Predictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks

The use of recycled polypropylene in industry to reduce environmental impact is increasing. Design for manufacturing and process simulation is a key stage in the development of plastic parts. Traditionally, a trial-and-error methodology is followed to eliminate uncertainties regarding geometry and process. A new proposal is presented, combining simulation with the design of experiments and creating prediction models for seven different process and part quality output features. These models are used to optimize the design without developing additional time-consuming simulations. The study aims to compare the precision and correlation of these models. The methods used are linear regression and artificial neural network (ANN) fitting. A wide range of eight injection parameters and geometry variations are used as inputs. The predictability of nonlinear behavior and compensatory effects due to the complex relationships between this wide set of parameter combinations is analyzed further in the state of the art. Results show that only Back Propagation Neural Networks (BPNN) are suitable for correlating all quality features in a single formula. The use of prediction models accelerates the optimization of part design, applying multiple criteria to support decision-making. The methodology is applied to the design of a plastic support for induction hobs. Furthermore, this methodology has demonstrated that a weight reduction of 27% is feasible. However, it is necessary to combine process parameters that differ from the standard ones with a non-uniform thickness distribution so that the remaining injection parameters, material properties, and dimensions fall within tolerances

Machine learning methods for quality prediction in thermoplastics injection molding

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksNowadays, competitiveness is a reality in all industrial fields and the plastic injection industry is not an exception. Due to the complex intrinsic changes that the parameters undergo during the injection process, it is essential to monitor the parameters that influence the quality of the final part to guarantee a superior quality of service provided to customers. Quality requirements impose the development of intelligent systems capable to detect defects in the produced parts. This article presents a first step towards building an intelligent system for classifying the quality of produced parts. The basic approach of this work is machine learning methods (Artificial Neural Networks and Support Vector Machines) and techniques that combine the two previous approaches (ensemble method). These are trained as classifiers to detect conformity or even defect types in parts. The data analyzed were collected at a plastic injection company in Portugal. The results show that these techniques are capable of incorporating the non-linear relationships between the process variables, which allows for a good accuracy ( ˜ 99%) in the identification of defects. Although these techniques present good accuracy, we show that taking into account the history of the last cycles and the use of combined techniques improves even further the performance. The approach presented in this article has a number of potential advantages for online predicting of parts quality in injection molding processes.This work was partially supported by the Spanish State Research Agency through the project CHLOE-GRAPH (PID2020-118649RB-l00) and by FCT—Portuguese Foundation for Science and Technology under project grant UIDB/00308/2020.Postprint (author's final draft

Latest Advancements in Micro Nano Molding Technologies – Process Developments and Optimization, Materials, Applications, Key Enabling Technologies

Micro- and nano-molding technologies are continuously being developed due to enduring trends like increasing miniaturization and higher functional integration of products, devices, and systems. Furthermore, with the introduction of higher performance polymers, feedstocks, and composites, new opportunities in terms of material properties can be exploited, and, consequently, more micro-products and micro/nano-structured surfaces are currently being designed and manufactured.Innovations in micro- and nano-molding techniques are seen in the different processes employed in production (injection molding, micro injection molding, etc.); on the use of new and functional materials; for an ever-increasing number of applications (health-care devices, micro-implants, mobility, and communications products, optical elements, micro-electromechanical systems, sensors, etc.); in several key enabling technologies that support the successful realization of micro and nano molding processes (micro- and nano-tooling technologies, process monitoring techniques, micro- and nanometrology methods for quality control, simulation, etc.) and their integration into new manufacturing process chains.This Special Issue reprint showcases research papers and review articles that focus on the latest developments in micro-manufacturing and key enabling technologies for the production of both micro-products and micro-structured surfaces

