A Multimodal Deep Learning-Based Fault Detection Model for a Plastic Injection Molding Process

The authors of this work propose a deep learning-based fault detection model that can be implemented in the field of plastic injection molding. Compared to conventional approaches to fault detection in this domain, recent deep learning approaches prove useful for on-site problems involving complex underlying dynamics with a large number of variables. In addition, the advent of advanced sensors that generate data types in multiple modalities prompts the need for multimodal learning with deep neural networks to detect faults. This process is able to facilitate information from various modalities in an end-to-end learning fashion. The proposed deep learning-based approach opts for an early fusion scheme, in which the low-level feature representations of modalities are combined. A case study involving real-world data, obtained from a car parts company and related to a car window side molding process, validates that the proposed model outperforms late fusion methods and conventional models in solving the problem

The Science and Technology of 3D Printing

Three-dimensional printing, or additive manufacturing, is an emerging manufacturing process. Research and development are being performed worldwide to provide a better understanding of the science and technology of 3D printing to make high-quality parts in a cost-effective and time-efficient manner. This book includes contemporary, unique, and impactful research on 3D printing from leading organizations worldwide

Utilização de mecanismos de inteligência artificial para a monitorização do processo de moldação por injeção

Dissertação de mestrado integrado em Engenharia de PolímerosO processo de moldação por injeção ao longo dos anos tem vindo a ser bastante utilizado na produção de alta cadência de componentes plásticos. Com o crescente desenvolvimento desta área, as peças foram-se tornando cada vez mais complexas, o que exige um melhor controlo do processo. Por outro lado, o aparecimento da quarta revolução industrial veio promover uma transformação digital capaz de melhorar o desempenho e adaptação do processo de moldação por injeção através da sensorização da produção, com o recurso a sistemas de inteligência artificial. Este projeto focou-se no desenvolvimento de um sistema de monitorização autónomo para o controlo de falhas durante a injeção de uma peça plástica, tendo sido realizado nas instalações do Pólo de Inovação em Engenharia de Polímeros (PIEP). O principal objetivo desta dissertação visou o desenvolvimento de um programa de deteção e previsão de falhas durante o processo de moldação através da implementação de modelos de inteligência artificial conhecidos como machine learning, intervindo com ações corretivas consoante a identificação do erro. Depois do estudo e compreensão dos protocolos de comunicação entre computador/máquina levou-se a cabo o desenvolvimento de uma base de dados para o armazenamento e processamento de todos os dados recolhidos em tempo real. A utilização de sensores para o estudo de comportamentos padrão de diversos defeitos influenciados por desvios paramétricos ajudaram na obtenção de uma correlação entre o ambiente virtual de injeção e injeções reais. Mais concretamente, as funcionalidades do programa idealizado foram implementadas e testadas através da monitorização do processo em ambiente virtual de injeção com a introdução de datasets não rotulados, tendo-se reforçado a correlação entre os dois ambientes de estudo testados através de um modelo de regressão de machine learning. Em suma, o objetivo principal de diminuir a intervenção humana durante o processo, através de mecanismos de monitorização computorizada e automática, foi alcançado com resultados bastantes positivos no contexto de teste implementado durante o projeto. Conseguiu-se alcançar uma monitorização autónoma do processo de injeção tendo-se verificado um aumento de eficiência e diminuição do tempo de resposta durante o controlo do processo quando comparado com o processo manual correspondente.The injection moulding process over the years has been widely used in the production of high cadence of plastic components. With the growing development of this area, the injection moulds geometry have become increasingly complex, which requires better control of the process. On the other hand, the emergence of the fourth industrial revolution promoted a digital transformation capable of improving the performance and adaptation of the injection moulding process through the sensing of the production linked to artificial intelligence systems. This project focused on the development of an autonomous monitoring system for the control of failures during the injection of a plastic mould, having been carried out in the facilities of the Pole of Innovation in Polymer Engineering (PIEP). Thus, the main objectives of the current dissertation aimed the development of a program with the ability of detecting and predicting process faults during production, where machine learning models were implemented in order to intervene autonomously with corrective actions depending on fault type. To ensure these requirements, a fully understanding of the communication protocols between computer/machine was needed. A database was created to store, and process all outsourced from real-time data. With the use of sensors to study diverse pattern behaviours influenced by parametric deviations, helped obtaining correlations between the virtual environment of injection and real injections environment. All the idealized program functionalities were tested through a virtual monitorization of the injection moulding process with the presence of non-labelled datasets. Looking ahead, all the functionalities of the software to monitor the process were tested through a virtual injection moulding environment with non-labelled datasets and the correlation between both environments of the study was increased by a machine learning regression model. As a conclusion, the main goal of reducing human intervention during the process with the use of monitorization mechanism was achieved with the outstanding positive results obtained during the application of all the measures conducted along the project. It was attained an autonomous monitorization of the injection moulding process, providing both good efficiency and good response time for the control of the process in comparison with the response time taken with the manual process

