Approach for the observation of surface conditions in-process by soft sensors during cryogenic hard turning

Uebel, Julian; Ströer, Felix; Basten, Stephan; Ankener, Werner; Hotz, Hendrik; Heberger, Lukas; Stelzer, Gerhard; Kirsch, Benjamin; Smaga, Marek; Seewig, Jörg; Aurich, Jan C.; Beck, Tilmann

doi:10.1016/j.procir.2019.03.304

Cited by 12 publications

(3 citation statements)

References 18 publications

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“…Other works showed the consequences of tool substrate [3,31], tool coating [34], cutting edge chamfer [3,35,36] and rake angle variation [37]. Different workpiece materials and initial material properties influence manufactured SLP as well [38][39][40]. Zahoor et al [41,42] report on the influence of tool vibrations, which are heavily dependent on the specific spindle and tool holder, on resulting SLP.…”

Section: Introductionmentioning

confidence: 99%

“…The predicted white layer thickness values showed deviations of more than 100% to experimental data. Uebel et al [40] proposed a soft sensor based on force, temperature, pneumatic, and angle resolved scattered light sensor measurement. Fricke et al [58][59][60] developed an eddy current sensor measurement in combination with a cutting simulation for the online prediction of martensite content during turning.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning of Surface Layer Property Prediction for Milling Operations

Uhlmann

Holznagel

Schehl

et al. 2021

JMMP

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Tool wear and cutting parameters have a significant effect on the surface layer properties in milling. Since the relation between tool wear, cutting parameters, and surface layer properties is mostly unknown, the latter cannot be controlled during production and may vary from part to part as tool wear progresses. To account for this uncertainty and to prevent premature failure, components often need to be oversized or surface layer properties need to be adjusted in subsequent manufacturing processes. Several approaches have been made to obtain models that predict the surface layer properties induced by manufacturing processes. However, those approaches need to be calibrated with a considerable number of experimental trials. As trials are time-consuming and surface layer measurements are laborious, no industrial applications have been realized. Complex models have one major drawback. They have to be re-parameterized as soon as process characteristics change. Therefore, manual experimental parameterization does not appear to be a feasible approach for industrial application. A highly automated approach for the machine learning of the relation between tool wear, cutting parameters and surface layer properties is presented in this paper. The amount of obtained measurement data allows a fundamental analysis of the approach, which paves the way for further developments.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning of Surface Layer Property Prediction for Milling Operations

Uhlmann

Holznagel

Schehl

et al. 2021

JMMP

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“…That is, when the correlations between the thermomechanical load and the resulting surface integrity have already been quantified, in-situ measurable values such as temperature and process forces can be used to determine the surface integrity already during the machining process. According to Uebel et al (2019) the use of soft-sensors enables the in-situ detection of deviations in the thermomechanical load caused by disturbance variables (e.g. tool wear), which in turn would result in undesired deviations in the surface integrity.…”

Section: Introductionmentioning

confidence: 99%

Impact of the thermomechanical load on subsurface phase transformations during cryogenic turning of metastable austenitic steels

2020

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When machining metastable austenitic stainless steel with cryogenic cooling, a deformation-induced phase transformation from γ-austenite to α′-martensite can be realized in the workpiece subsurface. This leads to a higher microhardness and thus improved fatigue and wear resistance. A parametric and a non-parametric model were developed in order to investigate the correlation between the thermomechanical load in the workpiece subsurface and the resulting α′-martensite content. It was demonstrated that increasing passive forces and cutting forces promoted the deformation-induced phase transformation, while increasing temperatures had an inhibiting effect. The feed force had no significant influence on the α′-martensite content. With the proposed models it is now possible to estimate the α′-martensite content during cryogenic turning by means of in-situ measurement of process forces and temperatures.

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