On-line quality control in polymer processing using hyperspectral imaging

L’industrie du plastique se tourne de plus en plus vers les matériaux composites afin d’économiser de la matière et/ou d’utiliser des matières premières à moindres coûts, tout en conservant de bonnes propriétés. L’impressionnante adaptabilité des matériaux composites provient du fait que le manufacturier peut modifier le choix des matériaux utilisés, la proportion selon laquelle ils sont mélangés, ainsi que la méthode de mise en œuvre utilisée. La principale difficulté associée au développement de ces matériaux est l’hétérogénéité de composition ou de structure, qui entraîne généralement des défaillances mécaniques. La qualité des prototypes est normalement mesurée en laboratoire, à partir de tests destructifs et de méthodes nécessitant la préparation des échantillons. La mesure en-ligne de la qualité permettrait une rétroaction quasi-immédiate sur les conditions d’opération des équipements, en plus d’être directement utilisable pour le contrôle de la qualité dans une situation de production industrielle. L’objectif de la recherche proposée consiste à développer un outil de contrôle de qualité pour la qualité des matériaux plastiques de tout genre. Quelques sondes de type proche infrarouge ou ultrasons existent présentement pour la mesure de la composition en-ligne, mais celles-ci ne fournissent qu’une valeur ponctuelle à chaque acquisition. Ce type de méthode est donc mal adapté pour identifier la distribution des caractéristiques de surface de la pièce (i.e. homogénéité, orientation, dispersion). Afin d’atteindre cet objectif, un système d’imagerie hyperspectrale est proposé. À l’aide de cet appareil, il est possible de balayer la surface de la pièce et d’obtenir une image hyperspectrale, c’est-à-dire une image formée de l’intensité lumineuse à des centaines de longueurs d’onde et ce, pour chaque pixel de l’image. L’application de méthodes chimiométriques permettent ensuite d’extraire les caractéristiques spatiales et spectrales de l’échantillon présentes dans ces images. Finalement, les méthodes de régression multivariée permettent d’établir un modèle liant les caractéristiques identifiées aux propriétés de la pièce. La construction d’un modèle mathématique forme donc l’outil d’analyse en-ligne de la qualité des pièces qui peut également prédire et optimiser les conditions de fabrication.The use of plastic composite materials has been increasing in recent years in order to reduce the amount of material used and/or use more economic materials, all of which without compromising the properties. The impressive adaptability of these composite materials comes from the fact that the manufacturer can choose the raw materials, the proportion in which they are blended as well as the processing conditions. However, these materials tend to suffer from heterogeneous compositions and structures, which lead to mechanical weaknesses. Product quality is generally measured in the laboratory, using destructive tests often requiring extensive sample preparation. On-line quality control would allow near-immediate feedback on the operating conditions and may be transferrable to an industrial production context. The proposed research consists of developing an on-line quality control tool adaptable to plastic materials of all types. A number of infrared and ultrasound probes presently exist for on-line composition estimation, but only provide single-point values at each acquisition. These methods are therefore less adapted for identifying the spatial distribution of a sample’s surface characteristics (e.g. homogeneity, orientation, dispersion). In order to achieve this objective, a hyperspectral imaging system is proposed. Using this tool, it is possible to scan the surface of a sample and obtain a hyperspectral image, that is to say an image in which each pixel captures the light intensity at hundreds of wavelengths. Chemometrics methods can then be applied to this image in order to extract the relevant spatial and spectral features. Finally, multivariate regression methods are used to build a model between these features and the properties of the sample. This mathematical model forms the backbone of an on-line quality assessment tool used to predict and optimize the operating conditions under which the samples are processed

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische Universität Ilmenau, September 04-08, 2023 : programme

In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this year’s conference “Engineering for a Changing World” refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people – enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: – Precision engineering and measurement technology Nanofabrication – Industry 4.0 and digitalisation in mechanical engineering – Mechatronics, biomechatronics and mechanism technology – Systems engineering – Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university

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

Design for additive manufacturing: Trends, opportunities, considerations, and constraints

The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry

Design for additive manufacturing: trends, opportunities, considerations, and constraints

© 2016 CIRP. The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM's full potential in industry

Prediction of Robot Execution Failures Using Neural Networks

In recent years, the industrial robotic systems are designed with abilities to adapt and to learn in a structured or unstructured environment. They are able to predict and to react to the undesirable and uncontrollable disturbances which frequently interfere in mission accomplishment. In order to prevent system failure and/or unwanted robot behaviour, various techniques have been addressed. In this study, a novel approach based on the neural networks (NNs) is employed for prediction of robot execution failures. The training and testing dataset used in the experiment consists of forces and torques memorized immediately after the real robot failed in assignment execution. Two types of networks are utilized in order to find best prediction method - recurrent NNs and feedforward NNs. Moreover, we investigated 24 neural architectures implemented in Matlab software package. The experimental results confirm that this approach can be successfully applied to the failures prediction problem, and that the NNs outperform other artificial intelligence techniques in this domain. To further validate a novel method, real world experiments are conducted on a Khepera II mobile robot in an indoor structured environment. The obtained results for trajectory tracking problem proved usefulness and the applicability of the proposed solution