National Educators' Workshop: Update 1991. Standard Experiments in Engineering Materials Science and Technology

Given here is a collection of experiments presented and demonstrated at the National Educators' Workshop: Update 91, held at the Oak Ridge National Laboratory on November 12-14, 1991. The experiments related to the nature and properties of engineering materials and provided information to assist in teaching about materials in the education community

Tekes projekti SuperMachines loppuraportti

Tutkimuksessa kerättiin best practice aineistoa ja kehitettiin internet alusta kerätyn aineiston tutkimiseen ja hakujen suorittamiseen. Aineisto löytyy internet osoitteesta: http://www.amcase.info/. Rekisteröitymällä kuka vain voi syöttää alustalle lisää aineistoa. Kappaleiden suunnitteluohjeet on julkaistu Suomen pikavalmistusyhdistyksen sivuilla: http://firpa.fi/html/am-tietoa.html. Ohjeesta löytyy mm. suositeltu minimi seinämänvahvuus, suositellun pienimmän yksityiskohdan koko, tyypillinen markkinoilta löytyvä rakennuskammin koko, sekä tyypilliset materiaalit. Valmiiden kokoonpanojen ja mekanismien suunnitteluun muodostettiin Objet 30 ja UPrint SE+ laitteelle ohjeistus josta löytyy pienin radiaalinen välys, aksiaalinen välys, sekä pienin rako riippuen rakennussuunnasta. Tutkimusprojektin aikana seurattiin alan teknologian kehitystä. Kahden vuoden aikana markkinoille ilmaantui noin. 50 uutta laitevalmistajaa, sekä noin 300 erilaista laitetta, sekä lukuisia materiaaleja. Merkittävimmät uudistukset listattiin ja pohdittiin mahdollisia kehityssuuntia. Kaikki uudet toimijat ja laitteet päivitettiin Firpan ylläpitämään tietokantaan: http://firpa.fi/html/am-tietoa.html. Markkinoilla on selvä suuntaus tuotantokomponenttien valmistamiseen, kotitulostimien hintojen laskemiseen, sekä isompien kappaleiden valmistamiseen. Muovilevy komponenttien muovaamista tutkittiin laserin ja alipaineen avulla DDShape laitteella. Laitteella onnistuttiin tekemään testikappaleita ja laitetta saatiin kehitettyä eteenpäin. Laitteiston kehittämiseksi ja kaupallistamisen tueksi Tekes on myöntänyt "Tutkimusideoista uutta tietoa ja liiketoimintaa" (TUTLI) rahoituksen. ISF mini projektissa onnistuttiin kehittämään edullinen pienten kappaleiden painomuovauskone. Samalla kartoitettiin laitteelle soveltuvat parametrit ja rajoitukset. Laseravusteisella muovaamisella päästään kuparilla isompaan seinämän kaltevuuteen ja pinnalaatu pysyy hyvänä. Teräksellä laserista ei ollut juuri hyötyä ja alumiinilla muovattavuus kyllä parani, mutta pinnalaatu huononi. AM kappaleiden viimeistelykoneistuksessa tutkittiin muovisten kappaleiden viimeistely jyrsimällä, sekä metallikappaleiden automaattista hiontaa. Jyrsinnässä vertailtiin eri menetelmillä tehtyjä kappaleita, sekä mitattiin kappaleiden mittatarkkuutta ja geometrisia toleransseja. Huonosta kotitulostimella tehdystä kappaleesta on vaikea saada hyvää kappaletta vaikka se viimeisteltäisiin koneistamalla. Suurimmat ongelmat liittyvät kappaleiden vääntymiseen johtuen lämpöjännityksistä valmistusprosessin aikana. Kappaleiden automaattisessa hionnassa parhaat tulokset saatiin DMLS kappaleille käyttämällä hionta-aineena teräshauleja ja pyörittämällä niitä hiottavat kappaleen kanssa rummussa. Ra arvo parani tällöin noin seitsemästä mikrometristä kolmeen mikrometriin

