Combinations Of Parameter Values Research Articles

Pathogenesis of multiple sclerosis: genetic, environmental and random mechanisms

BackgroundThe pathogenesis of multiple sclerosis (MS) requires both genetic factors and environmental events. The question remains, however, whether these factors and events completely describe the MS disease process. This question...

Long-term evolution of spin and other properties of neutron star low-mass X-ray binaries: implications for millisecond X-ray pulsars

ABSTRACT A neutron star (NS) accreting matter from a companion star in a low-mass X-ray binary (LMXB) system can spin up to become a millisecond pulsar (MSP). Properties of many such MSP systems are known, which is excellent for probing fundamental aspects of NS physics when modelled using the theoretical computation of NS LMXB evolution. Here, we systematically compute the long-term evolution of NS, binary, and companion parameters for NS LMXBs using the stellar evolution code mesa. We consider the baryonic to gravitational mass conversion to calculate the NS mass evolution and show its cruciality for the realistic computation of some parameters. With computations using many combinations of parameter values, we find the general nature of the complex NS spin frequency ($\nu$) evolution, which depends on various parameters, including accretion rate, fractional mass-loss from the system, and companion star magnetic braking. Further, we utilize our results to precisely match some main observed parameters, such as $\nu$, orbital period ($P_{\rm orb}$), etc., of four accreting millisecond X-ray pulsars (AMXPs). By providing the $\nu$, $P_{\rm orb}$, and the companion mass spaces for NS LMXB evolution, we indicate the distribution and plausible evolution of a few other AMXPs. We also discuss the current challenges in explaining the parameters of AMXP sources with brown dwarf companions and indicate the importance of modelling the transient accretion in LMXBs as a possible solution.

Intelligent Location-Routing for Sustainable Reverse Supply Chain of End-of-Life Vehicles Considering Awareness Cost and Carbon Penalty

This paper presents a comprehensive investigation into optimizing the reverse supply chain for end-of-life vehicles (ELVs) with a focus on sustainability considerations, including awareness cost and carbon penalty. A novel three-objective mathematical model is developed, leveraging fuzzy logic to address the complex nature of the problem. Two multi-objective algorithms, the Grey Wolf Optimizer (GWO) based on the Pareto boundary and the NSGA-II algorithm, are deployed to solve the model. A case study in China is conducted to validate the proposed model, with results compared against the outcomes of the algorithms. Additionally, a sensitivity analysis approach is employed to assess the impact of awareness cost and carbon penalty parameters. Furthermore, the Taguchi experimental design method is utilized to identify the optimal combination of parameter values. The findings reveal that the GWO algorithm surpasses NSGA-II in terms of solution quality and diversity, although NSGA-II demonstrates superior performance in uniformity and computational time. The sensitivity analysis highlights the positive correlation between increased awareness cost and various performance metrics, such as the number of collected vehicles, economic profit, and social profit, while also indicating a reduction in environmental impacts. Conversely, escalating carbon penalties leads to a decrease in the acceptance and processing of vehicles within the supply chain, resulting in diminished chain and social profits, despite the decrease in carbon penalties.

Contextual Acceptance of COVID-19 Mitigation Mobile Apps in the United States: Mixed Methods Survey Study on Postpandemic Data Privacy.

The COVID-19 pandemic gave rise to countless user-facing mobile apps to help fight the pandemic ("COVID-19 mitigation apps"). These apps have been at the center of data privacy discussions because they collect, use, and even retain sensitive personal data from their users (eg, medical records and location data). The US government ended its COVID-19 emergency declaration in May 2023, marking a unique time to comprehensively investigate how data privacy impacted people's acceptance of various COVID-19 mitigation apps deployed throughout the pandemic. This research aims to provide insights into health data privacy regarding COVID-19 mitigation apps and policy recommendations for future deployment of public health mobile apps through the lens of data privacy. This research explores people's contextual acceptance of different types of COVID-19 mitigation apps by applying the privacy framework of contextual integrity. Specifically, this research seeks to identify the factors that impact people's acceptance of data sharing and data retention practices in various social contexts. A mixed methods web-based survey study was conducted by recruiting a simple US representative sample (N=674) on Prolific in February 2023. The survey includes a total of 60 vignette scenarios representing realistic social contexts that COVID-19 mitigation apps could be used. Each survey respondent answered questions about their acceptance of 10 randomly selected scenarios. Three contextual integrity parameters (attribute, recipient, and transmission principle) and respondents' basic demographics are controlled as independent variables. Regression analysis was performed to determine the factors impacting people's acceptance of initial data sharing and data retention practices via these apps. Qualitative data from the survey were analyzed to support the statistical results. Many contextual integrity parameter values, pairwise combinations of contextual integrity parameter values, and some demographic features of respondents have a significant impact on their acceptance of using COVID-19 mitigation apps in various social contexts. Respondents' acceptance of data retention practices diverged from their acceptance of initial data sharing practices in some scenarios. This study showed that people's acceptance of using various COVID-19 mitigation apps depends on specific social contexts, including the type of data (attribute), the recipients of the data (recipient), and the purpose of data use (transmission principle). Such acceptance may differ between the initial data sharing and data retention practices, even in the same context. Study findings generated rich implications for future pandemic mitigation apps and the broader public health mobile apps regarding data privacy and deployment considerations.

