The surface texture of a turned component acts as a fingerprint of both the process parameters and the tool wear condition, imaging the cutting edge. Classical surface parameters such as R_a or R_q describe the topography only globally and allow no spatially resolved evaluation of the process-induced deterministic structures. This work investigates how far the feature characterization standardized in ISO 21920-2 makes this wear information accessible and physically interpretable. The database comprises roughness profiles of twelve AlTiN-coated carbide inserts (CNMG120408) machining normalized AISI 1045 steel, measured at nine wear states over the entire tool life, with three replicate profiles per state. The correlation of standardized field and feature parameters with crater wear, flank wear, and cutting time is first examined. Watershed segmentation is then adapted to extract the rotational tool grooves and evaluate their geometry statistically. A newly developed mean-feature approach decomposes the profile into a deterministic and a stochastic component. Wear-induced changes are almost entirely carried by the deterministic component, and within it by the trailing flank of the cutting groove. A comparison with confocal measurements confirms that the mean feature reconstructs the engaged cutting edge geometry, with the trailing-flank steepening attributable to notch wear on the secondary cutting edge. An exhaustive evaluation of more than 920,000 feature characterization combinations and multivariate models reveals that the groove-level mean maximum absolute gradient R_dt_groove alone explains 83-90

Contour scanning is commonly used in laser powder bed fusion (PBF-LB) to improve the geometrical accuracy and the surface roughness of additively manufactured parts. Nevertheless, the surface quality and geometrical accuracy is often insufficient for many applications. Machining as a post-processing operation enables the simultaneous improvement of these drawbacks. The machinability of additively manufactured parts differs from conventional cast material because of the unique microstructure caused by PBF-LB and can be further influenced by the PBF-LB process parameters. This study investigates the influence of contour scanning and different additive process parameters on the machining as a post-processing method and the resulting surface integrity. AlSi10Mg parts were manufactured in different orientations to the build-up directions (BUD) with no contour scanning, one contour scan, or five contour scans. The microstructure and residual stress state after additive manufacturing both depend on the BUD and the number of contour scans applied. Contour scanning reduced the surface roughness. However, applying several contours did not further improve the surface topography. Neither the BUD nor the contour scanning method significantly influenced the machining response during consecutive cuts. Machining altered the RS state of the additively manufactured parts. After machining, the RS state depended only on the BUD, not the contour strategy. The different PBF-LB parameters did seem to have no effect on the surface topography after machining.

This study investigates the effects of CO₂ mass flow rates on the surface integrity of cryogenically hard turned AISI 52100 roller bearing steel. Experimental analyses of process forces, process temperatures, roughness, residuals stress and geometric accuracy reveal that elevated CO₂ mass flows introduce process instabilities, particularly in residual stress distributions and process forces. While higher mass flow rates of CO2 reduce average cutting temperatures by up to 28%, localized thermal gradients promote inconsistent heat dissipation, leading to heterogeneous subsurface alterations. Residual stresses shift toward compressive states (up to -1400 MPa) with increasing mass flow rate, accompanied by fluctuations in process forces of up to 33% amplitude. Geometric deviations of < 5 μm cylindricity error occur revealing an optimum where the cooling is in balance with the heat generation during the machining process. Together with increasing surface roughness (Ra 0.29–0.44 μm) this underscores a trade-off between beneficial surface conditions and mechanical precision. The findings emphasize the need to optimize CO₂ mass flow within a critical threshold (0.1–2.5 kg/min in this setup) to balance cryogenic benefits of the surface layer against disadvantages in the dimensional accuracy. This work provides guidelines for stabilizing hard-turning processes in high-precision bearing manufacturing.

