1,720,987 research outputs found
A Neural Network-Based Approach to Estimate Printing Time and Cost in L-PBF Projects
No full textAdditive manufacturing is one of the foundational pillars of Industry 4.0, which is rooted in the integration of intelligent digital technologies, manufacturing, and industrial processes. Machine learning techniques are resources used to support Design for Additive Manufacturing, particularly in design phases and process analysis. Neural Networks are suited to manage complex and non-linear datasets. The article proposes a methodology for the time and cost assessment of the Laser-Powder Bed Fusion 3D printing process using a Neural Network-based approach. The methodology analyzes the main geometrical features of STL files to train Neural Network Machine Learning models. The methodology has been tested on a preliminary dataset that includes a set of parametric CAD models and their corresponding Additive Manufacturing simulations. The trained models achieve an R2 value greater than 0.97. A web-service platform has been implemented to provide a valuable tool for users, transforming a research-grade model into a production-grade online endpoint
A parametric optimization approach of an induction heating system for energy consumption reduction
No full textNowadays, electromagnetic high-frequency induction is very used for different non-contact heating applications such as the molding process. Every molding process requires the preheating and the thermal maintenance of the molds, to enhance the filling phase and the quality of the final products. In this context, an induction heating system, mostly, is a customized equipment. The design and definition of an induction equipment depends on the target application. This technology is highly efficient and performant, however it provides a high-energy consumption. Therefore, optimization strategies are very suitable to reduce energy cost and consumption. The proposed paper aims to define a method to optimize the induction heating of a mold in terms of time, consumption, and achieved temperature. The proposed optimization method involves genetic algorithms to define the design parameters related to geometry and controller. A test case describes the design of an induction heating system for a polyurethane molding process, which is the soles foaming. This case study deals with the multiobjective optimization of parameters such as the geometrical dimensions, the inductor sizing, and the controller setting. The multi-objective optimization aims to reduce the energy consumption and to increase the wall temperature of the mold
Energy Saving in Industrial Wireless Power Recharge System: Simulation of a PI-Sliding Mode Control for a Non-Inverting Buck-Boost Converter
No full textIn this work, a PI-Sliding mode controller is developed in feedback loop from a non-inverting buck boost converter, in order to control the recharge of a Li-Ion battery of a Laser Guided Vehicle (LGV), which is the last stage of an industrial wireless recharge power station. In detail, the industrial case study regards the wireless recharge of a LGV employed to transport molds used for footwear soles production. The proposed solution has been modelled and developed in the MATLAB/Simulink® framework, and the performances of this controller are compared with the classical PID industrial controller. The obtained results depend only from the developed recharge system and can be applied in other industrial cases (e.g., car battery recharge). In particular, the numerical simulations show that the proposed solution has an important impact on the energy consumptions
Induction Mold Heating: Modelling and Hardware-in-the-Loop Simulation for Temperature Control
No full textMold heating is an important key factor for the mold cycle which affects the quality of the molded product. The involved molding processes regards polymers foaming, plastics injection, or resin-curing with composites. While the manufacturing cycle of plastics injection molding requires a heating/cooling system, other processes do not require the cooling phase. Polymers foaming and resin-curing require specific values of temperature to provide full chemical reactions and a good product quality on the surface. Induction heating systems are suitable for such applications; however, many industrial cases require customized solutions to support the molding cycle of different parts. A temperature control is always mandatory to reduce the energy cost and increase the heating efficiency. This paper studies a molds temperature control applied in the polyurethane foaming of footwear soles. The proposed induction heating system and its control have been studied using the Hardware-In-the-Loop simulations. Due to a high mold thermal inertia, which increases the mold temperature even if the control system turns off the thermal power, tailored controllers have been analyzed to achieve the desired temperature set-point. The thermal model of the foaming mold and the induction heating system have been modelled and developed in the MATLAB/Simulink® framework. An ATMEGA processor was used to implement and test a discrete PID controller while Simulink induction-heating system model was running, creating an Hardware-In-The-loop platform
Augmented Reality Smart Glasses in the Workplace: Safety and Security in the Fourth Industrial Revolution Era
No full textIndustry 4.0 is reinventing the way in which production is performed. Based on its eight pillars, I4.0 environments are adopting digital solutions in order to make production smart. One of these is the concept of augmented operator, which can act with the aid of digital tools to facilitate daily work. Augmented Reality can represent the turnkey. In this light, the aim of this research is to present a case study of a “security and safety” application through the use of AR smart glasses, tested in a real scenario. For our experiments, Vuzix Blade smart glasses have been tested in combination with a cloud-based architecture connected with an oil-extractor plant. The goal is to develop an AR application that allows to assist the operator during the working process. In particular, it acts as a guide system for the operator who wears glasses, provides remote support (remote operator) and, from a security point of view, sends real-time alerts in dangerous situations. The application has been validated after a number of practical tests carried out by specialised technicians who normally perform the work
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge
Get PDFProduction efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching (Formula presented.) compared to (Formula presented.) for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of (Formula presented.) s compared to (Formula presented.) s for the other model
Machine learning in design for additive manufacturing: A state-of-the-art discussion for a support tool in product design lifecycle
No full textAdditive manufacturing represents one of the most significant improvements in Industry 4.0. Design for additive manufacturing is the discipline that studies integrated CAD/CAE tools with guidelines for optimizing 3D printing in terms of cost, process time, quality, and precision. In this context, machine learning is used to support control and decision-making activities in additive manufacturing. However, the use of machine learning methods is generally limited to one single process phase. No studies are proposing a machine learning approach focused on different phases of the product lifecycle, from the early design phase to manufactured parts. In the literature, machine learning applications for additive manufacturing regard only one specific phase of the production process. This paper describes current improvements in the integration of additive manufacturing and machine learning, highlighting limitations, and proposes to include different phases of the product lifecycle while designing with machine learning tools. The research provides a guide to develop a new design platform where machine learning supports the engineers in the definition of the product design and process parameters. Finally, the paper also introduces the informatics infrastructure and necessary capabilities to implement the proposed model
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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