Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges

Most machine learning algorithms are configured by a set of hyperparameters whose values must be carefully chosen and which often considerably impact performance. To avoid a time-consuming and irreproducible manual process of trial-and-error to find well-performing hyperparameter configurations, various automatic hyperparameter optimization (HPO) methods—for example, based on resampling error estimation for supervised machine learning—can be employed. After introducing HPO from a general perspective, this paper reviews important HPO methods, from simple techniques such as grid or random search to more advanced methods like evolution strategies, Bayesian optimization, Hyperband, and racing. This work gives practical recommendations regarding important choices to be made when conducting HPO, including the HPO algorithms themselves, performance evaluation, how to combine HPO with machine learning pipelines, runtime improvements, and parallelization. This article is categorized under: Algorithmic Development > Statistics Technologies > Machine Learning Technologies > Prediction

Tribological study on tailored-formed axial bearing washers

To enhance tribological contacts under cyclic load, high performance materials are required. Utilizing the same high-strength material for the whole machine element is not resource-efficient. In order to manufacture machine elements with extended functionality and specific properties, a combination of different materials can be used in a single component for a more efficient material utilization. By combining different joining techniques with subsequent forming, multi-material or tailored components can be manufactured. To reduce material costs and energy consumption during the component service life, a less expensive lightweight material should be used for regions remote from the highly stressed zones. The scope is not only to obtain the desired shape and dimensions for the finishing process, but also to improve properties like the bond strength between different materials and the microscopic structure of the material. The multi-material approach can be applied to all components requiring different properties in separate component regions such as shafts, bearings or bushes. The current study exemplarily presents the process route for the production of an axial bearing washer by means of tailored forming technology. The bearing washers were chosen to fit axial roller bearings (type 81212). The manufacturing process starts with the laser wire cladding of a hard facing made of martensitic chromium silicon steel (1.4718) on a base substrate of S235 (1.0038) steel. Subsequently, the bearing washers are forged. After finishing, the surfaces of the bearing washers were tested in thrust bearings on an FE-8 test rig. The operational test of the bearings consists in a run-in phase at 250 rpm. A bearing failure is determined by a condition monitoring system. Before and after this, the bearings were inspected by optical and ultrasonic microscopy in order to examine whether the bond of the coat is resistant against rolling contact fatigue. The feasibility of the approach could be proven by endurance test. The joining zone was able to withstand the rolling contact stresses and the bearing failed due to material-induced fatigue with high cycle stability

Numerical simulation and experimental validation of the cladding material distribution of hybrid semi-finished products produced by deposition welding and cross-wedge rolling

The service life of rolling contacts is dependent on many factors. The choice of materials in particular has a major influence on when, for example, a ball bearing may fail. Within an exemplary process chain for the production of hybrid high-performance components through tailored forming, hybrid solid components made of at least two different steel alloys are investigated. The aim is to create parts that have improved properties compared to monolithic parts of the same geometry. In order to achieve this, several materials are joined prior to a forming operation. In this work, hybrid shafts created by either plasma (PTA) or laser metal deposition (LMD-W) welding are formed via cross-wedge rolling (CWR) to investigate the resulting thickness of the material deposited in the area of the bearing seat. Additionally, finite element analysis (FEA) simulations of the CWR process are compared with experimental CWR results to validate the coating thickness estimation done via simulation. This allows for more accurate predictions of the cladding material geometry after CWR, and the desired welding seam geometry can be selected by calculating the cladding thickness via CWR simulation. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Influence of degree of deformation on welding pore reduction in high-carbon steels

