Rough Assessment Research Articles

Development of the rough Defining Interrelationships Between Ranked criteria II method and its application in the MCDM model

Although numerous methods of multi-criteria decision-making have been developed so far, there are still those that have not been improved by different theories that deal well with uncertainties and inaccuracies that are a normal occurrence in everyday life. The goal of this research is the development of the Rough Defining Interrelationships Between Ranked Criteria II (Rough DIBR II) method and the presentation of its application in the multi-criteria decision-making (MCDM) problem, specifically in human resource management (HRM). The paper presents the subject-developed method and its practical application in the MCDM model with the Rough Additive Ratio Assessment (Rough ARAS) method. In order to check the consistency and validity of the proposed methodology, a sensitivity analysis and a comparative analysis were performed. The conclusions of this research indicate a stable and valid proposed methodology, as well as the possible application of the Rough DIBR II method for defining weighting coefficients of criteria in real-life decision-making problems.

A Sequence-Based Hybrid Ensemble Approach for Estimating Trail Pavement Roughness Using Smartphone and Bicycle Data

Trail pavement roughness significantly impacts user experience and safety. Measuring roughness over large areas using traditional equipment is challenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capture data using these have accuracy limitations. While machine learning has the potential to improve accuracy, there have been few applications of real-time roughness evaluation. This study proposes a hybrid ensemble machine learning model that combines sequence-based modeling with support vector regression (SVR) to estimate trail roughness using smartphone sensor data mounted on bicycles. The hybrid model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54–74% for asphalt concrete (AC) trails and 50–59% for Portland cement concrete (PCC) trails. For the 0.31 mi (499 m) segments, RMSE reductions of 37–60% and 49–56% for AC and PCC trails were achieved, respectively. Additionally, the hybrid model outperformed the base random forest model by 17%, highlighting the effectiveness of combining ensemble learning with sequence modeling and SVR. These results demonstrate that the hybrid model provides a cost-effective, scalable, and highly accurate alternative for large-scale trail roughness monitoring and assessment.

Abrasive challenge effects on enamel and dentin from irradiated human teeth: an in vitro study.

This study aimed to evaluate the effects of radiotherapy (RT) and chemoradiotherapy (CRT) on the wear and surface roughness of in vitro irradiated human enamel and dentin subjected to abrasive challenge. Enamel and dentin specimens (n = 42) were prepared from teeth donated by healthy patients and those with head and neck cancer who had received radiotherapy (RT) or chemoradiotherapy (CRT). The specimens were categorized into three groups: control, RT, and CRT (n = 14 per group for both enamel and dentin). These samples were subjected to an in vitro abrasive experiment using a brushing machine, followed by wear and surface roughness assessments with a confocal laser scanning microscope conducted before and after the abrasive challenge, considering both exposed and non-exposed areas. Statistical analysis used Shapiro-Wilk tests for normality, Wilcoxon tests for comparing two means, and Kruskal-Wallis tests. A significance level of 5% was adopted. In enamel specimens, wear profile values ​​of CRT and RT groups were not different from the control (p > 0.05). The RT group presents lower step values than the CRT and control groups (p < 0.001). No significant difference in final surface roughness was observed in all groups (p > 0.05). In dentin specimens, no significant difference in wear profile and step was observed in all groups (p > 0.05). However, CRT and RT groups present higher values in final surface roughness (p < 0.001). The exposure to ionizing radiation (associated or not to chemotherapy) influenced the surface roughness of dentin and the wear (step) of enamel after the in vitro abrasive challenge.Trial registration: Ethical procedures were approved by the FORP/USP Research Ethics Committee (CAAE: 61308416.4.0000.5419), and Hospital do Câncer de Barretos/Fundação Pio XII (CAAE: 61308416.4.3001.5437).

Towards AI driven surface roughness evaluation in manufacturing: a prospective study

AbstractIn the era of Industry 4.0 and the digital transformation of the manufacturing sector, this article explores the significant potential of machine learning (ML) and deep learning (DL) techniques in evaluating surface roughness—a critical metric of product quality. The integration of edge computing with current computational resources and intelligent sensors has revolutionized the application of AI-driven algorithms in smart manufacturing. It provides real-time data analysis and decision-making capabilities that were unattainable only a decade ago. The research effort intends to improve data-driven decision-making for product quality evaluation by leveraging data integration from manufacturing operations and surface quality measurements. Although a substantial amount of research has been conducted in the related fields, it is still difficult to comprehend and compile all the data on surface roughness research predictive assessment in the form of a process pipeline. This thorough systematic analysis examines scholarly articles published between 2014 and 2024 focusing on surface roughness assessment in precision manufacturing settings. The article is thoroughly classified based on the manufacturing processes, datasets, and ML models used, giving light on the present status, prominent approaches, and existing issues in this sector. A table summarizing the relevant works in this domain providing an easy access to the current trends have been provided. The article not only compiles essential findings and identifies research gaps and similarities in existing methodologies, but it also discusses future research directions and open issues in AI-aided surface roughness evaluation. The critical analysis of the literature reveals a scientific gaps which includes consistent development of benchmarked datasets and making the AI models more explainable using the state-of-the-art explainable AI (XAI) algorithms. The ultimate objective of the article is not only to provide a guide for the practitioners in either of the three domains of AI, manufacturing or surface metrology but also to pave the path for more robust, efficient, and accurate surface quality evaluation processes in production.

