In this paper, a method for determining the filling level of grooves (1 mm (W) × 0.25 mm (H)) in pressed titanium pipe-fitting joints is presented. The joints are inspected in a water bath using a 20 MHz phased array ultrasound, and the acquired raw B-scans are evaluated by a convolutional neural network that performs per-groove regression. Reference filling levels are obtained destructively from micrographs. Compared to X-ray computed tomography and destructive sectioning, the proposed approach overcomes the low material contrast between pipe and fitting, avoids long scan times, and enables a nondestructive, potentially inline-capable quantitative assessment of sub-millimeter grooves. A manual high-frequency ultrasound evaluation with a single probe and conceivable rule-based time-of-flight pipelines with hand-crafted echo picking and thresholds both show only moderate agreement with CT references and require substantial feature engineering for multiple echoes. In contrast, the PAUT-CNN method exploits the full raw B-scan without explicit feature design and achieves a root mean square error of about 7% of the groove filling levels on a held-out test set, corresponding to an absolute error on the order of a few tens of micrometers in groove height. This demonstrates that high-frequency phased array ultrasound combined with data-driven evaluation can quantitatively assess the filling of sub-millimeter grooves in aerospace-relevant press-fit connections.

Electromagnetic methods for non-destructive evaluation (NDE) are presented, with which sheet metal components can be identified and their material properties can be characterized. The latter is possible with 3MA, the Micromagnetic Multiparametric Microstructure and stress Analyser. This is a combination of several micromagnetic NDE methods that make it possible to analyse the microstructure in a ferromagnetic material and to determine quantitative values of the mechanical material properties or the stress state. In the case of cold forming, the 3MA application for pre-process testing of sheet metal is discussed. Based on the 3MA information, the formability of the sheets can be predicted. To apply 3MA in-line, the influence of the relative speed and the relative distance between the 3MA probe head and the sheet was investigated. In a second study, a spatially resolved eddy current (EC) method was used to create an image of the intrinsic material microstructure of a component for its identification and traceability. It turned out, that these intrinsic fingerprint images can still be recognized even after subsequent plastic deformation or coating of the surface. This enabled the development of a marker-free traceability method for sheet metal processing. It is based on a low-cost array sensor and a specimen identification using robust and partly redundant features of the fingerprint images processed by machine learning (ML).

With the increasing power and speed of laser welding, in-process monitoring has become even more crucial to ensure process stability and weld quality. Due to its low cost and installation flexibility, acoustic process monitoring is a promising method and has demonstrated its effectiveness. Although its feasibility has been the focus of existing studies, the temporal resolution of acoustic emissions (AE) has not yet been addressed despite its utmost importance for realizing real-time systems. Aiming to provide a benchmark for further development, this study investigates the relationship between duration and informativeness of AE signals during high-power (3.5 kW) and high-speed (12 m/min) laser beam butt welding. Specifically, the informativeness of AE signals is evaluated based on the accuracy of detecting and quantifying joint gaps for various time windows of signals, yielding numerical comparison. The obtained results show that signals can be shortened up to a certain point without sacrificing their informativeness, encouraging the optimization of the signal duration. Our results also suggest that large gaps (>0.3 mm) induce unique signal characteristics in AE, which are clearly identifiable from 1 ms signal segments, equivalent to 0.2 mm weld seam.

This study aimed to explore the feasibility of using airborne acoustic emission in laser beam butt welding for the development of an automated classification system based on neural networks. The focus was on monitoring the formation of joint gaps during the welding process. To simulate various sizes of butt joint gaps, controlled welding experiments were conducted, and the emitted acoustic signals were captured using audible-to-ultrasonic microphones. To implement an automated monitoring system, a method based on short-time Fourier transformation was developed to extract audio features, and a convolutional neural network architecture with data augmentation was utilized. The results demonstrated that this non-destructive and non-invasive approach was highly effective in detecting joint gap formations, achieving an accuracy of 98%. Furthermore, the system exhibited promising potential for the low-latency monitoring of the welding process. The classification accuracy for various gap sizes reached up to 90%, providing valuable insights for characterizing and categorizing joint gaps accurately. Additionally, increasing the quantity of training data with quality annotations could potentially improve the classifier model’s performance further. This suggests that there is room for future enhancements in the study.

