Optimisation of manufacturing process parameters using deep neural networks as surrogate models

J Pfrommer, C Zimmerling, J Liu, L Kärger, F Henning… - Procedia CiRP, 2018 - Elsevier
Optimisation of manufacturing process parameters requires resource-intensive search in a
high-dimensional parameter space. In some cases, physics-based simulations can replace …

Anomaly detection and prediction in discrete manufacturing based on cooperative LSTM networks

B Lindemann, N Jazdi… - 2020 IEEE 16th …, 2020 - ieeexplore.ieee.org
Manufacturing processes are characterized by their temporal and spatial distributed
nonlinear physics. Analytical models are not available and numerical models do not …

Numerical product design: Springback prediction, compensation and optimization

T Meinders, IA Burchitz, MHA Bonte… - International Journal of …, 2008 - Elsevier
Numerical simulations are being deployed widely for product design. However, the accuracy
of the numerical tools is not yet always sufficiently accurate and reliable. This article focuses …

Rapid feasibility assessment of components to be formed through hot stamping: A deep learning approach

HR Attar, H Zhou, A Foster, N Li - Journal of Manufacturing Processes, 2021 - Elsevier
The state-of-the-art non-isothermal Hot Forming and cold die Quenching (HFQ®) process
can enable the cost-effective production of complex shaped, high strength aluminium alloy …

[HTML][HTML] A machine learning assisted approach for textile formability assessment and design improvement of composite components

C Zimmerling, D Dörr, F Henning, L Kärger - Composites Part A: Applied …, 2019 - Elsevier
Manufacturing continuous fibre reinforced components often involves a forming process of
textiles. Process simulations using Finite Element (FE) techniques allow for an accurate …

Estimating optimum process parameters in textile draping of variable part geometries-a reinforcement learning approach

C Zimmerling, C Poppe, L Kärger - Procedia manufacturing, 2020 - Elsevier
Fine-tuning of manufacturing processes for optimum part quality requires many resource-
intensive trial experiments in practice. To reduce the experimental effort, physics-based …

Optimization of forging processes using finite element simulations: a comparison of sequential approximate optimization and other algorithms

MHA Bonte, L Fourment, T Do… - Structural and …, 2010 - Springer
During the last decades, simulation software based on the Finite Element Method (FEM) has
significantly contributed to the design of feasible forming processes. Coupling FEM to …

An optimisation strategy for industrial metal forming processes: Modelling, screening and solving of optimisation problems in metal forming

MHA Bonte, AH van den Boogaard… - Structural and …, 2008 - Springer
Product improvement and cost reduction have always been important goals in the metal
forming industry. The rise of finite element (FEM) simulations for processes has contributed …

Enhancing an intelligent digital twin with a self-organized reconfiguration management based on adaptive process models

T Müller, B Lindemann, T Jung, N Jazdi, M Weyrich - Procedia CIRP, 2021 - Elsevier
Shorter product life cycles and increasing individualization of production leads to an
increased reconfiguration demand in the domain of industrial automation systems, which will …

[HTML][HTML] Development of a deep learning platform for sheet stamping geometry optimisation under manufacturing constraints

HR Attar, A Foster, N Li - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Sheet stamping is a widely adopted manufacturing technique for producing complex
structural components with high stiffness-to-weight ratios. However, designing such …