Requirements for building information modeling based lean production management systems for construction

Smooth flow of production in construction is hampered by disparity between individual trade teams' goals and the goals of stable production flow for the project as a whole. This is exacerbated by the difficulty of visualizing the flow of work in a construction project. While the addresses some of the issues in Building information modeling provides a powerful platform for visualizing work flow in control systems that also enable pull flow and deeper collaboration between teams on and off site. The requirements for implementation of a BIM-enabled pull flow construction management software system based on the Last Planner System™, called ‘KanBIM’, have been specified, and a set of functional mock-ups of the proposed system has been implemented and evaluated in a series of three focus group workshops. The requirements cover the areas of maintenance of work flow stability, enabling negotiation and commitment between teams, lean production planning with sophisticated pull flow control, and effective communication and visualization of flow. The evaluation results show that the system holds the potential to improve work flow and reduce waste by providing both process and product visualization at the work face

Special Session on Industry 4.0

No abstract available

A framework to design smart manufacturing systems for Industry 5.0 based on the human-automation symbiosis

The concept of Industry 5.0 (I5.0) promotes the human-centricity as the core value behind the evolution of smart manufacturing systems (SMSs), based on a novel use of digital technologies in the design and management of modern industrial systems to take up the socio-technical challenges. In this context, the paper proposes a Smart Manufacturing Systems Design (SMSD) framework enabling I5.0, based on the human-automation symbiosis. Thanks to an 'Augmented Digital Twin' (ADT) able to integrate and digitize all the entities of the factory (i.e. machines, robots, environments, interfaces, people), AI-driven applications can be built to support the user domain and make people and machines co-evolve thanks to a systematic data sharing between physical and digital assets (e.g. digital twin, virtual mock-ups, human-machine interfaces), optimizing factory productivity and workers wellbeing. In this framework, machines and humans can both generate knowledge and learn from each other, generating a virtuous co-evolution, supporting the understanding of the human-machine interplay and the creation of an effective collaboration between people and SMSs. The framework was conceived and validated involving four industrial companies, belonging to diverse sectors, interested in overcoming the current limits of I4.0 lines by including the human factors for future SMS management

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Engage D1.2 Final Project Results Report

This deliverable summarises the activities and results of Engage, the SESAR 2020 Knowledge Transfer Network (KTN). The KTN initiated and supported multiple activities for SESAR and the European air traffic management (ATM) community, including PhDs, focused catalyst fund projects, thematic workshops, summer schools and the launch of a wiki as the one-stop, go-to source for ATM research and knowledge in Europe. Key throughout was the integration of exploratory and industrial research, thus expediting the innovation pipeline and bringing researchers together. These activities laid valuable foundations for the SESAR Digital Academy

Analysis and evaluation of multi-agent systems for digital production planning and control

Industrial manufacturing companies have different IT control functions that can be represented with a so-called hierarchical automation pyramid. While these conventional software systems especially support the mass production with consistent demand, the future project “Industry 4.0” focuses on customer-oriented and adaptable production processes. In order to move from conventional production systems to a factory of the future, the control levels must be redistributed. With the help of cyber-physical production systems, an interoperable architecture must be, implemented which removes the hierarchical connection of the former control levels. The accompanied digitalisation of industrial companies makes the transition to modular production possible. At the same time, the requirements for production planning and control are increasing, which can be solved with approaches such as multi-agent systems (MASs). These software solutions are autonomous and intelligent objects with a distinct collaborative ability. There are different modelling methods, communication and interaction structures, as well as different development frameworks for these new systems. Since multi-agent systems have not yet been established as an industrial standard due to their high complexity, they are usually only tested in simulations. In this bachelor thesis, a detailed literature review on the topic of MASs in the field of production planning and control is presented. In addition, selected multi-agent approaches are evaluated and compared using specific classification criteria. In addition, the applicability of using these systems in digital and modular production is assessed

Artificial Intelligence as an Enabler of Quick and Effective Production Repurposing Manufactur-ing: An Exploratory Review and Future Research Propositions

The outbreak of Covid-19 created disruptions in manufacturing operations. One of the most serious negative impacts is the shortage of critical medical supplies. Manufacturing firms faced pressure from governments to use their manufacturing capacity to repurpose their production for meeting the critical demand for necessary products. For this purpose, recent advancements in technology and artificial intelligence (AI) could act as response solutions to conquer the threats linked with repurposing manufacturing (RM). The study’s purpose is to investigate the significance of AI in RM through a systematic literature review (SLR). This study gathered around 453 articles from the SCOPUS database in the selected research field. Structural Topic Modeling (STM) was utilized to generate emerging research themes from the selected documents on AI in RM. In addition, to study the research trends in the field of AI in RM, a bibliometric analysis was undertaken using the R-package. The findings of the study showed that there is a vast scope for research in this area as the yearly global production of articles in this field is limited. However, it is an evolving field and many research collaborations were identified. The study proposes a comprehensive research framework and propositions for future research development