Improving the laser cutting process design by machine learning techniques

Tercan, Hasan; Khawli, Toufik Al; Eppelt, Urs; Büscher, Christian; Meisen, Tobias; Jeschke, Sabina

doi:10.1007/s11740-017-0718-7

Cited by 37 publications

(16 citation statements)

References 15 publications

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“…Thereby the data analytics process including appropriate data visualization methods are implemented within a Virtual Production Intelligence (VPI) platform [13]. The process is described in detail in [14]. It implemented a hybrid data analytics approach with clustering and classification tree to identify parameters of the manufacturing process that result in desired outputs.…”

Section: Example For Laser Drilling Manufacturingmentioning

confidence: 99%

Sparse Data Enrichment by ContextOriented Model Reduction Techniquesin Manufacturing Industry with an ExampleLaser Drilling Process

Wang

Tercan

Hermanns

et al. 2018

JICTS

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Nowadays, the internet of things and industry 4.0 from Germany are all focused on the application of data analytics and Artificial Intelligence to build the succeeding generation of manufacturing industry. In manufacturing planning and iterative designing process, the data-driven issues exist in the context of the purpose for approaching the optimal design and generating an explicit knowledge. The multi-physical phenomena, the time consuming comprehensive numerical simulation, and a limited number of experiments lead to the so-called sparse data problems or "curse of dimensionality". In this work, an advanced technique using reduced models to enrich sparse data

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Section: Example For Laser Drilling Manufacturingmentioning

confidence: 99%

Sparse Data Enrichment by ContextOriented Model Reduction Techniquesin Manufacturing Industry with an ExampleLaser Drilling Process

Wang

Tercan

Hermanns

et al. 2018

JICTS

Self Cite

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“…Recently, Genna et al [6] applied factorial designs to analyze the effect of material type (AlMg3 aluminium alloy, carbon steel, and stainless steel), workpiece material thickness, cutting speed, and pressure of assist gas on the kerf geometry characteristics, surface roughness and cut edge quality in high power CO 2 laser cutting. A hybrid machine learning approach, consisting of clustering and classification methods, aimed at enhancing the planning process of the laser cutting operations was proposed by Tercan et al [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of specific cutting energy in laser fusion cutting of an aluminium alloy

Madić

Coteaţă

Janković

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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Laser cutting is a non-contact, non-conventional method for contour cutting which is characterized by several process performances. The present paper aims to analyze the specific cutting energy in CO2 laser cutting of an aluminium alloy using nitrogen as an assist gas. An experimental investigation, based on the use of central composite design, was conducted to define mathematical models for the prediction of the kerf width, surface roughness, and specific cutting energy. Thus, an analysis of the effects of the laser power, cutting speed, and nitrogen pressure on the specific cutting energy is enabled. Moreover, the analysis of experimental data included the correlation between the specific cutting energy and surface roughness. Pareto optimization, considering specific cutting energy and surface roughness, was conducted to propose alternative laser cutting conditions concerning conflicting criteria.

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“…Besides the data science problems like clustering, regression, image recognition or pattern formation there are novel applications in the field of engineering, as e.g. for production processes [28,37,42]. Also, application of artifical neural networks (ANN) has recently become a trend in material science since ANN models are more flexible than conventional regression models.…”

Section: Introductionmentioning

confidence: 99%

Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

Gebhardt¹,

Trimborn²,

Weber³

et al. 2020

Preprint

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The heterogeneous microstructure in metallic components results in locally varying fatigue strength. Metal fatigue strongly depends on size and shape of non-metallic inclusions and pores, commonly referred to as "defects". Nodular cast iron (NCI) contains graphite inclusions (nodules) whose shape and frequency influence the fatigue strength. Fatigue strength can be simulated by micromechanical finite element models. The drawback of these models are the large computational costs. Therefore, we employ a data-driven machine learning methodology. More precisely, we utilize the simplified residual neural network (SimResNet) which was recently introduced in [16] to predict fatigue strength from metallographic data.For the training, we use fatigue data which is simulated with a micromechanical model and the shakedown theorem. The micromechanical models are derived directly from micrographs of nodular cast iron, respectively. The application of SimResNet shows a good performance to predict fatigue strength by local microstructures of nodular cast iron. We show several test cases. The simplified character of SimResNet enables fast predictions of fatigue by microstructures, even in comparision to classical residual neural networks.

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Improving the laser cutting process design by machine learning techniques

Cited by 37 publications

References 15 publications

Sparse Data Enrichment by ContextOriented Model Reduction Techniquesin Manufacturing Industry with an ExampleLaser Drilling Process

Sparse Data Enrichment by ContextOriented Model Reduction Techniquesin Manufacturing Industry with an ExampleLaser Drilling Process

Analysis of specific cutting energy in laser fusion cutting of an aluminium alloy

Simplified ResNet approach for data driven prediction of microstructure-fatigue relationship

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