Sammlung_FB 15

Every new material needs to be assessed and qualified for an envisaged application. A steadily increasing number of new alloys, designed to address challenges in terms of reliability and sustainability, poses significant demands on well-known analysis methods in terms of their efficiency, e.g., in X-Ray diffraction analysis. Particularly in laboratory measurements, where the intensities in diffraction experiments tend to be low, a possibility to adapt the exposure time to the prevailing boundary conditions, i.e., the investigated microstructure, is seen to be a very effective approach. The counting time is decisive for, e.g., complex texture, phase, and residual stress measurements. Traditionally, more measurement points and, thus, longer data collection times lead to more accurate information. Here, too short counting times result in poor signal-to-background ratios and dominant signal noise, respectively, rendering subsequent evaluation more difficult or even impossible. Then, it is necessary to repeat experiments with adjusted, usually significantly longer counting time. To prevent redundant measurements, it is state-of-the-art to always consider the entire measurement range, regardless of whether the investigated points are relevant and contribute to the subsequent materials characterization, respectively. Obviously, this kind of approach is extremely time consuming and, eventually, not efficient. All relevant data including the code are carefully assessed and will be the basis for a widely adapted strategy enabling efficient measurements not only in lab environments but also large scale facilities.

This data set consists of the fully measured data from the diffraction experiments as well as the manuscript for data analyzing.

IMPORTANT: In case you use the data please cite our corresponding article: https://doi.org/10.1038/s41598-025-96221-1

Description of the dataset:

Hard-turning experiments were conducted on cylindrical specimens made of a quenched and tempered (Q&T) steel 51CrV4
in different initial surface hardness levels, i. e. 400 HV30, 500 HV30 and 600 HV30. Prior to heat treatment,
all specimens were manufactured with the same roughness requirements in order to ensure an almost identical surface
finish before hard-turning. Three different sections of nine specimen of each hardness level
(i.e. in total 27 section-specific areas per hardness level) were machined with varied cutting parameters feed rate
(f), depth of cut (a_p) and cutting speed (v_c).
Hard-turning of the specimens was carried out on a servo-conventional lathe of type Weiler C30 using
polycrystalline boron nitride (PCBN) inserts with a corner radius of 0.8 mm.

After hard-turning, residual stress depth profiles were determined using X-ray diffraction (XRD),
respectively for each specific area of a specimen. Residual stress measurements were conducted using a
Pulstec μ-X360 diffractometer equipped with a 0.3 mm collimator and CrKα-radiation with an exposure time
of 120 sec. Depth profiles were determined by successive removal of the material surface layer using
electro-chemical polishing. The obtained data have been evaluated applying the cos α-method without consideration
of any mathematical stress correction.

Post process surface roughness in axial direction was determined using a Mitutoyo SJ-210 tactile roughness
measuring device. Post process Vickers hardness testing was carried out using a Struers DuraScan-70 system
employing a load of 294.2 N (HV30).
For more detailed information on the material and the experimental setup, i.e., chemical composition, specimen
geometry, machines and parameters used for hard-turning operations and post process measurements, the reader is
referred to [1].

A full factorial experiment design was used with two levels for the initial hardness levels H_Vinit and three
factors for the cutting parameters, f: {0.05 mm, 0.25 mm, 0.5 mm}, v_c: {100 m/min, 175 m/min, 250 m/min},
a_p: {0.05 mm, 0.25 mm, 0.5 mm}, resulting in 81 different combinations. For the residual stress depth profile
modeling, measurements for 12 different depths are available:
{0, 10, 20, 30, 40, 50, 60, 80, 100, 120, 150, 200} μm, providing for N = 972 samples in total.

Two data sets are provided:
1. "hardness_roughness_hard_turning.csv" with the 81 data points for the surface hardness and roughness measurements.
2. "residual_stress_profile_hard_turning.csv" with the 972 data points for the tangential residual stress depth profiles.

[1]     Thomas Wegener, Alexander Liehr, Artjom Bolender, Sebastian Degener, Felix Wittich, Andreas Kroll, & Thomas Niendorf,
        "Calibration and validation of micromagnetic data for non-destructive analysis of near-surface properties
        after hard turning" in HTM Journal of Heat Treatment and Materials, 2022, 77(2), 156-172.

The associated paper explores the trilemma between different circular economy stakeholders (customer, manufacturer and legislator). The overview database (2019-2024) contains 84 relevant papers (out of 880), which were reduced to 27 through analysis. In addition, findings from expert interviews are used to uncover conflicts and synergies.

The provided Excel file contains the researched literature and documents the process and findings. The main content is found in "References". Each PDF contains an interview protocol, numbered according to the paper’s Table 3. Each protocol contains a description of the expert and a discussion summary.

The provided presentation was used as a guide to conduct the interviews.

Ultra-high-performance concrete (UHPC) possesses mechanical characteristics that significantly outperform traditional concrete. However, replicating these properties consistently across different production batches—even when using the same recipe—remains a challenge.

This dataset examines how variations in raw materials, environmental conditions, dosage variation, and both mixing and curing practices affect the mechanical properties of UHPC produced from a single reference formulation. Designed according to a three-phase design of experiments methodology, the dataset comprises 150 systematically planned experiments, offering a comprehensive view of the multiple factors influencing UHPC quality. Measurements of compressive and flexural strengths are provided at 24 hours and after 28 days post-mixing. Beside the mechanical properties, the dataset includes five characteristics of the fresh state, measured directly after each mixing process.

All experiments are conducted in the laboratory of G.tec Engineering GmbH under controlled conditions, using the same mixer, same mixing tool, and the same team of technicians. The environment is maintained at a constant temperature of 20 °C throughout the experimental process.

From each experiment, three specimens are cured under the designed conditions. First, the flexural strength is measured by carefully halving each of the three specimens. Then, the resulting six halves are used to measure the compressive strength. Finally, after a careful analysis of the results from each specimen, the averages for flexural and compressive strengths are reported.

The dataset also includes outliers. After analysis by UHPC experts and the data science team, 11 data points (numbered 5, 17, 30, 36, 41, 47, 57, 99, 101, 128, and 148) were identified and removed as outliers to assure data quality.

By offering a structured collection of high-dimensional data and a relatively small data size, this dataset is particularly suitable for advanced regression analyses, notably those addressing sparse data scenarios.