Looking for Stability in Proof-of-Stake based Consensus Mechanisms

The Proof-of-Stake (PoS) consensus algorithm has been criticized, in the literature and in several cryptocurrencies communities, due to the so-called compounding effect: who is richer has more coins to stake, therefore higher probability of being selected as a block validator and obtaining the corresponding rewards, thus becoming even richer. In this paper, we present a PoS simulator written in the Julia language that allows one to test several variants of PoS-based consensus algorithms, tweaking their parameters, and observe how the distribution of cryptocurrency coins among the users evolves over time. Such a tool can be used to investigate which combinations of parameters values allow to obtain a “fair” and stable consensus algorithm, in which, over the long term, no one gets richer or poorer by the mere act of validating blocks. Based on this investigation, we also introduce a new PoS-based consensus mechanism that allows the system to keep the wealth distribution stable even after a large number of epochs.

Explaining the impact of parameter combinations in agent-based models

Simulation is a useful and effective way to analyze and study complex, real-world systems, allowing researchers, practitioners, and decision makers to make sense of the inner working of a system involving many factors, often resulting in some sort of emergent behavior. The number of parameter value combinations grows exponentially and it quickly becomes infeasible to test them all or even to explore a suitable subset. How does one then efficiently identify the parameter value combinations that matter for a particular simulation study, and determine their impact on the result? In addition, is it possible to train a machine learning model to predict the outcome of an agent-based model (ABM) with a systematically chosen small subset of parameter value combinations, such that the result could be predicted without running the ABM? We use covering arrays to create t-way (t = 2, 3, etc.) combinations of parameter values to significantly reduce an ABM’s parameter value exploration space, which is supported by our prior work. In our ICCS 2023 paper (Olsen et al., 2023) we built on that work by applying it to Wilensky’s Heatbugs model and training a random forest machine learning model to predict simulation results by using the covering arrays to select our training and test data. Our results show that a 2-way covering array provides sufficient training data to train our random forest to predict three different simulation outcomes. Our process of using covering arrays to decrease parameter space to then predict ABM results using machine learning is successful. In this paper that extends the ICCS 2023 paper (Olsen et al., 2023), we analyze the role of parameter combinations and parameter values in determining model output via combination frequency difference (CFD) analysis and Shapley values. CFD has not previously been applied to agent-based models; we provide a process for using this approach and compare and contrast with Shapley values and random forest feature importance.

Two improving approaches for faulty interaction localization using logistic regression analysis

Faulty Interaction Localization (FIL) is a process to identify which combination of input parameter values induced test failures in combinatorial testing. An accurate and fast FIL provides helpful information to fix defects causing the test failure. One type of conventional FIL approach, which analyzes test results of whole test cases and estimates the suspiciousness of each combination, has two main concerns; (1) the accuracy is not enough, (2) the huge time cost is sometimes needed. In this paper, we propose two novel approaches to improve those concerns. FROGa attempts to estimate suspiciousness more accurately using logistic regression analysis. FROGb attempts to estimate failure-inducing combinations at high speed by estimating the subsets of them using logistic regression analysis and exploring just their supersets. Through evaluation experiments using a large number of artificial test results based on several real software systems, we observed that FROGa has very high accuracy, and FROGb can drastically reduce time cost for targets that have been difficult to complete by the conventional method.

