Recent advances and applications of machine learning in metal forming processes

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Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics

A Process-based Cost Model for Wire and Arc Additive Manufacturing

In engineering one of the main criteria to evaluate new technology or product is its eco- nomic viability. This can only be done by identifying the costs related to the process or prod- uct. Within the Smart WAAM project, which aims to study the use of wire and arc additive manufacturing (WAAM) technology to create, repair and expand the life of large industrial components, it was necessary to develop a cost model to study the economic viability of this technology. This thesis's primary goal is to develop a cost model for the WAAM technology, consid- ering a product life cycle approach. For this purpose, it was necessary to develop a model to estimate the cost of the WAAM technology, as well as the main factors influencing the cost. A process-based cost model (PBCM) was developed since it allows to analyse the costs of the different life cycle phases of a product and estimates the production costs. The study main steps were the objective and scope definition, using a cradle to gate approach, the pro- cess description, and the cost model's development. The object of study was an experimental WAAM machine developed at NOVA School of Science and Technology, and the functional unit was a hollow stainless steel AISI316LSI cube of approximately 7x7x7 cm. The data collec- tion process included the compilation of secondary data available in public websites, but also primary data was collected through unstructured interviews with researchers who developed and worked with the WAAM machine. The model was validated, and the factors influencing the cost were identified. It was possible to determine that the production of 500 cubes has a total cost of 259.95€ per piece. The WAAM process and the surface finishing process and substrate removal rep- resent 84% of the total cost. The main factors that influence the total cost of the process are the acquisition cost of the machines for the production and parts finishing, the cost of the tools, namely the cutters, and the production overheads.Um dos principais critérios para a avaliação de uma nova tecnologia ou produto, na engenharia, é a sua viabilidade económica. Esta, só pode ser estudada através da identificação dos custos inerentes ao processo ou produto. No âmbito do projeto Smart WAAM, que pre- tende estudar a utilização da tecnologia fabrico aditivo usando fio consumível e arco elétrico (WAAM) para criar, reparar e expandir a vida útil de grandes componentes industriais, foi necessário desenvolver um modelo de custos que permita estudar a viabilidade económica da utilização desta tecnologia. O principal objetivo desta dissertação é o desenvolvimento de um modelo de custo para a tecnologia WAAM, considerando uma perspetiva do ciclo de vida do produto. Para esta finalidade, foi necessário desenvolver um modelo que permita estimar os custos da tecnologia WAAM, assim como os principais fatores que influenciam o custo. Foi desenvolvido um modelo de custo baseado no processo, pois permite analisar os custos das diferentes fases do ciclo de vida do produto, bem como fazer estimativas dos custos de produção. Inicialmente o estudo consistiu na definição do objetivo e do âmbito, utilizando uma abordagem berço ao portão, a descrição do processo e o desenvolvimento do modelo de custo. O objeto de estudo foi um equipamento experimental desenvolvido na NOVA School of Science and Technology e a unidade de análise foi um cubo oco de aço inoxidável AISI316LSI com aproximadamente 7x7x7 cm. Foram recolhidos dados secundários (por exem- plo preços de energia e matéria-prima) e dados primários recorrendo a entrevistas não estru- turadas a investigadores que desenvolveram e trabalham com o equipamento. Por fim, o mo- delo foi validado e foram identificados quais os fatores que mais influenciam o custo. Foi possível determinar que a produção de 500 cubos, tem o custo total de 259.95 € por peça. O processo WAAM e o processo de acabamento e remoção do substrato representam 84% do custo total. Os principais fatores que influenciam o custo total do processo são: o custo de aquisição das máquinas de produção e acabamento das peças, o custo das ferramentas, nomeadamente as fresas, e as despesas gerais de produção

Modeling the oriented strandboard manufacturing process and the oriented strandboard continuous rotary drying system

Oriented Strand Board (OSB) is the leading structural panel product used in residential building construction. This dissertation describes three models and a statistical process control technique all designed to aid manufacturers to cost effectively manufacture OSB. The first model is an OSB Mill Process Flow Model that defines the processing steps and the desired outcomes. The second model is an OSB Mill Model, an ExcelRTM based computer program, designed to answer operational what if and trade-off questions. The model is a spreadsheet representation of the OSB production process. The third model is an OSB Dryer Model that predicts the dryer outlet moisture content derived using a multivariate data analysis technique called projection to latent structures by means of Partial Least Squares (PLS). PLS was instrumental in identifying outlet temperature and heat source temperatures as the most influential dryer system variables in predicting dryer outlet moisture content. The SPC technique is Multivariate Statistical Process Control (MSPC) that uses multivariate scores or Hotelling T2 to determine the state of the drying process; and if the drying process is out of control, what process variables influenced the process shift

On assessing grindability of recycled and ore-based crankshaft steel: an approach combining data analysis with material science

Material-related grindability variations when grinding recycled and ore-based steel can significantly impair the process efficiency during finishing of automotive crankshafts. To address this problem and to achieve more robust grinding processes, the underlying causes of variation need to be understood. The present work investigates the feasibility of using quality data obtained during production to study grindability variations and identify material-related effects. Analysis of non-destructive inspection protocols indicates steel supplier-dependent differences in grindability. However, no systematic grindability differences between recycled and ore-based steel could be identified. Possible correlations between grindability and material characteristics obtained from supplied steel certificates are discussed

Numerical Modelling and Simulation of Metal Processing

This book deals with metal processing and its numerical modelling and simulation. In total, 21 papers from different distinguished authors have been compiled in this area. Various processes are addressed, including solidification, TIG welding, additive manufacturing, hot and cold rolling, deep drawing, pipe deformation, and galvanizing. Material models are developed at different length scales from atomistic simulation to finite element analysis in order to describe the evolution and behavior of materials during thermal and thermomechanical treatment. Materials under consideration are carbon, Q&T, DP, and stainless steels; ductile iron; and aluminum, nickel-based, and titanium alloys. The developed models and simulations shall help to predict structure evolution, damage, and service behavior of advanced materials