Surface conditioning in machining processes

Schulze, Volker; Zanger, Frederik; Stampfer, Benedict; Seewig, Jörg; Uebel, Julian; Zabel, Andreas; Wolter, Bernd; Böttger, David

doi:10.1515/teme-2020-0044

Cited by 7 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those approaches rely on the generation and implementation of complex models and are thus called soft sensors. This term and the complete nomenclature of surface conditioning is specified in a glossary [5], which was also harmonized with the terms in [6]. Those works are closely connected to the Collaborative Research Centers 136, 926 and the Priority Program 2086 of the Deutsche Forschungsgemeinschaft (DFG).…”

Section: Introductionmentioning

confidence: 99%

The Present State of Surface Conditioning in Cutting and Grinding

Stampfer

González

Gerstenmeyer

et al. 2021

JMMP

Self Cite

View full text Add to dashboard Cite

All manufacturing processes have an impact on the surface layer state of a component, which in turn significantly determines the properties of parts in service. Although these effects should certainly be exploited, knowledge on the conditioning of the surfaces during the final cutting and abrasive process of metal components is still only extremely limited today. The key challenges in regard comprise the process-oriented acquisition of suitable measurement signals and their use in robust process control with regard to the surface layer conditions. By mastering these challenges, the present demands for sustainability in production on the one hand and the material requirements in terms of lightweight construction strength on the other hand can be successfully met. In this review article completely new surface conditioning approaches are presented, which originate from the Priority Program 2086 of the Deutsche Forschungsgemeinschaft (DFG).

show abstract

Section: Introductionmentioning

confidence: 99%

The Present State of Surface Conditioning in Cutting and Grinding

Stampfer

González

Gerstenmeyer

et al. 2021

JMMP

Self Cite

View full text Add to dashboard Cite

show abstract

Towards developing a control of grinding processes using a combination of grinding power evaluation and Barkhausen noise analysis

Jedamski,

Kuhlmann,

Rößler

et al. 2024

Prod. Eng. Res. Devel.

View full text Add to dashboard Cite

After grinding of hardened workpieces, especially in case of safety-relevant components, usually non-destructive testing is carried out to detect thermo-mechanical damages in the surface layer. In-process detection methods allow a fast reaction to negative changes before producing a large amount of rejects and open the possibility to save additional time for post-process inspection. However, these are not industrially used yet, since boundary conditions and application limits are not defined so far. This study shows the potential of a grinding process control based on a soft sensor combining a thermal limit in a $${P}_{c}^{{\prime}{\prime}}$$ P c ′ ′ − Δt-diagram and magnetic Barkhausen noise analysis applied in-situ during grinding (BN). The specific grinding power $${P}_{c}^{{\prime}{\prime}}$$ P c ′ ′ in dependence of the contact time Δt allows to identify/avoid grinding burn starting from light tempering zones. Furthermore, the BN is well suited for the detection of detrimental residual stress changes (tensile) occurring even before tempering zones are generated. The developed process control based on this combination optimizes the process parameters to prevent negative thermo-mechanical surface changes while keeping the productivity of the process as high as possible. The applicability is demonstrated for external cylindrical and non-circular grinding processes. Due to the radius varying over the workpiece circumference, the latter ones require special demands on signal evaluation, resolution and control speed.

show abstract

Soft sensor for in-line quality control of turning processes based on non-destructive testing techniques and advanced data fusion

Böttger,

González,

Geiser

et al. 2024

Prod. Eng. Res. Devel.

View full text Add to dashboard Cite

This study describes the systematic process of training, testing, and validating a soft sensor designed for quality control of a turning process on components made of AISI 4140 steel. The soft sensor allows product quality to be predicted and unfavorable surface conditions to be identified, in particular the appearance of a phenomenon known as “White Layer”, often characterized in the case of AISI 4140 steel by an ultra-fine-grained microstructure (UFG). Basis of the soft sensor is a data fusion supported by non-destructive testing techniques (NDT), particularly micromagnetic methods (3MA). A critical part of this work is to address challenges such as lift-off compensation and in-process detection using 3MA. The application of machine-learning techniques, including Principal Component Analysis (PCA) and regression analysis, is detailed. These techniques result in robust models capable of detecting the occurrence of the White Layer phenomenon while minimizing the influence of measurement setup variations and process disturbances. In addition, the study demonstrates the integration of NDT into the machining process which drives the soft sensor and allows suitable adjustments of the process parameters. The data-driven soft sensor approach demonstrates a possible In-Line control system and discusses different control theories and their respective advantages and disadvantages. This system can effectively set targeted surface conditions in real time during the turning process.

show abstract

Surface conditioning in machining processes

Cited by 7 publications

References 5 publications

The Present State of Surface Conditioning in Cutting and Grinding

The Present State of Surface Conditioning in Cutting and Grinding

Towards developing a control of grinding processes using a combination of grinding power evaluation and Barkhausen noise analysis

Soft sensor for in-line quality control of turning processes based on non-destructive testing techniques and advanced data fusion

Contact Info

Product

Resources

About