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

Pfrommer, Julius; Zimmerling, Clemens; Liu, Jinzhao; Kärger, Luise; Henning, Frank; Beyerer, Jürgen

doi:10.1016/j.procir.2018.03.046

Cited by 108 publications

(59 citation statements)

References 27 publications

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“…Such surrogate models can be developed with machine-learning techniques either with data from realworld experiments, or with data from high-fidelity simulations. One application example is the optimization of process parameters using deep neural networks as surrogate models [27]. Kernel-based approaches are also commonly used as surrogate models for simulations, an example to improve the energetic efficiency of a gas transport network is shown in [10].…”

Section: Machine-learning Assisted Simulationmentioning

confidence: 99%

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions

Rueden

Mayer

Sifa

et al. 2020

Lecture Notes in Computer Science

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In this paper, we describe the combination of machine learning and simulation towards a hybrid modelling approach. Such a combination of data-based and knowledge-based modelling is motivated by applications that are partly based on causal relationships, while other effects result from hidden dependencies that are represented in huge amounts of data. Our aim is to bridge the knowledge gap between the two individual communities from machine learning and simulation to promote the development of hybrid systems. We present a conceptual framework that helps to identify potential combined approaches and employ it to give a structured overview of different types of combinations using exemplary approaches of simulation-assisted machine learning and machine-learning assisted simulation. We also discuss an advanced pairing in the context of Industry 4.0 where we see particular further potential for hybrid systems.

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Section: Machine-learning Assisted Simulationmentioning

confidence: 99%

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions

Rueden

Mayer

Sifa

et al. 2020

Lecture Notes in Computer Science

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“…[18]), including artificial neural networks (ANNs) (e.g. [26,34]). In the particular case of forming processes, researchers are also trying to explore the large amount of data generated (both experimental and numerical) while designing new products, to guide the process design from its early stage with the help of ANN meta-models to predict product feasibility (e.g.…”

Section: Sheet Metal Formingmentioning

confidence: 99%

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Dib

Oliveira

Marques

et al. 2019

Neural Comput & Applic

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This paper presents an approach, based on machine learning techniques, to predict the occurrence of defects in sheet metal forming processes, exposed to sources of scatter in the material properties and process parameters. An empirical analysis of performance of ML techniques is presented, considering both single learning and ensemble models. These are trained using data sets populated with numerical simulation results of two sheet metal forming processes: U-Channel and Square Cup. Data sets were built for three distinct steel sheets. A total of eleven input features, related to the mechanical properties, sheet thickness and process parameters, were considered; also, two types of defects (outputs) were analysed for each process. The sampling data were generated, assuming that the variability of each input feature is described by a normal distribution. For a given type of defect, most single classifiers show similar performances, regardless of the material. When comparing single learning and ensemble models, the latter can provide an efficient alternative. The fact that ensemble predictive models present relatively high performances, combined with the possibility of reconciling model bias and variance, offer a promising direction for its application in industrial environment.

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“…injection pressure [125]. Surrogate model based optimisations have been implemented to solve the draping problem [126] and to allow robust optimisation of the cure process [122]. A Bayesian inverse problem has been implemented to improve the probabilistic knowledge of permeability during the RTM process.…”

Section: Batch Processesmentioning

confidence: 99%

Numerical optimisation of thermoset composites manufacturing processes: A review

Struzziero

Teuwen

Skordos

2019

Composites Part A: Applied Science and Manufacturing

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The impetus for higher performance, robustness and efficiency in the aerospace, automotive and energy industries has been reflected in more stringent requirements which the composite manufacturing industry needs to comply with. The process design challenges associated with this are significant and can be only partially met by integration of simulation in the design loop. The implementation of numerical optimisation tools is therefore necessary. The development of methodologies linking predictive simulation tools with numerical optimisation techniques is pivotal to identify and therefore develop optimal design conditions that allow full exploitation of the efficiency opportunities in composite manufacturing. Numerical and experimental results concerning the optimisation techniques and methodologies implemented in literature to address the optimisation of thermoset composite manufacturing processes are presented and analysed in this study.

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

Cited by 108 publications

References 27 publications

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions

Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions

Single and ensemble classifiers for defect prediction in sheet metal forming under variability

Numerical optimisation of thermoset composites manufacturing processes: A review

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