Open Interfaces for Connecting Automated Guided Vehicles to a Fleet Management System

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Shape Memory Composites for Self-deployable Structures in Aerospace Applications

AbstractShape memory composites (SMCs) are attractive materials as they combine typical mechanical and functional properties of composites with shape memory properties. Such properties can be given to composite materials and structures by using shape memory polymer (SMP) matrices or integrating parts made of SMPs. In the case of integration, flexible composite skins can be applied over a shape memory foam core obtaining composite sandwich that can be shaped to change its stiffness or to reduce its volume. After the application of a given stimulus (generally by heating) the initial shape can be recovered. Future applications for this class of materials are self-deployable structures for space systems (such as actuators of solar sails or smart aerodynamic structures). In this work, two new SMC self-deployable structures were prototyped: a composite cross and a composite frame containing a thin aluminum sheet. The former structure represents a possible deploying configuration for a structural sheet whereas the latter is a conceptual study of a solar sail. The experimental results are very promising, showing that such structures can successfully self-deploy following the desired design constraints without noticeable damages. Finally, new perspectives for applications are highlighte

Asset Administration Shell as an interoperable enabler of Industry 4.0 software architectures: a case study

In recent years, the discipline of Digital Transformation in manufacturing companies turned out to be a hot topic of research debate, which allowed the design and introduction of new technologies and tools able to exploit the potential of the data produced by the shop floor assets. This increased interest in data generation and management has however highlighted a crucial issue about the lack of standardised models and structures to share these data and ensure interoperability. Among the several concepts proposed by the recent initiatives devoted to solving or mitigating this issue, Asset Administration Shell (AAS) is increasing in popularity, given its potential in providing standardised and modular information about the assets and events represented. This paper deals with a demonstration of the easiness of integration of AAS in pre-existing software architecture, allowing higher flexibility and a better understanding of the ongoing processes: a production line has been indeed entirely represented with modular AAS metamodels and it has been used to feed a Digital Model representing the line configuration. The use case proposed proves the effectiveness of the obtained solution when used for virtual commissioning operations

An Edge-Cloud based Reference Architecture to support cognitive solutions in Process Industry

Process Industry is one of the leading sectors of the world economy, characterized however by intense environmental impact, and very high-energy consumption. Despite a traditional low innovation pace in PI, in the recent years a strong push at worldwide level towards the dual objective of improving the efficiency of plants and the quality of products, significantly reducing the consumption of electricity and CO2 emissions has taken momentum. Digital Technologies (namely Smart Embedded Systems, IoT, Data, AI and Edge-to-Cloud Technologies) are enabling drivers for a Twin Digital-Green Transition, as well as foundations for human centric, safe, comfortable and inclusive workplaces. Currently, digital sensors in plants produce a large amount of data, which in most cases constitutes just a potential and not a real value for Process Industry, often locked-in in close proprietary systems and seldomly exploited. Digital technologies, with process modelling-simulation via digital twins, can build a bridge between the physical and the virtual worlds, bringing innovation with great efficiency and drastic reduction of waste. In accordance with the guidelines of Industrie 4.0 this work proposes a modular and scalable Reference Architecture, based on open source software, which can be implemented both in brownfield and greenfield scenarios. The ability to distribute processing between the edge, where the data have been created, and the cloud, where the greatest computational resources are available, facilitates the development of integrated digital solutions with cognitive capabilities. The reference architecture is being validated in the three pilot plants, paving the way to the development of integrated planning solutions, with scheduling and control of the plants, optimizing the efficiency and reliability of the supply chain, and balancing energy efficiency

Architecture for Data Acquisition in Research and Teaching Laboratories

In the recent years, several research activities focused on the design, development and deployment of software architectures addressed to the monitoring, management and control of industrial plants, since the ongoing digitalisation of manufacturing companies demanded guidelines and cutting-edge solutions to the research community. In this context, more and more research centres’ laboratories have been hence asked to run in parallel different architectures, relying on different research activities, whose feeds consist of the same signals from the machine pool. When these laboratories belong to universities, the request for data from students and trainees is added to the request for data from research activities. this often results in a network congestion with severe implications on the data integrity. For this reason, the authors have designed and implemented an open modular software architecture able to minimise the requests to the machines and to embody Service Oriented Architecture functionalities. The architecture has been named SHIELD, indeed SHIELD Has Integrated Existing Laboratory Data

Fostering Circular Manufacturing Through the Integration of Genetic Algorithm and Process Mining