Understanding the wear behavior of coated cutting tools is essential for reliable tool life prediction and the implementation of intelligent tool condition monitoring. Tool wear influences not only process stability but also surface integrity and dimensional accuracy. In this study, AlTiN-coated indexable inserts were tested during longitudinal turning, with acoustic emission signals continuously recorded throughout the process. The wear progression was documented at discrete intervals using optical and microscopic analyses to capture characteristic wear forms and their development in dependence of tool life. The AE signals were segmented and analyzed using wavelet transformation and statistical feature extraction methods to describe both global signal evolution and locally occurring transient events. Correlation analyses were performed to identify dependencies between AE features and measured wear states. The results reveal distinct AE features that exhibit strong correlations with tool wear progression, indicating their potential as sensitive indicators of wear-induced changes in coated tools. The findings establish a data-driven basis for predictive tool condition monitoring using wear-sensitive acoustic emission characteristics.

In addition to the process parameters, the properties of the metal powder in laser based powder bed fusion affect the process stability and the resulting part properties. In recent years, the variety of commercially available metal powders for additive manufacturing has increased. Nowadays, various powders made from different atomization processes are available. This study investigates how the powder properties and processability differ for commercially available gas- and plasma-atomized AlSi10Mg powders by analyzing the powder and resulting part properties. It is shown that even though the powder properties are different, high relative densities can be achieved in the same processing window.

In laser-based powder bed fusion (PBF-LB), support structures are often necessary to ensure the dimensional accuracy of components. However, these supports are not part of the final product and must be removed after the additive manufacturing process. This is done through subtractive post-processing. This study investigates the milling-based removal of Rod and Wall support structures made from AlSi10Mg. The analysis focuses on the removal mechanisms, resulting chips, forces, and surface topography of the workpiece. The results reveal that the geometry of the support structures plays a crucial role in their removability. The Rods were significantly deformed or even broken off during milling. This was not observed in the Wall supports. Milling the Walls resulted in a more stable cutting process in comparison to the Rods. However, highly fluctuating forces and significant vibrations were observed during the milling of both Rod and Wall supports. These vibrations are caused by the low stiffness of the supports and by the discontinuous tooth engagement due to the geometry of the support structures. The vibrations can be reduced by using suitable cutting conditions. Up milling in combination with low feeds per tooth has considerably decreased vibrations. Low-stiffness structures such as Rods require carefully selected cutting conditions to minimize deformation and prevent the supports from breaking, which can compromise the surface quality.

Predicting the life of coated tools in high-performance machining requires a profound understanding of wear mechanisms, as tool wear directly affects tool life, process stability, and workpiece quality. Although advanced coatings can extend tool life, reliable prediction remains challenging due to complex thermo-mechanical and chemical interactions.In this study, systematic tool life experiments were conducted with coated indexable inserts when longitudinal turning of AISI 1045. Tool wear was characterized by means of the arising wear forms and analyzed using energy-dispersive X-ray spectroscopy to investigate wear progression. In addition, the machined surfaces were evaluated with respect to wear-induced changes in surface quality. To correlate wear characteristics and workpiece surface deterioration with in situ process data, cutting forces and acoustic emission were continuously measured. This approach enabled the identification of relevant wear indicators and provided insights into the time-dependent development of dominant wear mechanisms.The results reveal correlations between process signals, wear progression, and surface integrity. The investigation demonstrated that cutting forces provide reliable indicators for end of tool life detection, while acoustic emission exhibits sensitivity to wear mechanism transitions. The comprehensive dataset establishes an experimental basis for grey-box modeling approaches that integrate physical wear mechanisms with machine learning techniques.

In industrial practice, cutting tools are often changed early to maintain process reliability, which results in increased tool costs and unused potential. Machine learning (ML) based tool condition monitoring can be used to detect tool wear and reduce the risk of tool failure; facilitating longer tool operation times. However, these models have not found widespread use in industrial practice. Therefore, this paper presents an approach on how ML models can be integrated into the business models of tool manufacturers. It is shown how anomaly detection models and the prediction of remaining useful life serve as the foundation for this purpose.