Revised clusters of annotated unknown sounds in the Belgian part of the North sea

Acoustic signals, especially those of biological source, remain unexplored in the Belgian part of the North Sea (BPNS). The BPNS, although dominated by anthrophony (sounds from human activities), is expected to be acoustically diverse given the presence of biodiverse sandbanks, gravel beds and artificial hard structures. Under the framework of the LifeWatch Broadband Acoustic Network, sound data have been collected since the spring of 2020. These recordings, encompassing both biophony, geophony and anthrophony, have been listened to and annotated for unknown, acoustically salient sounds. To obtain the acoustic features of these annotations, we used two existing automatic feature extractions: the Animal Vocalization Encoder based on Self-Supervision (AVES) and a convolutional autoencoder network (CAE) retrained on the data from this study. An unsupervised density-based clustering algorithm (HDBSCAN) was applied to predict clusters. We coded a grid search function to reduce the dimensionality of the feature sets and to adjust the hyperparameters of HDBSCAN. We searched the hyperparameter space for the most optimized combination of parameter values based on two selected clustering evaluation measures: the homogeneity and the density-based clustering validation (DBCV) scores. Although both feature sets produced meaningful clusters, AVES feature sets resulted in more solid, homogeneous clusters with relatively lower intra-cluster distances, appearing to be more advantageous for the purpose and dataset of this study. The 26 final clusters we obtained were revised by a bioacoustics expert. We were able to name and describe 10 unique sounds, but only clusters named as ‘Jackhammer’ and ‘Tick’ can be interpreted as biological with certainty. Although unsupervised clustering is conventional in ecological research, we highlight its practical use in revising clusters of annotated unknown sounds. The revised clusters we detailed in this study already define a few groups of distinct and recurring sounds that could serve as a preliminary component of a valid annotated training dataset potentially feeding supervised machine learning and classifier models.

Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes

Abstract. The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining “the right answers for the wrong reasons”, compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (Davis et al., 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs.

Stroke Prediction with Enhanced Gradient Boosting Classifier and Strategic Hyperparameter

A stroke is a medical condition that occurs when the blood supply to the brain is interrupted. Stroke can cause damage to the brain that can potentially affect a person's function and ability to move, speak, think, and feel normally. The effect of stroke on health emphasizes the importance of stroke detection, so an effective model is needed in predicting stroke. This research aimed to find a new approach that can improve the performance of stroke prediction by comparing four derivative algorithms from Gradient Boosting by adding hyperparameters tuning. The addition of hyperparameters was used to find the best combination of parameter values that can improve the model accuracy. The methods used in this research were Categorical Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Extreme Gradient Boosting. The research involved retrieving, cleaning, and analyzing data and then the model performance was evaluated with a confusion matrix and execution time. The results obtained were Light Gradient Boosting with Hyperparameter RandomSearchCV achieved the highest accuracy at 95% among the algorithms tested, while also being the fastest in execution. The contribution of this research to the medical field can help doctors and patients predict the occurrence of stroke early and reduce serious consequences.

A Multifactor Combination Optimization Design Based on Orthogonality for a Two-Degree-of-Freedom Floating Machine Gun Vibration System

This paper introduces a novel type of floating machine gun that can be simplified as a self-balancing two-degree-of-freedom mechanical system with distinct vibration characteristics. The model accounts for intricate motion patterns and encompasses numerous potential influencing factors. Multifactor combination optimization of the system represents a pressing engineering challenge. After establishing a simulation model for the machine gun and validating it experimentally, seven factors were chosen as optimization variables. The maximum recoil displacement of the inner receiver (MRD) and the firing rate were chosen to be indicators. Orthogonal combinations and variance analyses were used, and the effects of multiple factors were analyzed using SPSS software; these processes led to a determination of the optimal combination. The results indicated that the piston cylinder pressure, the bi-directional buffer spring energy storage, and the inner receiver mass significantly affected the MRD. Furthermore, the automaton mass and the reset spring energy storage were found to substantially affect the firing rate. Careful analysis of the variance results facilitated the determination of the optimal combination of parameter values. Remarkably, the optimal combination chosen resulted in an MRD reduction of approximately 20.2% and a firing rate increase of approximately 26.6%.

On the interaction between the search parameters and the nature of the search problems in search‐based model‐driven engineering

AbstractThe use of search‐based software engineering to address model‐driven engineering activities (SBMDE) is becoming more popular. Many maintenance tasks can be reformulated as a search problem, and, when those tasks are applied to software models, the search strategy has to retrieve a model fragment. There are no studies on the influence of the search parameters when applied to software models. This article evaluates the impact of different search parameter values on the performance of an evolutionary algorithm whose population is in the form of software models. Our study takes into account the nature of the model fragment location problems (MFLPs) in which the evolutionary algorithm is applied. The evaluation searches 1895 MFLPs (characterized through five measures that define MFLPs) from two industrial case studies and uses 625 different combinations of search parameter values. The results show that the impact on the performance when varying the population size, the replacement percentage, or the crossover rate produces changes of around 30% in performance. With regard to the nature of the problems, the size of the search space has the largest impact. Search parameter values and the nature of the MFLPs influence the performance when applying an evolutionary algorithm to perform fragment location on models. Search parameter values have a greater effect on precision values, and the nature of the MFLPs has a greater effect on recall values. Our results should raise awareness of the relevance of the search parameters and the nature of the problems for the SBMDE community.