Recently, the increasing lack of raw materials is forcing the manufacturing sector in revising the internal operating and strategic activities to embrace Circular Economy (CE) principles thus, moving towards Circular Manufacturing (CM). CE principles are pursued during product design, product realisation, as well as product end-of-life. As an enabler of end-of-life CM strategies, disassembling represents the cornerstone to facilitate other ones to take place, as remanufacturing and recycling. Indeed, nowadays, empowering companies in the disassembling process by maintaining high their environmental sustainability performances is essential. Indeed, identifying the best disassembly sequence that is also energy-effective is an open challenge to guarantee a 360° application of CM strategies. Therefore, the objective of this contribution is to develop a framework able to automatically reconstruct the disassembly sequence while minimising the energy consumption. The solution is based on process mining technique, which aims at representing the original process, and genetic algorithm, which is instead in charge of identifying the solution with minimal energy consumption. Once the framework has been developed, its feasibility has been tested first at laboratory scale and then through a simulated case. The proposed framework represents a Proof of Concept that aims at promoting the pursue of CM strategies in the product end-of-life by facilitating the identification of the disassembly sequence which is also energy-effective

Computer Vision Technology for Robotized Wire Harness Assembly

Wire harnesses are essential hardware for electronic systems in modern automotive vehicles. With a shift in the automotive industry towards electrification and autonomous driving, more and more automotive electronics are responsible for energy transmission and safety-critical functions such as maneuvering, driver assistance, and safety system. This paradigm shift places more demand on automotive wiring harnesses from the safety perspective and stresses the greater importance of high-quality wire harness assembly in vehicles. However, most of the current operations of wire harness assembly are still performed manually by skilled workers, and some of the manual processes are problematic from different perspectives, such as quality control and ergonomics. There is also a persistent demand in the industry to increase competitiveness and gain market share. Hence, assuring assembly quality while improving ergonomics and optimizing labor costs is desired. Robotized assembly, accomplished by robots or in human-robot collaboration, is a key enabler for fulfilling the increasingly demanding quality and safety as it enables more replicable, transparent, and comprehensible processes than completely manual operations. However, robotized assembly of wire harnesses is challenging in real environments due to the flexibility of the deformable objects, though many preliminary automation solutions have been proposed under simplified industrial configurations. Previous research efforts have proposed the use of computer vision technology to facilitate robotized automation of wire harness assembly, enabling the robots to better perceive and manipulate the flexible wire harness. This article presents an overview on computer vision technology proposed for robotized wire harness assembly and derives research gaps that require further study to facilitate a more practical robotized assembly of wire harness.Comment: This paper has been accepted by CIRP CMS 2023. The information of the published version will be updated late

A Systematic Literature Review of Computer Vision Applications in Robotized Wire Harness Assembly

This article presents a systematic literature review on computer vision applications that have been proposed for robotized wire harness assembly, derives challenges from existing studies, and identifies opportunities for future research to promote a more practical robotized assembly of wire harnesses

Smart Factory Security: A Case Study on a Modular Smart Manufacturing System

Abstract Smart manufacturing systems are an attractive target for cyber attacks, because they embed valuable data and critical equipment. Despite the market is driving towards integrated and interconnected factories, current smart manufacturing systems are still designed under the assumption that they will stay isolated from the corporate network and the outside world. This choice may result in an internal architecture with insufficient network and system compartmentalization. As a result, once an attacker has gained access, they have full control of the entire production plant because of the lack of network segmentation. With the goal of raising cybersecurity awareness, in this paper we describe a practical case study showing attack scenarios that we have validated on a real modular smart manufacturing system, and suggest practical security counter measures. The testbed smart manufacturing system is part of the Industry 4.0 research laboratory hosted by Politecnico di Milano, and comprises seven assembly stations, each with their programmable logic controllers and human-computer interfaces, as well as an industrial robotic arm that performs pick-and-place tasks. On this testbed we show two indirect attacks to gain initial access, even under the best-case scenario of a system not directly connected to any public network. We conclude by showing two post-exploitation scenarios that an adversary can use to cause physical impact on the production, or keep persistent access to the plant. We are unaware of a similar security analysis performed within the premises of a research facility, following a scientific methodology, so we believe that this work can represent a good first step to inspire follow up research on the many verticals that we touch

Revision of the Learning Factory Morphology

Since its first publication in 2015, the learning factory morphology has been frequently used to design new learning factories and to classify existing ones. The structuring supports the concretization of ideas and promotes exchange between stakeholders. However, since the implementation of the first learning factories, the learning factory concept has constantly evolved. Therefore, in the Working Group "Learning Factory Design" of the International Association of Learning Factories, the existing morphology has been revised and extended based on an analysis of the trends observed in the evolution of learning factory concepts. On the one hand, new design elements were complemented to the previous seven design dimensions, and on the other hand, new design dimensions were added. The revised version of the morphology thus provides even more targeted support in the design of new learning factories in the future