Today, in industrial manufacturing coated cutting tools with geometrically defined cutting edges are predominantly used. These offer significantly better performance than uncoated tools, enabling more efficient and economical machining. Despite their technological relevance, there is still no reliable method for predicting the wear behavior of such tools, especially with regard to transient failure processes. To close this research gap, an in-depth understanding of the mechanisms of tool wear is to be developed.To this end, worn indexable inserts that were used in turning tests are analyzed fractographically and metallographically in various states of wear. To do so, the initial condition of the tools was characterized metallographically first. After the turning tests, the worn areas of the indexable inserts were documented using a scanning electron microscope and analyzed using energy-dispersive X-ray spectroscopy before several cross sections were made in different planes of the indexable inserts and evaluated under a light microscope and a scanning electron microscope with regard to the damage patterns. This allows the damage processes to be identified and the wear mechanisms to be better understood.

This study presents an innovative approach combining numerical simulations with experimental data to improve the accuracy of tool wear prediction. A hybrid modeling strategy is employed, integrating physics-based finite element method with data-driven machine learning techniques. Tool life experiments in turning operations were conducted, and a tool wear state-dependent finite element model was developed alongside an acoustic emissionbased extreme gradient boosting regression model. Cutting forces calculated through the finite element model were integrated into the machine learning model to enhance predictive performance. The results show that incorporating simulated process data significantly improves wear prediction capabilities and accuracy compared to purely data-driven models. This demonstrates the potential of hybrid modeling approaches, so called grey-box, to bridge the gap between physical process understanding and machine learning predictions, minimizing the need for extensive experimental data collection. Furthermore, this approach reduces the dependency on expensive measurement technologies by substituting real measurement data with simulated data. By leveraging these advancements, this research contributes to the development of a robust and reliable tool wear prediction system, which not only improves manufacturing efficiency but also reduces operational costs in the future.

Porous metals combine favorable mechanical, electrical, thermal, and acoustic properties. These properties are especially desirable for lightweight components. However, in order to realize the full potential of this material group, it is essential to develop techniques for combining them with solid parts, for example in the form of a shell-core principle. Directed Energy Deposition (DED) can be used to add solid material on top of an existing porous metal part. Due to the uneven surface topography of porous metals, several problems arise regarding a suitable manufacturing strategy.This study analyses the effect of laser remelting (LR), DED, and the combination of them on a porous surface and the deposited material. Optical micrographs were studied to evaluate the bonding to the substrate. Primary profile roughness parameters proved to be suitable to quantify the effect of the different processes on the surface topography. The combination of LR and DED led to the best results regarding the surface topography but showed bonding problems between the LR zone and the deposited material. The study also shows further potential to improve the surface topography, and the bonding of deposited material on porous metals via the optimization of LR and DED strategies.

Due to the increasing global challenges, the demand for sustainable, resource- and energy-efficient manufacturing processes is growing. In particular, functional surfaces need increased performance due to the expected load in later use. This includes adapted surface and subsurface properties such as increased hardness to ensure resistance to given stresses. Heat treatment is typically carried out to adjust the material hardness. This means an additional process step, which is associated with additional costs and energy expenditure. Cryogenic processing offers the advantage of being able to adjust the required subsurface properties locally during the manufacturing of a workpiece. In this study, the influence of different cryogenic manufacturing technologies on the resulting surface and subsurface properties is analyzed, taking into account the importance of dimensional accuracy. A comparison is made between in-process CO2 cooling and LN2 pre-cooling.