Accurate analysis and optimal operation of electromagnetic devices can only be predicted by a robust modeling of ferromagnetic materials. In this context, several hysteresis models have been proposed. The Jiles-Atherton model [1] is commonly used, it is among the main studied in the literature. However, it requires robust identification process of five interdependent parameters, based on experimental hysteresis loop. In a recent paper, the artificial bee colony ABC method, inspired by the collective behavior of bees [2] is applied for the identification [3]. The present work deals about comparison between the ABC method and the particle swarm optimization method PSO [4], which is based on the motion of swarms of birds and fishes. The objective of this study is to give performance of both algorithms in computing time, iteration numbers and parameter estimation. Finally, obtained hysteresis loops is compared to experiment data.

This paper discusses laser hardening depth inspection via the industrial 3MA-eddy current module by combining numerical modeling and experiments. The hardening treatment process leads to difference in microstructure from the surface to the bulk, which engenders various signatures in the eddy current signals, using single fre-quency Eddy current excitation, it is possible to define a link between the measured quantities and laser hard-ening depth. The results of finite element simulations confirms that the obtained signals are intrinsically linked to the magnetic properties of the specimen material. From the profile of the eddy current signals versus laser hardening depth, the skin depth is accurately evaluated at the indicated frequency.

The paper addresses the investigation of microstructures from AISI 52100 and AISI 4140 in hardened as well as in quenched and tempered conditions. The specimens are compared in terms of their magnetic hysteresis and their microstructural and mechanical properties. Material properties were determined by hardness, microhardness, and X-ray diffraction measurements. Two different approaches were used to characterize magnetic properties via a hysteresis frame device, aiming, on the one hand, to record the magnetic hysteresis with established proceedings by setting a constant magnetic flux and, on the other hand, by offsetting a constant field strength to facilitate reproducibility of the results with other micromagnetic measurement systems. Comparable differences in both the micromagnetic and the mechanical material properties could be determined and quantified for the specifically manufactured specimens. The sensitivity of the magnetic hysteresis and, determined from that, the relationship between magnetic flux and magnetic field strength were confirmed. It was shown that a consistent change in hysteresis shape from hardened to high temperature tempered material states develops and that this change allows the characterization of different materials without the need to adjust magnetization parameters. Repeatedly, an increase in remanence with decreasing hardness was found for both test approaches. Likewise, a decreasing coercivity and increasing maximum magnetic flux could be detected with decreasing retained austenite content. The investigated correlations should thus contribute to the calibration of comparable measurement systems through the holistic characterized specimens.

Y This paper highlights the robustness of the semi analytical computation so-called 'coupled circuit' method for the simulation of the eddy current method integrated in the commercial non-destructive testing, 3MA equipment. For this purpose, semi-analytical development are carried out for calculation of voltage around eddy current-receiver coil, taking into account the skin depth of the sample. The challenge in the following is to reproduce the eddy current 3MA measuring quantities on conductive and ferromagnetic materials under several inspection situation. Furthermore, eddy current parameters will be underlined in impedance diagram varying the frequency and lift-off.

In diesem Beitrag wird das Detektionsvermögen der in-situ Hochgeschwindigkeits-3MA-Prüftechnik hinsichtlich qualitätskritischer Merkmale an AISI4140 mittels maschineller Lernverfahren wie der kanonischen Diskriminanzanalyse (CDA) und die Bewertung von dessen in-situ Fähigkeit erläutert. Abschließend wird das Potenzial in Bezug einer Steuerung und Regelung des Prozesses mithilfe von Softsensorik gezeigt. The detection capability of the in-situ high-speed 3MA inspection technique with respect to quality-critical features on AISI4140 using maschine learning techniques such as canonical discriminant analysis (CDA) and the evaluation of its in-situ capability are shown. The potential regarding control and regulation of the process by means of softsensors is shown in conclusion.

In this paper, the impact of lift-off on the 3MA minaturized probe head via harmonic analysis method is discussed. The electromagnetic signals are examined using two numerical computational methods; the finite element method (FEM) and the finite volume method (FVM) by taking into account the hysteretic and eddy current behavior of ferromagnetic parts. The investigation is run on bilayer specimen and the result demonstrates the ability and accuracy of both FVM and FEM to reprocduce the experimental signals. Beside this, simulations are carried out for various lift-off in order to evaluate the skin depth and limit of the magnetic NDT technique.

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 Industry4.0 production-integrated determination of material characteristics can enable in-line monitoring and closed-loop control for an optimization of production processes.