Locally adapted properties within a machine component offer opportunities to increase the performance of a component by using high strenght materials where they are needed. The economic production of such hybrid components on the other hand represents a major challenge. The new tailored forming process chain, which is developed within the collaborative research center (CRC 1153) represents a possible solution to produce hybrid components. This is made possible by the use of pre-joined hybrid semi-finished products made from two different steel alloys, which are subsequently formed. The semi-finished products can be manufactured for example by means of deposition welding. Due to a thermal mechanical treatment, an overall higher component strength of the joining zone can be achieved. The deposition welding processes can be used to generate a cladding on a base material. During the welding, one of the most difficult tasks is to reduce the amount and size of pores in the joining zone. These pores can reduce the strength in the joining zone of the welded parts. However, additional pores can occur in the intermediate zone between the substrate and the cladding. In the presented study, the influence of the forming process on the closing of pores in the cladding and in the intermediate zone was investigated. Therefore, cylindrical specimen were extracted in longitudinal direction of the welding track by wire-cut eroding. These welding tracks are manufactured by plasma-transferred arc welding of AISI 52100 on a base plate made of AISI 1015. Further, specimens were prepared transversely, so that the base material, the intermediate layer, and the welded material are axially arranged in the specimen. The prepared specimen were checked for pores by means of scanning acoustic microscopy. Subsequently, an uniaxial compression test was carried out with various degrees of deformation and the two specimen designs were examined again for pores. A microstructure analysis was carried out after each step. The investigations show that there is a need for a minimum degree of deformation to reduce pores in the welded material. However, this required plastic strain cannot be achieved in the welded material of the hybrid specimen, which is a result of the homogeneous temperature distribution in the specimen. The homogeneous temperature distribution leads to different flow properties in the specimen, which means that the main plastic deformation is taking place in the base material. © 2021, The Author(s)

Machine Learning for the Educational Sciences

Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non-linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows, as larger and more complex datasets become available through massive open online courses (MOOCs) and large-scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. To provide educational researchers with an elaborate introduction to the topic, we provide an instructional summary of the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. Specifically, we (1) provide an overview of the types of data suitable for ML and (2) give practical advice for the application of ML methods. In each section, we provide analytical examples and reproducible R code. Also, we provide an extensive Appendix on ML-based applications for education. This instructional summary will help educational scientists and practitioners to prepare for the promises and threats that come with the shift towards digitisation and large-scale assessment in education

Machine Learning for the Educational Sciences

Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytical approaches of this millennium and has recently become popular in the behavioral and social sciences. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows as larger and more complex datasets become available through massive open online courses (MOOCs) and large scale investigations. The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. Here, we review the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. In this review, we (1) provide an overview of the types of data suitable for ML, (2) give practical advice for the application of ML methods, and (3) show how ML-based tools and applications can be used to enhance the quality of education. Additionally we provide practical R code with exemplary analyses, available at https: //osf.io/ntre9/?view only=d29ae7cf59d34e8293f4c6bbde3e4ab2

Predicting instructed simulation and dissimulation when screening for depressive symptoms

The intentional distortion of test results presents a fundamental problem to self-report-based psychiatric assessment, such as screening for depressive symptoms. The first objective of the study was to clarify whether depressed patients like healthy controls possess both the cognitive ability and motivation to deliberately influence results of commonly used screening measures. The second objective was the construction of a method derived directly from within the test takers' responses to systematically detect faking behavior. Supervised machine learning algorithms posit the potential to empirically learn the implicit interconnections between responses, which shape detectable faking patterns. In a standardized design, faking bad and faking good were experimentally induced in a matched sample of 150 depressed and 150 healthy subjects. Participants completed commonly used questionnaires to detect depressive and associated symptoms. Group differences throughout experimental conditions were evaluated using linear mixed-models. Machine learning algorithms were trained on the test results and compared regarding their capacity to systematically predict distortions in response behavior in two scenarios: (1) differentiation of authentic patient responses from simulated responses of healthy participants; (2) differentiation of authentic patient responses from dissimulated patient responses. Statistically significant convergence of the test scores in both faking conditions suggests that both depressive patients and healthy controls have the cognitive ability as well as the motivational compliance to alter their test results. Evaluation of the algorithmic capability to detect faking behavior yielded ideal predictive accuracies of up to 89%. Implications of the findings, as well as future research objectives are discussed. Trial Registration The study was pre-registered at the German registry for clinical trials (Deutsches Register klinischer Studien, DRKS; DRKS00007708)