While the geometric accuracy of parts produced with laser-based powder bed fusion (PBF-LB) is high compared to other laser metal additive manufacturing processes, the build-up rate can be a limiting factor for a broader industrial application. To achieve higher build-up rates either the process parameters or the machine can be optimized. The process parameters scanning speed and layer thickness can be optimized for maximum efficiency. However, this optimization is limited to a small processing window to ensure a sufficient part quality and increasing the layer height reduces the geometric accuracy of the part. Another process parameter that can also improve the build-up rate is the variation of the laser spot diameter. Similar to adjusting the layer thickness, increasing the laser spot diameter comes at the expense of the geometric accuracy. To overcome this issue, different processing parameters can be assigned to specific feature requirements within one part: Features with high geometrical accuracy are produced with small laser spot diameters and small layer thicknesses, features with low geometric requirements are produced with high laser spot diameters or high layer thicknesses to maximize the build-up rate. The approach investigated in this study applies two different strategies to implement feature specific processing parameters (FEAPS): the variation along and perpendicular to the build-up direction. To ensure full bonding between different FEAPS in one layer the part specific process parameters need to be optimized from the standard settings in the machine software. In this study the laser beam offset was adapted to ensure full bonding between the different areas. The FEAPS samples were characterized in terms of surface topography, porosity, microstructure and microhardness. The bonding zone showed no significant variation of the microstructure and microhardness because of the specific properties of PBF-LB Ti-6Al-4V.

Cryogenic minimum quantity lubrication (MQL) offers the potential to combine high cooling capacities, advantageous lubrication effects and a significant reduction regarding resource consumption. However, due to the wide range of machining applications and different load cases, an efficient design of the respective supply strategy is comparatively difficult. In this study, a tribological approach is presented that enables a characterization of the cooling and lubrication properties in dependence of the supply strategy and two representative load cases. The results show that the tribological setup is well suited to evaluate the occurring thermomechanical loads in dependence of the cryogenic MQL strategies applied.

High-speed directed energy deposition via laser beam (HS-DED-LB) represents a novel powder-based approach for the additive manufacturing of metals. By significantly increasing the feed rate compared to conventional processes, a more economical production can be achieved. Within this study, the influence of different laser beam profiles (Gaussian and Top-Hat) on the resulting properties of 316L stainless steel specimens made by HS-DED-LB was investigated. All parameters except the laser beam profiles were held constant. The manufactured specimens were analyzed regarding their microstructure, microhardness, and relative density. Due to the different laser beam profiles, different melt pool geometries existed, which resulted in variances of the specimen's properties. (c) 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Abstract Compared to uncoated tools, coated tools offer a longer tool life. However, it is difficult to predict the wear of coated tools using only empirical methods. The combination of deterministic whitebox models and data-driven blackbox models to greybox models can enable a prediction of the wear condition. Therefore, this article presents a concept for the application of greybox models for wear prediction.

Abstract Novel hybrid porous materials enable the local variation of component properties to meet individual application requirements. To realize complex component geometries, laser directed energy deposition can be used as an additive manufacturing process to produce multifunctional high-performance components made of hybrid porous materials. This work presents results from the manufacturing of functionally graded materials from the stainless steels 316 L (austenitic) and 17-4 PH (martensitic) for this purpose. A grading strategy with a 50 % 316 L and 50 % 17-4 PH interlayer was successfully manufactured and characterized in terms of relative density and microhardness.

Micro milling is a very flexible micro cutting process widely deployed to manufacture miniaturized parts. However, size effects occur when downscaling the cutting processes. They lead to higher mechanical loads on the tools and therefore increased tool wear. Micro milling tools are usually made of cemented carbides due to their mechanical strength and fine grain structure. Technical ceramics as alternative tool materials offer very good mechanical properties as well, with grain sizes well below 1 m. In conventional machining, they have proven to be able to reduce tool wear. To transfer these wear improvements to the micro scale, we manufactured all-ceramic micro end mills in previous studies ( ∅ 50 and ∅ 100 m). Tools made from zirconia (Y-TZP) showed the sharpest cutting edges, and were the best performing in micro milling trials amongst the substrates tested. However, the advantages of the ceramic substrate could not be utilized for the brass and titanium materials tested in those studies. Therefore, in this study the capabilities of all-ceramic micro end mills ( ∅ 50 m) in different workpiece materials (1.4404, 1.7225, 3.1325 and PMMA GS) were researched. For the two steels and the aluminum alloy, the ceramic tools did not offer an improvement over the cemented carbide tools used as reference. For the thermoplastic PMMA however, significant improvements could be achieved by utilizing the Y-TZP ceramic tools: Less tool wear, less and more stable cutting forces, and higher surface qualities.

