacks in the construction industry. In doing so, building information modeling (BIM) has been proposed to all construction members such as engineers, architects, contractors, and owners to take benefit from. Since BIM was emerged into the construction industry, it has received the attention of many researchers and practitioners. While there have been roughly numerous studies conducted on the benefits involved in the use of BIM, it is a unresolved point why there has not been a greater take up of exploiting BIM throughout the whole lifecycle of construction projects. Therefore, this paper is mainly aimed to examine the effectiveness of exploiting BIM throughout the three different phases of building’s lifecycle, including preconstruction, construction, and post construction in great details regarding the previous studies conducted in this field. The authors have concluded that utilization of BIM has several benefits in different stages of construction projects, including minimizing design error, reducing rework, increasing work efficiency and cooperation amongst team members, facilitating the process of delivery and procurement, and reusing the wastages of materials

Circular Production and Maintenance of Automotive Parts:An Internet of Things (IoT) Data Framework and Practice Review

The adoption of the Circular Economy paradigm by industry leads to increased responsibility of manufacturing to ensure a holistic awareness of the environmental impact of its operations. In mitigating negative effects in the environment, current maintenance practice must be considered for its potential contribution to a more sustainable lifecycle for the manufacturing operation, its products and related services. Focusing on the matching of digital technologies to maintenance practice in the automotive sector, this paper outlines a framework for organisations pursuing the integration of environmentally aware solutions in their production systems. This research sets out an agenda and framework for digital maintenance practice within the Circular Economy and the utilisation of Industry 4.0 technologies for this purpose

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

Context-aware Process Management for the Software Engineering Domain

Historically, software development projects are challenged with problems concerning budgets, deadlines and the quality of the produced software. Such problems have various causes like the high number of unplanned activities and the operational dynamics present in this domain. Most activities are knowledge-intensive and require collaboration of various actors. Additionally, the produced software is intangible and therefore difficult to measure. Thus, software producers are often insufficiently aware of the state of their source code, while suitable software quality measures are often applied too late in the project lifecycle, if at all. Software development processes are used by the majority of software companies to ensure the quality and reproducibility of their development endeavors. Typically, these processes are abstractly defined utilizing process models. However, they still need to be interpreted by individuals and be manually executed, resulting in governance and compliance issues. The environment is sufficiently dynamic that unforeseen situations can occur due to various events, leading to potential aberrations and process governance issues. Furthermore, as process models are implemented manually without automation support, they impose additional work for the executing humans. Their advantages often remain hidden as aligning the planned process with reality is cumbersome. In response to these problems, this thesis contributes the Context-aware Process Management (CPM) framework. The latter enables holistic and automated support for software engineering projects and their processes. In particular, it provides concepts for extending process management technology to support software engineering process models in their entirety. Furthermore, CPM contributes an approach to integrate the enactment of the process models better with the real-world process by introducing a set of contextual extensions. Various events occurring in the course of the projects can be utilized to improve process support and activities outside the realm of the process models can be covered. That way, the continuously growing divide between the plan and reality that often occurs in software engineering projects can be avoided. Finally, the CPM framework comprises facilities to better connect the software engineering process with other important aspects and areas of software engineering projects. This includes automated process-oriented support for software quality management or software engineering knowledge management. The CPM framework has been validated by a prototypical implementation, various sophisticated scenarios, and its practical application at two software companies

D8.6 OPTIMAI commercialization and exploitation strategy

Deliverable D8.6 OPTIMAI commercialization and exploitation strategy 1 st version is the first version of the OPTIMAI Exploitation Plan. Exploitation aims at ensuring that OPTIMAI becomes sustainable well after the conclusion of the research project period so as to create impact. OPTIMAI intends to develop an industry environment that will optimize production, reducing production line scrap and production time, as well as improving the quality of the products through the use of a variety of technological solutions, such as Smart Instrumentation of sensors network at the shop floor, Metrology, Artificial Intelligence (AI), Digital Twins, Blockchain, and Decision Support via Augmented Reality (AR) interfaces. The innovative aspects: Decision Support Framework for Timely Notifications, Secure and adaptive multi-sensorial network and fog computing framework, Blockchain-enabled ecosystem for securing data exchange, Intelligent Marketplace for AI sharing and scrap re-use, Digital Twin for Simulation and Forecasting, Embedded Cybersecurity for IoT services, On-the-fly reconfiguration of production equipment allows businesses to reconsider quality management to eliminate faults, increase productivity, and reduce scrap. The OPTIMAI exploitation strategy has been drafted and it consists of three phases: Initial Phase, Mid Phase and Final Phase where different activities are carried out. The aim of the Initial phase (M1 to M12), reported in this deliverable, is to have an initial results' definition for OPTIMAI and the setup of the structures to be used during the project lifecycle. In this phase, also each partner's Individual Exploitation commitments and intentions are drafted, and a first analysis of the joint exploitation strategies is being presented. The next steps, leveraging on the outcomes of the preliminary market analysis, will be to update the Key Exploitable Results with a focus on their market value and business potential and to consolidate the IPR Assessment and set up a concrete Exploitation Plan. The result of the next period of activities will be reported in D8.7 OPTIMAI commercialization and exploitation strategy - 2nd version due at month 18 (June 2022

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