Cloud-based Control of Thermal Based Manufacturing Processes

AbstractWith non-conventional manufacturing processes, being increasingly integrated into manufacturing process chains, controllers and control strategies, remote nowadays, have to take into account a plethora of phenomena and criteria. The current study addresses the challenges, associated with the framework of the thermal oriented processes, having holistic (digital) modelling as a main objective. Herein two different case studies are performed; numerical examples regarding big data impact on manufacturing and simulation-based paradigms of control design taking into account communications. Implementation of the aforementioned takes into account the controller's complexity

Manufacturing Resilience during the Coronavirus Pandemic: On the investigation Manufacturing Processes Agility

The unprecedented events that worldwide population experienced during year 2020 due to the COVID-19 pandemic, resulted in the formation of numerous challenges across the majority of aspects of every day life. Manufacturing industries and supply chain networks faced a unique decostruction during this period due to restrictions created by global or local lockdowns. Reduction of human resources availability and transportation restrictions linked with the extreme and rapid increase of demand for medical supplies led manufactring related activities to reach their limits. Moreover, the non flexible manufacturing methods that are employed for the production of this type of equipment as well as the delayed delivery of products that are used as raw material at the early production stages, resulted in market shortage of medical supplies while demand constantly growing. Additive Manufacturing was used for the immediate production of plastic/metal medical equipment, taking advantage of reverse engineering, process flexibility and commercialization of printing devices. Small and large enteprises in cooperation with individuals have been organized into Local Hubs in order to reduce the transportation time and accelerate the supply of the required equipment at local hopitals until the the non flexible production lines manage to produce the desired production volume under the required production rate. This study aims to identify reasons why traditional manufacturing facilies faced such difficulties in the production of medical equipment and to propose a framework where Additive Manufacturing methods can be used for the immediate, local and low volume production of the desired product, giving time to non easily adjustable industries to initiate the mass production

Cloud-Based Architecture for Production Information Exchange in European Micro-Factory Context

In a constantly changing world, information stands as one of the most valuable assets for a manufacturing site. However, exchanging information is not a straightforward process among factories, and concerns regarding the trustability and validation of transactions between various stakeholders have emerged within the context of micro-factories. This work presents an architecture designed to enable information exchange among heterogeneous stakeholders, taking advantage of the cloud infrastructure. It was designed to enable the use of several tools, connected through a middleware system deployed on the cloud. To demonstrate the potential of this architecture, a platform was instantiated, and two use cases—designed to accurately represent real manufacturing sites—were implemented.© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Deep Quality Assessment of a Solar Reflector Based on Synthetic Data: Detecting Surficial Defects from Manufacturing and Use Phase

Vision technologies are used in both industrial and smart city applications in order to provide advanced value products due to embedded self-monitoring and assessment services. In addition, for the full utilization of the obtained data, deep learning is now suggested for use. To this end, the current work presents the implementation of image recognition techniques alongside the original the quality assessment of a Parabolic Trough Collector (PTC) reflector surface to locate and identify surface irregularities by classifying images as either acceptable or non-acceptable. The method consists of a three-step solution that promotes an affordable implementation in a relatively small time period. More specifically, a 3D Computer Aided Design (CAD) of the PTC was used for the pre-training of neural networks, while an aluminum reflector surface was used to verify algorithm performance. The results are promising, as this method proved applicable in cases where the actual part was manufactured in small batches or under the concept of customized manufacturing. Consequently, the algorithm is capable of being trained with a limited number of data

Systematic Design Applied in Outdoor Spatiotemporal Lighting

Outdoor lighting design consists of many different objectives, depending on the area that is illuminated. In addition, besides functional lighting, extra messages may be superimposed to a lighting configuration. This adds to the complexity of the lighting design. Furthermore, temporal variations in lighting may be used as an additional tool of expressivity. All the above require some basic training and also some familiarity with expression tools. In this work, a framework is given for seamless communication through lighting, including both spatial and temporal lighting patterns. To this end, two different kinds of time scales are considered, leading to case studies for both seasonal lighting and communication through rapid spatiotemporal differentiations in it. The framework is two-fold, allowing for both diagrammatic and quasi-algebraic elaboration, leading to interesting visual results and providing the first step towards optimization. Different cases of outdoor lighting are considered as case studies, namely façade lighting and glass cases. These are used to illustrate the applicability and the added value of the current framework, that is, the systematization of the lighting procedure taking into account artistic interventions, which can be considered an extension of utilizing semantics

