A framework concept for data visualization and structuring in a complex production process

This paper provides a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives. The concept is tested and validated on a smaller simulation of a paint booth with several interconnected and supporting control systems to prove the functionality and usefulness in this kind of production system.The paper presents the resulting five abstraction levels in the framework concept, from a production top view down to the signal exchange between the different resources in one production cell, together with additional features. The simulation proves the setup with Sequence Planner and the visual interface to work by extract and present process data from a running sequence

Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge

NASA SpaceCube Next-Generation Artificial-Intelligence Computing for STP-H9-SCENIC on ISS

Recently, Artificial Intelligence (AI) and Machine Learning (ML) capabilities have seen an exponential increase in interest from academia and industry that can be a disruptive, transformative development for future missions. Specifically, AI/ML concepts for edge computing can be integrated into future missions for autonomous operation, constellation missions, and onboard data analysis. However, using commercial AI software frameworks onboard spacecraft is challenging because traditional radiation-hardened processors and common spacecraft processors cannot provide the necessary onboard processing capability to effectively deploy complex AI models. Advantageously, embedded AI microchips being developed for the mobile market demonstrate remarkable capability and follow similar size, weight, and power constraints that could be imposed on a space-based system. Unfortunately, many of these devices have not been qualified for use in space. Therefore, Space Test Program - Houston 9 - SpaceCube Edge-Node Intelligent Collaboration (STP-H9-SCENIC) will demonstrate inflight, cutting-edge AI applications on multiple space-based devices for next-generation onboard intelligence. SCENIC will characterize several embedded AI devices in a relevant space environment and will provide NASA and DoD with flight heritage data and lessons learned for developers seeking to enable AI/ML on future missions. Finally, SCENIC also includes new CubeSat form-factor GPS and SDR cards for guidance and navigation

All-electric LNG a viable alternative to conventional gas turbine driven LNG plant

The world demand for natural gas which is at an increasing trend has rekindled interest in the production and transportation of Liquefied Natural Gas (LNG) from resource rich areas in Africa, Middle East, Far East, Australia and Russia to customers in Europe, Americas, China and India. The challenges for the future are to produce and transport gas in a cost effective manner to be competitive in the market place. Gas is beginning to play an increasingly important role in energy scenario of the world economy. The easiest ways of getting gas to the market is by pipe lines. However to reach markets far and wide across oceans, gas needs to be converted and transported in liquid form. Competitive pressure and search for economies of scale is driving up the size of LNG facilities and hence the capital requirement of each link of the value chain. Interdependent financing of the various links of the value chain, while maintaining their economic viability, is the challenge that sponsors need to address. The industry is potentially a high risk business due to uncertainty associated with the characteristics of the industry, which calls for high level of investment in an environment of volatility of the price and political and economic changes in the world market. LNG production facilities are becoming larger and larger than ever before to take advantage of economies of scale. These massive plants not only have created new challenges in design, procurement and construction and environment but will create new challenges in operation and maintenance. Innovative technologies and first of a kind equipment applications with a rigorous technology development and a stringent testing plans ensure that the facility will achieve a long term reliable operation. Conventional LNG plants use Gas Turbine as main drivers for refrigerant compressors. To this effect All-Electric LNG has a potential to provide an alternative offer a life cycle advantage over the convention. Hence it would be worthwhile to study the pros and cons and prospects offered by this new technology from an overall life cycle perspective for future of LNG projects. This research is an endeavours in this direction

Annals of Scientific Society for Assembly, Handling and Industrial Robotics

This Open Access proceedings present a good overview of the current research landscape of industrial robots. The objective of MHI Colloquium is a successful networking at academic and management level. Thereby the colloquium is focussing on a high level academic exchange to distribute the obtained research results, determine synergetic effects and trends, connect the actors personally and in conclusion strengthen the research field as well as the MHI community. Additionally there is the possibility to become acquainted with the organizing institute. Primary audience are members of the scientific association for assembly, handling and industrial robots (WG MHI)

Q'@gile: quantum agile manufacture of internal combustion engines

Current trends in the automotive industry towards fuel efficient and low emission vehicles, are dictated by more environmental friendly customers, more strict environmental legislation, rising fuel costs and intensive competition. These factors are pressuring vehicle manufacturers to speed up R&D and improve the efficiency and flexibility of their manufacturing operations so that improved products can be introduced over shortened timeframes. Recent advances have been focused on improving the design of internal combustion engines, coupled with research into alternative fuels and related new forms of vehicle propulsion. Natural impacts of these advances have been shorter engine lifetimes, increased pace of engine innovations, and significant changes in propulsion type share: in Europe the diesel engine share is increasing relative to that of petrol engines while in the US and Japan hybrids are becoming popular. In the long-term fuel cell- and hydrogen-fuelled vehicles may largely make internal combustion engines obsolete. [Continues.