Daniel Gauder

Daniel Gauder, MBA

  • 76131 Karlsruhe
    Kaiserstraße 12

Daniel Gauder, MBA

Area of Research:

  • Process optimization through the implementation of in process measurement technology in machine tools
  • In-Line measurement

General Tasks:

  • Production metrology
    • Acoustic measurement
  • Learning Factory Scalable Automation


  • SPP-OK Prozessintegrierte Softsensorik zur Oberflächenkonditionierung
  • ProIQ, Use case Mikroverzahnung

Test benches:

Curriculum Vitae:

since 07/2018

Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT

10/2015 - 02/2018 

Master Studies of „Production Management“at Chemnitz University of Technology

10/2011 - 05/2015 

Bachelor Studies of Business Administration and Engineering

18/04/1985 Born in Bruchsal


[ 1 ] Stampfer, B.; Böttger, D.; Gauder, D.; Zanger, F.; Häfner, B.; Straß, B.; Wolter, B.; Lanza, G. & Schulze, V. (2020), "Experimental identification of a surface integrity model for turning of AISI4140". Procedia CIRP 87, pp. 83-88. 10.1016/j.procir.2020.02.067
In this work an experimental study of the turning of AISI4140 is presented. The scope is the understanding of the workpiece microstructure and hardness-depth-profiles which result from different cutting conditions and thus thermomechanical surface loads. The regarded input parameters are the cutting velocity (vc = 100, 300 m/min), feed rate (f = 0.1, 0.3 mm), cutting depth (ap = 0.3, 1.2 mm) and the heat treatment of the workpiece (tempering temperatures 300, 450 and 600?C). The experimental data is interpreted in terms of machining mechanisms and material phenomena, e.g. the generation of white layers, which influence the surface hardness. Hereby the process forces are analyzed as well. The gained knowledge is the prerequisite of a workpiece focused process control.

[ 2 ] Böttger, D.; Stampfer, B.; Gauder, D.; Straß, B.; Häfner, B.; Lanza, G.; Schulze, V. & Wolter, B. (2020), "Concept for soft sensor structure for turning processes of AISI4140", tm - Technisches Messen, vol. 87, no. 12, pp. 745-756. 10.1515/teme-2020-0054
During turning of quenched and tempered AISI4140 surface layer states can be generated, which degrade the lifetime of manufactured parts. Such states may be brittle rehardened layers or tensile residual stresses. A soft sensor concept is presented in this work, in order to identify relevant surface modifications during machining. A crucial part of this concept is the measurement of magnetic characteristics by means of the 3MA-testing (Micromagnetic Multiparameter Microstructure and Stress Analysis). Those measurements correlate with the microstructure of the material, only take a few seconds and can be processed on the machine. This enables a continuous workpiece quality control during machining. However specific problems come with the distant measurement of thin surface layers, which are analyzed here. Furthermore the scope of this work is the in-process-measurement of the tool wear, which is an important input parameter of the thermomechanical surface load. The availability of the current tool wear is to be used for the adaption of the process parameters in order to avoid detrimental surface states. This enables new approaches for a workpiece focused process control, which is of high importance considering the goals of Industry 4.0.

[ 3 ] Wurster, M.; Häfner, B.; Gauder, D.; Stricker, N. & Lanza, G. (2021), "Fluid Automation - A Definition and an Application in Remanufacturing Production Systems". Digitalizing smart factories, Elsevier, pp. 508-513. 10.1016/j.procir.2020.05.267
Production systems must be able to quickly adapt to changing requirements. Especially in the field of remanufacturing, the uncertainty in the state of the incoming products is very high. Several adaptation mechanisms can be applied leading to agile and changeable production systems. Among these, adapting the degree of automation with respect to changeover times and high investment costs is one of the most challenging mechanisms. However, not only long-term changes, but also short-term adaptations can lead to enormous potentials, e.g. when night shifts can be supported by robots and thus higher labor costs and unfavorable working conditions at night can be avoided. These changes in the degree of automation on an operational level are referred to as fluid automation, which will be defined in this paper. The mechanisms of fluid automation are presented together with a case study showing its application on a disassembly station for electrical drives.

[ 4 ] Böttger, D.; Stampfer, B.; Gauder, D.; Lanza, G.; Schulze, V.; Straß, B. & Wolter, B. (2021), "Working point determination of 3MA micromagnetic NDT-technique for production integrated detection of white layer during turning of AISI4140". Elsevier, doi.org/10.1016/j.procir.2021.02.002
High strength steels are important in terms of lightweight, safety and economical aspects for mobility concepts of the future. In fact, machined surfaces and its characteristics are essential for the entire product-lifecycle. In the presented work, the capability of micromagnetic nondestructive-testing (NDT) techniques combined in 3MA, and optimal working point determination to detect critical surface states such as white layer (WL) associated to hardness increase and its characteristics is discussed. An outlook is given how in terms of Industry 4.0 production-integrated determination of material characteristics can enable in-line monitoring and closed-loop control for an optimization of production processes.

[ 5 ] Gauder, D.; Biehler, M.; Gölz, J.; Stampfer, B.; Böttger, D.; Häfner, B.; Wolter, B.; Schulze, V. & Lanza, G. (2021), "Development of a methodical approach for uncertainty quantification and meta-modeling of surface hardness in white layers of longitudinal turned AISI4140 surfaces", tm - Technisches Messen, doi.org/10.1515/teme-2021-0037
The formation of thermally and mechanically induced near-surface microstructures in the form of white layers leads to different hardness properties in these areas. Therefore, this paper conducts systematic surface hardness measurements and uncertainty quantification utilizing the Monte Carlo Method (MCM) in accordance with the Guide to the Expression of Uncertainty in Measurement (GUM). Furthermore, several meta-models describing the hardness course in relationship to the material depth are used to model this nonlinear relationship via machine learning. The evaluation and selection of the optimal model considers the trade-off between measurement uncertainty and prediction quality in terms of mean squared error (MSE). The resulting measurement uncertainty is to be used for the calibration of a non-destructive micromagnetic material sensor. This will then be implemented for in-process monitoring in the outer diameter longitudinal turning process. This should make it possible to detect white layers during machining and to avoid them accordingly by controlling the machine parameters. By means of a soft sensor, the corresponding target value is to be derived from the micromagnetic material sensor measurement.