Smart manufacturing in the framework of space industry. An industry 4.0 approach to large scale production of satellite constellations

One of the major trend in the so-called New Space Economy or Space 4.0 paradigm has seen a number of new commercial players entering the satellite industry and creating completely new business models, most of which based on very large constellations consisting of several hundreds or even thousands of satellites. The production of the high number of satellites involved in the modern mega-constellations is bringing in the Space Industry the necessity of improved and optimized manufacturing approaches suitable for serial production (standard environment/high number of platforms). In this framework, the adoption of Industry 4.0 methodologies into space industry will lead to a significant improvement and optimization of the whole Manufacturing Assembly Integration and Testing (MAIT) cycle. The main aim of Industry 4.0 is the creation of intelligent factories where manufacturing technologies are upgraded and transformed by Cyber-Physical Systems (CPSs) the Internet of Things (IoT), cloud computing and big data analytics with predictive monitoring features such as the ones that characterize Smart Structures [4]. One main element of the Industry 4.0 approach is the synergic use of embedded production technologies with intelligent production processes with positive and important modifications of the industrial values chains, production value chains, and business models. In the present work, developed in the frame of project of European Space Agency, possible scenarios of applications of the Industry 4.0 concepts are presented and discussed in terms of applicability and advantages for Satellite Manufacturing [10,17]. Particular focus will be given to development of a CPS, by establishing a control network of sensors (e.g. temperature, location, load) over a target MAIT process

SMART MANUFACTURING IN THE SPACE INDUSTRY. A CYBER-PHYSICAL SYSTEM ARCHITECTURE AND ITS IMPLEMENTATION TO A MAIT PROCESS FOR MEGA CONSTELLATIONS OF SATELLITES

Industry 4.0, or Smart Manufacturing, is driving the evolution of industrial scenarios worldwide through an extensive introduction of Information Technologies as an integration to Operational Technologies. Access and use of data have become determinant factors of the efficacy of innovation requested to answer the increasingly urgent need of lowering production costs. Reviewing the State-of-Art of Smart Manufacturing technologies in the space industry and beyond, the authors found that the newest tools, such as Digital Twins, Internet of Things and Cyber-Physical Systems, have started to be applied, following the principles of interoperability, connectivity and modularity. However, still huge improvements are possible to optimize space productions to reach the ambitious efficiency goals of new commercial businesses, first of which the rising trend of mega constellations of small satellites. RUAG’s Space satellite composite sandwich panel Manufacturing Assembly Integration and Testing process was selected as reference. The as-is process data measurements and collection nowadays still rely on human workers’ long-term expertise, mainly related to visual inspection of defects or physical standard equipment. Data is stored in remote databases, not connected to the process nor easily available to the personnel. Moreover, the process is far from low-cost high-pace mass-market production systems, being materials and processes customized based on the product and heavily depending on procurement logics. Considerable effort has been made toward innovation, resulting for example in the automation of highly repetitive operations, such as insert potting, with the introduction of an Automated insert Potting Machine (APM). Taking as reference this operation to measure process KPIs’ improvement, a two-level approach was applied to implement a Cyber-Physical System to the process: first, using the existing measurement systems (e.g. sensors, devices and equipment like APM) to gather data in a digital database and then introducing new data by the addition of new sensing equipment. The CPS is based on the interconnection of sensors through a networking infrastructure managed by a central processing unit. Data flows from physical devices, digitally twinned, up to an operator dashboard, where the results of intermediate processing steps, including normalization, categorization, storage and interpretation by a closed-feedback loop logic based on KPIs’ statistical predictive models, are displayed. In this paper, the CPS conceptual and system architecture will be presented: data detection and measurement systems, techniques and strategies for data treatment and correlation, how decision making and process control is activated in the process, how hardware and software components are interrelated

An industry 4.0 approach to large scale production of satellite constellations. The case study of composite sandwich panel manufacturing

In recent years the so-called New Space Economy or Space 4.0 paradigm has seen a number of new commercial players entering the satellite industry and creating completely new business models, most of which based on very large constellations consisting of several hundreds or even thousands of satellites. The production of the high number of satellites involved in modern mega-constellations is bringing in the space industry the necessity of improved and optimized manufacturing approaches suitable for serial production, i.e., standard environment/high number of platforms. In this framework, the adoption of Industry 4.0 methodologies within the space industry will lead to a significant improvement and optimization of the whole Manufacturing Assembly Integration and Testing (MAIT) cycle. The main aim of Industry 4.0 is the creation of intelligent factories where manufacturing technologies are upgraded and transformed by Cyber-Physical Systems (CPSs), the Internet of Things (IoT), Cloud Computing and Big Data Analytics with predictive monitoring features. Main element of the Industry 4.0 approach is the synergic use of embedded sensing technologies in the frame of intelligent production processes, fostering a radical evolution of the industrial values chains, production value chains, and business models. In the present work, a possible application of the Industry 4.0 concepts to space industry is presented and discussed in terms of applicability and obtainable advantages. As a case study, the composite sandwich panel manufacturing line of RUAG Space is considered. Particular focus will be given to the development of a CPS, by establishing a control network of sensors (e.g. temperature, location, load) over a targeted MAIT process

NKp30 isoforms and NKp30 ligands are predictive biomarkers of response to imatinib mesylate in metastatic GIST patients.

Despite effective targeted therapy acting on KIT and PDGFRA tyrosine kinases, gastrointestinal stromal tumors (GIST) escape treatment by acquiring mutations conveying resistance to imatinib mesylate (IM). Following the identification of NKp30-based immunosurveillance of GIST and the off-target effects of IM on NK cell functions, we investigated the predictive value of NKp30 isoforms and NKp30 soluble ligands in blood for the clinical response to IM. The relative expression and the proportions of NKp30 isoforms markedly impacted both event-free and overall survival, in two independent cohorts of metastatic GIST. Phenotypes based on disbalanced NKp30B/NKp30C ratio (ΔBC(low)) and low expression levels of NKp30A were identified in one third of patients with dismal prognosis across molecular subtypes. This ΔBC(low) blood phenotype was associated with a pro-inflammatory and immunosuppressive tumor microenvironment. In addition, detectable levels of the NKp30 ligand sB7-H6 predicted a worse prognosis in metastatic GIST. Soluble BAG6, an alternate ligand for NKp30 was associated with low NKp30 transcription and had additional predictive value in GIST patients with high NKp30 expression. Such GIST microenvironments could be rescued by therapy based on rIFN-α and anti-TRAIL mAb which reinstated innate immunity