Friction stir welding (FSW) is a solid‐state joining method that is suitable for joining dissimilar materials such as aluminum and steel due to its comparatively low process temperatures. Such hybrid joints are of great interest in view of lightweight construction efforts in various industrial sectors such as transportation. As the combination of different metals in hybrid structures may cause corrosion problems in the welding area because of the formation of a galvanic couple, the corrosion properties are investigated. This work also deals with the influence of additionally transmitted power ultrasound during friction stir welding on the joint properties of AA6061/DP600. Light microscopic analysis and radiographic results show differences in the amount and size of steel particles in the near‐surface area of the joints depending on the used ultrasound power. Although the aluminum alloy and the dual‐phase steel exhibit a Volta potential difference of about 0.8 V in scanning Kelvin probe (SKP) measurements, the measured corrosion current densities on different positions of the AA6061/DP600 joints in 0.5 m sodium chloride solution are only low and no enhanced Galvanic corrosion is observed. A distinct influence of the power ultrasound on the corrosion properties is not given.

The paper deals with the application of the artificial bee colony (ABC) method for hysteresis parameters identification. For the first time, the ABC method will be applied on hysteresis model optimization. For this purpose, two hysteresis models are tested: the first is based on a physical magnetic material behavior, which is Jiles-Atherton and the second is simpler, Fröhlich hysteresis model built on mathematical considerations. This method’s robustness will be assessed, by comparing the experimental signals to model results.

Structure-borne acoustic emission (AE) measurement shows major advantages regarding quality assurance and process control in industrial applications. In this paper, laser beam welding of steel and aluminum was carried out under varying process parameters (welding speed, focal position) in order to provide data by means of structure-borne AE and simultaneously high-speed video recordings. The analysis is based on conventionally (e.g. filtering, autocorrelation, spectrograms) as well as machine learning methods (convolutional neural nets) and showed promising results with respect to the use of structure-borne AE for process monitoring using the example of spatter formation.

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.

Das Forschungsprojekt „Prozessintegrierte Softsensorik zur Oberflächenkonditionierung beim Außenlängsdrehen von 42CrMo4“ widmet sich der Entstehung und der In-process-Erfassung von industriell relevanten Randschichtzuständen. Im Speziellen werden sogenannte White Layer und Eigenspannungszustände untersucht. Durch die modulare Verknüpfung von zerstörungsfreier Prüftechnik, Simulationsergebnissen und Prozesswissen mittels Datenfusion wird ein Softsensor erforscht. Dieser soll im Rahmen einer adaptiven Regelung des Drehprozesses eingesetzt werden und eine gezielte Einstellung von vorteilhaften Randschichtzuständen erlauben. The research project „Process-integrated soft sensor technology for surface conditioning during external longitudinal turning of 42CrMo4“ is dedicated to the formation and in-process-detection of surface layers with industrial relevance. In particular, so-called white layers and residual stresses are investigated. A soft sensor is being researched through the modular combination of non-destructive testing technology and process knowledge by means of data fusion. This is to be used in the context of an adaptive control of the turning process in order to adjust beneficial surface states.

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.

This paper investigates differential and incremental permeability in non-destructive testing application, evaluated by two different technologies, 1) using a "yoke probe head" principal, via a surrounding coil; and 2) via a local receiver coil situated over the sample. The detected signals are simulated using 3D non-linear finite element method. The dynamic vector hysteresis nature of the ferromagnetic part is considered. The incremental permeability is calculated analytically and the profile is compared to measurement data. The FEM computed signals of both NDT technologies are compared to the experimental results, and show good agreement.

As consistent lightweight construction nowadays becomes more and more important in smart production processes, the demand for joints of dissimilar materials increases steadily due to their variety of advantages in engineering. Friction stir welding (FSW) is an innovative pressure welding technique, which offers the ability to realize such dissimilar joints while achieving high tensile strengths. Furthermore, it has been proved that ultrasound enhanced friction stir welding (USE-FSW) has an additional positive effect on the joint strength of these compounds due to the additional introduction of mechanical energy into the joining zone through influencing the formation of brittle intermetallic phase (IMP) and particle allocation in the weld nugget. In this paper, the influence of power ultrasound introduction via USE-FSW on hybrid joints of industrially die-cast aluminum alloy EN AC-48000 (AlSi12CuNiMg) and magnesium alloy AZ91 (MgAl9Zn1) has been investigated. Besides mechanical testing, light microscopic and scanning electron microscopic investigations (SEM) as well as differential scanning calorimetry have been conducted. Furthermore, corrosion behavior of the base material and X-ray radiographic images of FSW and USE-FSW joints have been examined. Additionally, the influence of different ultrasound powers and changes in the introduction side on the tensile strength and microstructure of the joints has been investigated.