Generating Edge Cases for Testing Autonomous Vehicles Using Real-World Data.

In the past decade, automotive companies have invested significantly in autonomous vehicles (AV), but achieving widespread deployment remains a challenge in part due to the complexities of safety evaluation. Traditional distance-based testing has been shown to be expensive and time-consuming. To address this, experts have proposed scenario-based testing (SBT), which simulates detailed real-world driving scenarios to assess vehicle responses efficiently. This paper introduces a method that builds a parametric representation of a driving scenario using collected driving data. By adopting a data-driven approach, we are then able to generate realistic, concrete scenarios that correspond to high-risk situations. A reinforcement learning technique is used to identify the combination of parameter values that result in the failure of a system under test (SUT). The proposed method generates novel, simulated high-risk scenarios, thereby offering a meaningful and focused assessment of AV systems.

Optimal and Non-Optimal MACD Parameter Values and Their Ranges for Stock-Index Futures: A Comparative Study of Nikkei, Dow Jones, and Nasdaq

This study investigates the optimal and non-optimal parameter values of the MACD (Moving Average Convergence Divergence) technical analysis indicator for three major stock market index futures: the Nikkei 225, the Dow Jones, and the Nasdaq. Using a recently developed methodology, it reveals the existence of specific ranges of optimal and non-optimal values for each of the three parameters of the MACD indicator in these indices. Sample models employing the optimal parameter values in the three index futures generated significantly higher returns, outperforming both a non-technical buy-and-hold strategy and a random strategy that did not incorporate any market information. This discovery suggests that the three market indices may not be weak-form efficient. Therefore, this study contributes to the research on market efficiency by verifying inefficiency using a new approach. The highlight of this study is identifying that the ranges of optimal parameter values for the three indices are different from each other, but the optimal parameter value combinations for each of the three indices share a unique characteristic form. This issue and its finding have not been explored in the existing literature. Several interesting findings and valuable insights for market participants and researchers arise from this study. The new methodology is unique in finding optimal and non-optimal parameter values through the analysis of parameter sets used in well-performing and poorly performing sample models. Its validity and reliability have been confirmed by this study, making a useful contribution to the field of technical analysis research, particularly in parameter optimization insight.

Metamodelling of a two-population spiking neural network.

In computational neuroscience, hypotheses are often formulated as bottom-up mechanistic models of the systems in question, consisting of differential equations that can be numerically integrated forward in time. Candidate models can then be validated by comparison against experimental data. The model outputs of neural network models depend on both neuron parameters, connectivity parameters and other model inputs. Successful model fitting requires sufficient exploration of the model parameter space, which can be computationally demanding. Additionally, identifying degeneracy in the parameters, i.e. different combinations of parameter values that produce similar outputs, is of interest, as they define the subset of parameter values consistent with the data. In this computational study, we apply metamodels to a two-population recurrent spiking network of point-neurons, the so-called Brunel network. Metamodels are data-driven approximations to more complex models with more desirable computational properties, which can be run considerably faster than the original model. Specifically, we apply and compare two different metamodelling techniques, masked autoregressive flows (MAF) and deep Gaussian process regression (DGPR), to estimate the power spectra of two different signals; the population spiking activities and the local field potential. We find that the metamodels are able to accurately model the power spectra in the asynchronous irregular regime, and that the DGPR metamodel provides a more accurate representation of the simulator compared to the MAF metamodel. Using the metamodels, we estimate the posterior probability distributions over parameters given observed simulator outputs separately for both LFP and population spiking activities. We find that these distributions correctly identify parameter combinations that give similar model outputs, and that some parameters are significantly more constrained by observing the LFP than by observing the population spiking activities.

Evaluation of process parameter of friction stir processing of AA2024-T351 Alloy using RSM and GRA

A fundamental method for changing the characteristics of metals through localised severe plastic deformation is friction stir processing (FSP), which is both straightforward and efficient. Because of its high strength-to-mass ratio and resilience to fatigue, aluminium alloy 2024 is commonly utilised. The experiments were designed utilising the Box–Behnken method and the process parameters’ chosen range values. In this research, the impact of FSP process variables on the tensile, surface roughness, and hardness properties of AA2024 will be examined. These parameters include tool rotation speed, welding speed, and axial load. The base sample tensile strength, surface roughness and microhardness values were 325 MPa, 0.5125 μm, and 120 HV. The optimised sample’s maximum tensile strength, minimum surface roughness, and maximum microhardness values were 420 MPa, 0.4 μm, and 144 HV. The grey relational analysis (GRA) determines the ideal combination of process parameter values for the best values in all responses. XRD was used to assess residual stress, dislocation density and micro-strain. The optimal process parameters have been obtained at a rotational speed of 1000 rpm, traverse speed of 45 mm/min, and axial load of 10.25 kN.