Reinforced Polymer Composites

This book, consisting of 21 articles, including three review papers, written by research groups of experts in the field, considers recent research on reinforced polymer composites. Most of them relate to the fiber-reinforced polymer composites, which are a real hot topic in the field. Depending on the reinforcing fiber nature, such composites are divided into synthetic and natural fiber-reinforced ones. Synthetic fibers, such as carbon, glass, or basalt, provide more stiffness, while natural fibers, such as jute, flax, bamboo, kenaf, and others, are inexpensive and biodegradable, making them environmentally friendly. To acquire the benefits of design flexibility and recycling possibilities, natural reinforcers can be hybridized with small amounts of synthetic fibers to make them more desirable for technical applications. Elaborated composites have great potential as structural materials in automotive, marine and aerospace application, as fire resistant concrete, in bridge systems, as mechanical gear pair, as biomedical materials for dentistry and orthopedic application and tissue engineering, as well as functional materials such as proton-exchange membranes, biodegradable superabsorbent resins and polymer electrolytes

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Engineering Sustainability for the Future

The 38th International Manufacturing Conference, IMC38, showcases current research in the field of "manufacturing engineering" undertaken in Ireland by postgraduate students and experienced researchers. Indicative topics, in line with the contents of these proceedings, include; sustainable and energy efficient manufacturing, additive manufacturing, Industry 4.0 and digital manufacturing, machine tool, automation and manufacturing system design, surface engineering, forming and joining process research. The IMC community is also involved in research aimed at improving the learning experience of undergraduate and graduate engineers and developing high level skills for the manufacturing engineer of the future. The theme for this year’s conference is Sustainable Manufacturing, with a particular emphasis on a) Digitalisation of Manufacturing – its impact on sustainability and b) Addressing sustainability in Engineering Education, Industrial Training and CPD.Science Foundation Irelan

The 1st Advanced Manufacturing Student Conference (AMSC21) Chemnitz, Germany 15–16 July 2021

The Advanced Manufacturing Student Conference (AMSC) represents an educational format designed to foster the acquisition and application of skills related to Research Methods in Engineering Sciences. Participating students are required to write and submit a conference paper and are given the opportunity to present their findings at the conference. The AMSC provides a tremendous opportunity for participants to practice critical skills associated with scientific publication. Conference Proceedings of the conference will benefit readers by providing updates on critical topics and recent progress in the advanced manufacturing engineering and technologies and, at the same time, will aid the transfer of valuable knowledge to the next generation of academics and practitioners. *** The first AMSC Conference Proceeding (AMSC21) addressed the following topics: Advances in “classical” Manufacturing Technologies, Technology and Application of Additive Manufacturing, Digitalization of Industrial Production (Industry 4.0), Advances in the field of Cyber-Physical Systems, Virtual and Augmented Reality Technologies throughout the entire product Life Cycle, Human-machine-environment interaction and Management and life cycle assessment.:- Advances in “classical” Manufacturing Technologies - Technology and Application of Additive Manufacturing - Digitalization of Industrial Production (Industry 4.0) - Advances in the field of Cyber-Physical Systems - Virtual and Augmented Reality Technologies throughout the entire product Life Cycle - Human-machine-environment interaction - Management and life cycle assessmen