Cryogenic turning of metastable austenitic stainless steels can improve wear resistance of the resulting surface due to the phase transformation of gamma-austenite into alpha'- and/or & varepsilon;-martensite in the near surface layer. By using a cryogenic two-step turning process, the amount of deformation-induced alpha'-martensite in the subsurface regime can be further increased. To determine the influence of the implemented and optimized two-step turning strategy on the tribological properties of countersurfaces for radial shaft seals, an evaluation of wear behavior of the shaft seal countersurface as well as microstructural analyses in subsurface regime is presented and compared to the cryogenic single step turning process. The results show that not only the integral phase transformation in the overall subsurface region, but also the local phase distribution plays an important role when it comes to the surface performance in tribological applications. Das kryogene Drehen von metastabilen austenitischen rostfreien St & auml;hlen kann die Verschlei ss festigkeit der resultierenden Oberfl & auml;che aufgrund der Phasenumwandlung von gamma-Austenit nach alpha'- und/oder & varepsilon;-Martensit im oberfl & auml;chennahen Bereich verbessern. Durch den Einsatz eines zweistufigen kryogenen Drehprozesses kann der verformungsinduzierte alpha'-Martensitgehalt weiter erh & ouml;ht werden. Um den Einfluss des implementierten und optimierten zweistufigen kryogenen Drehprozesses auf die tribologischen Eigenschaften von Gegenlauffl & auml;chen f & uuml;r Radialwellendichtringe zu ermitteln, werden die Verschlei ss untersuchungen der Wellendichtring-Gegenlauffl & auml;che sowie die Mikrostrukturanalysen in den oberfl & auml;chennahen Bereichen durchgef & uuml;hrt und mit der einstufigen Drehstrategie verglichen. Die Ergebnisse zeigen, dass nicht nur die integrale Phasenumwandlung in der gesamten Einflusszone, sondern auch die lokale Phasenverteilung eine wichtige Rolle f & uuml;r die Oberfl & auml;chenqualit & auml;t in tribologischen Anwendungen spielen. Metastable austenitic steels exhibit excellent wear resistance despite their lower hardness compared to reference carbon steels. In this study, the local distribution of deformation-induced martensite is more important than the integral volume fraction in determining wear resistance, which provides valuable insights into the tribological behavior of the radial shaft seal system. image

Milled thin-walled monolithic aluminum structural parts are widely used in the aerospace industry due to their appropriate properties such as a high overall strength-to-weight ratio. The semifinished products made of aluminum alloy 7050 undergo a heat treatment to gain this increased strength and hardness: Typically, three steps including solution heat treatment, quenching, and age hardening are carried out. This also leads to high initial bulk residual stresses (IBRS) within the part in the range of ±200 MPa. In rolled aluminum plate, plastic stretch (T7451 heat treatment designation) provides very effective relief of residual stress, decreasing IBRS to a level of ±20 MPa. In large parts with low bending stiffness, even that low level of IBRS can cause appreciable distortion. Besides the IBRS, the machining-induced residual stresses (MIRS) contribute to the distortion. This study investigates how IBRS in different stress relieved 7050-T7451 semifinished products vary and how this variation affects the distortion of milled thin-walled monolithic structural parts. A linear elastic finite element distortion prediction model, which considers the IBRS as well as the MIRS as input, was used to analyze the effect of varying IBRS on the distortion for different part sizes and geometries. The model was validated by machining of those parts and measuring their distortion. IBRS were measured via slitting technique and MIRS via incremental hole-drilling.