RETRACTED ARTICLE: Enhancing throughput using channel access priorities in frequency hopping network using federated learning

The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.

AN INVESTIGATION ON MEAN ROUGHNESS DEPTH AND MATERIAL EROSION SPEED DURING MANUFACTURING OF STAINLESS-STEEL MINIATURE RATCHET GEARS BY WIRE-EDM

This paper presents the results of an investigation conducted on wire electric discharge machining (wire-EDM) of miniature ratchet gears. Effects of three important process parameters spark duration, ‘Ton’, spark-off-duration ‘Toff’, and wire tension ‘WT’ on surface quality, i.e., mean roughness depth ‘Rz’ and productivity, i.e., material erosion speed ‘MES’, have been investigated by conducting seventeen experimental trials. Both Ton and Toff have been identified as the significant parameters. Further, an optimization of wire-EDM parameters resulted in simultaneously best compromise values of Rz 5.30 µm and MES 6.75 mm/min and is achieved the following cutting regime: Ton 1.5 µs, Toff 42.5 µs, and WT 1500 g. At the end, surface quality study has been conducted to evaluate the tribological fitness of the miniature ratchet gear machined at optimum combination of wire-EDM parameter values. It was investigated that the generation of uniform and shallow craters on the flank surfaces of ratchet gear machined at optimum values of parameters, imparted smoother bearing area curve and lower coefficient of friction. The profile and flank surface of the ratchet gear also found free from cracks, burrs, and dirt.

Entropy Measures with Three-axis Motion Time-series Data: Comparison of Dominant and Non-dominant Hand in Multi-directional Reach

Background: Motion variance differs among individuals, knowing as the differences in dominant and non-dominant hand functions provide beneficial information to clinicians or therapists regarding accurate diagnosis and rehabilitation purposes. However, due to subjective considerations, there are some limitations of the handedness questionnaires, which are one of the standard methods for evaluating interlimb function differences. Objective: This study aims to quantify the differences in upper limb motions between the dominant and non-dominant hands in the reaching task using approximate entropy (ApEn) and sample entropy (SampEn) measures. This study also provides proper combinations of parameter values m and r for the ApEn and SampEn measures in the hand movement data of the reaching task. Methods: Twenty volunteers performed a multi-directional reaching task. The acceleration data of hand motions were recorded by GENEActiv 3D acceleration sensor (Activinsights Ltd., UK) with a sampling frequency of 1000 Hz. In addition, the ApEn and SampEn values were analysed. Results: The ApEn values of the dominant hand were statistically significantly lower than those of the non-dominant hand for parameter combinations of m=2,3,4,5 with r=0.15,0.20,0.25 for the Y- and Z-axis (p<0.05). The SampEn values of the dominant hand significantly demonstrated lower than those of the non-dominant hand for all axes when computing on combinations of parameter m=2,3,4,5 with r=0.15,0.20 (p<0.05). Conclusion: The ApEn and SampEn measures could be used to predict the degree of regularity or complexity of the reaching hand motion time-series data. These entropy measures also reveal the differences between the dominant and non-dominant hand movements, quantifying movement differences in the dynamic motor tasks associated with hemispheric brain asymmetry.

Influence of Cutting Parameters and Tool Geometry on Topography of Hard Turned Surfaces

Three-dimensional topography measurement is a focus area of precision machining because it provides detailed information on the functional behavior of contacting surfaces. The purpose of this study is to analyze how the process parameters and the edge geometry of the cutting insert influence the topographical characteristics of hardened 16MnCr5 steel surfaces and to determine the favorable combinations of technology parameter values based on the analyzed topography parameters (Ssk, Sku, Sz, Sa, Sk, Spk, Svk). A research question is whether the analyzed parameters can be substituted by each other or not. A hard turning experiment was carried out based on a full factorial design of experiments, and the areas of the machined surfaces were scanned and analyzed by analysis of variance, regression analysis (response function), and correlation analysis. It was found that there are strong correlations between the topography parameters that allow the substitution of one with another; the main influencing cutting parameter is the feed rate for both insert types. The contributions of the study are the numerical information with regard to the correlations among the topography parameters, as well as the extent of the influence of cutting parameters.