Αριθμητική μελέτη της διάδοσης ελαστικών κυμάτων σε υλικά με επίδραση μικροδομής: εφαρμογή σε οστά

Fractures are common at human bones. So, a callus is formed and the procedureof osteogenesis is initiated. Medical doctors need to have a tool that allowsthem to evaluate the healing procedure without taking X-ray photos every week.Such a variety of tools can be provided by non-destructive inspection techniques.But rst, one has to create a model for predicting phenomena such as size-eectsand in particular dispersive acoustic waves propagation.Before this thesis, there has been made an attempt by (Vavva, 2009), topredict modal wave propagation with Mindlin's Form-II. Herein, for the rsttime there are presented dynamic solutions of this theory.To begin with, the bone is considered to be a dampless homogeneous (ortho)isotropic composite material, with interstitial tissue being the matrix andthe osteons being the bres. So, Mindlin's theory can be applied in this case.Next, a fundamental solution is obtained for Mindlin's Form-II of his gradientelasticity theory. In conjunction to an existing integral representation, there canbe obtained solutions using the Boundary Element Method. With the help of aconsidered Representative Volume Element, simulations have been conducted andresults are presented for the cases of P, S and Rayleigh waves, as well as guidedwaves in plates. The dispersion diagrams as given by Wigner-Ville representationsare compared to the theoretical ones. What is more, the validity and accuracy ofthe BEM code have been checked using analytical solutions of one-dimensionalproblems.Furthermore, relaxation functions from viscoelastic theories are consideredand are taken into account using the correspondence principle. So, both viscoelasticand gradient-visco-elastic models have been considered and the resultsof various cases (P, S, Rayleigh and Lamb waves) have been compared to theabove.Finally, since the present thesis has to do with information extracted fromdispersive wave propagation, some studies have been made and measures havebeen proposed for velocities and dispersion.All in all, this has been a work dealing with the fact that micro-structureaects the macro-behavior of a material concerning waves propagation and, inthe framework of Mindlin's Form-II, there have been extracted several conclusionsconcerning bone-like materials

A CPS platform oriented for Quality Assessment in welding

The major advantages of spot and seam welding are high speed and adaptability primarily for high-volume and/or high-rate manufacturing. However, this paradigm fails to meet the principles laid down by Industry 4.0 for real-time control towards Zero Defect Manufacturing for each individual product and intuitive technical assistance on the process parameters. In this paper, a Robust Software Platform oriented for a CPS-based Quality Assessment system for Welding is presented based on data derived from IR cameras. Imaging data are pre – processed in real-time and streamed into a module which utilizes Machine Learning algorithms to perform quality assessment. A database enables data archiving and post processing tasks along with an intuitive User Interface which provide visualization capabilities and Decision Support on the welding process parameters. The modules’ IoT-based communication is performed with 5C architecture and is in line with Web Services

Robust and Secure Quality Monitoring for Welding through Platform-as-a-Service: A Resistance and Submerged Arc Welding Study

For smart manufacturing systems, quality monitoring of welding has already started to shift from empirical modeling to knowledge integration directly from the captured data by utilizing one of the most promising Industry 4.0’s key enabling technologies, artificial intelligence (AI)/machine learning (ML). However, beyond the advantages that they bring, AI/ML introduces new types of security threats, which are related to their very nature and eventually, they will pose real threats to the production cost and quality of products. These types of security threats, such as adversarial attacks, are causing the targeted AI system to produce incorrect or malicious outputs. This may undermine the performance (and the efficiency) of the quality monitoring systems. Herein, a software platform servicing quality monitoring for welding is presented in the context of resistance and submerged arc welding. The hosted ML classification models that are trained to perform quality monitoring are subjected to two different types of untargeted, black-box, adversarial attacks. The first one is based on a purely statistical approach and the second one is based on process knowledge for crafting these adversarial inputs that can compromise the models’ accuracy. Finally, the mechanisms upon which these adversarial attacks are inflicting damage are discussed to identify which process features or defects are replicated. This way, a roadmap is sketched toward testing the vulnerability and robustness of an AI-based quality monitoring system