Influence of Silver Nanoparticle Integration on the Composition and Surface Properties of Acrylic Dental Resins Employed in Temporary Bridges

Background: Recognized widely for its irreversible effects, periodontal disease stands as one of the most prevalent conditions, particularly emphasizing the potential for permanent damage caused by gingivitis, extending to encompass the entirety of the supportive complex, including the connective tissue and alveolar bone. Patients necessitating comprehensive periodontal and prosthetic treatments require an interdisciplinary approach, involving the coordination of pre-treatment procedures and appropriate prosthetic strategies. The adoption of advanced materials and techniques can prove beneficial in addressing complex dental issues. (2) Methods: Forty specimens were prepared using the prescribed procedure, with 20 acting as controls and the other 20 integrating AgNPs. The initial step involved crafting wax patterns of distinct shapes: dumbbell shapes for tensile testing and rectangular shapes for roughness assessments. These dimensions were aligned with ASTM D-638 and ISO 527-2 standards and adjusted to match the specifications of the analysis device for mechanical properties. (3) Results: In this case, the results indicate minimal differences in heat-curing acrylic resins compared to the control samples at a 5% concentration. However, notable disparities arise at concentrations of 10% and 20%. (4) Conclusions: Temporary prosthetic constructions are essential for preventing functional imbalances and complications. Incorporating AgNPs at a 5% concentration maintains mechanical properties while providing antibacterial effects for long-term interim prosthodontic restorations. Higher nanoparticle concentrations may reduce resin mechanical properties without affecting material surface condition

Influence of infill pattern and layer height on surface characteristics and fatigue behavior of FFF‐printed PEEK

AbstractPolyEther Ether Ketone (PEEK) is a leading thermoplastic biomaterial renowned for its exceptional overall properties and suitability for processing via Fused Filament Fabrication (FFF) Additive Manufacturing (AM). Despite its widespread use in industries, its fatigue behavior remains a critically underexplored aspect. This study aims to investigate the fatigue behavior of FFF‐produced PEEK, considering variations in infill pattern (triangular or rectilinear) and layer height (0.15 mm or 0.25 mm). Fatigue tests revealed a significant influence of the infill pattern on fatigue behavior, with the rectilinear pattern outperforming the triangular one. Layer height had a negligible effect when paired with rectilinear infill. Specimens with rectilinear patterns exhibited superior fatigue performance, achieving infinite fatigue life at maximum applied stress around 70% of ultimate tensile stress. Surface roughness assessment and fracture surface analysis at scanning electron microscope enhanced the interpretation of the results. This pioneering study lays the groundwork for future research in design for AM.

Synergizing empirical and AI methods to examine nano-silica's microscale contribution to epoxy coating corrosion resistance

Epoxy-nanomaterials offer a promising solution to combat corrosion threatening civil infrastructure. To examine the impact of nano silica on corrosion resistance, varying amounts of nano silica, ranging from 0.0 to 3 wt%, were introduced into the epoxy matrix. Mechanical properties were studied through studying the dog-bone tensile strength and tribological aspects were assessed through mass loss abrasion tests. Corrosion resistance performance was studied using electrochemical impedance spectroscopy (EIS) and salt spray tests. Nano structure of the coated specimens was studied after salt spray tests using SEM-EDX analysis. Micro Chromatography (M-Ct) technique was employed to investigate the pore formation in the epoxy matrix. Besides, atomic force microscopy (AFM) was introduced to study surface roughness. The test results displayed a notable improvement (5–10 %) in tensile strength upon the introduction of nano silica. Taber tester results showed a reduction in mass loss with the addition of nano silica. Electrochemical impedance spectroscopy (EIS) studies further established a significant increase in corrosion resistance, having an impedance of 12 GOhm.cm2 for epoxy matrix samples containing 2.5 % nano silica after a 768-h salt spray test. SEM-EDX analysis revealed the absence of iron content in the epoxy matrix reinforced with uniformly distributed nano silica. Surface roughness assessments illustrated that nanolaminates contributed to an increase in overall roughness, minimizing surface irregularities in the epoxy matrix. Micro-CT scanning results proposed a decrease in epoxy matrix porosity upon the addition of nano silica. AI based prediction model helped to understand the relationship between surface roughness and corrosion. In summary, the experimental findings suggest valuable insights for future advancements in the development of protective coatings for worldwide civil infrastructures.

Assessment of Surface Roughness, Color, and Bonding Efficacy: Self-Adhesive vs. Conventional Flowable Resin.

This in vitro study aimed to analyze the surface roughness (Ra) and color stability (ΔEab, ΔE00) following simulated mechanical brushing and to evaluate the microtensile (μTBS) of self-adhering resin flowable (SARF) to dentin. The selected materials were Constic, Yflow AS, and Tetric N flow (TNF/control). Thirty composite resin cylinders were fabricated for surface property evaluation. Ra and color were assessed both before and after simulated brushing. The thresholds of 50:50% perceptibility and acceptability of color differences in the evaluated resins were assessed. For μTBS analysis, fifteen molars were selected, sectioned to expose flat dentin surfaces, and restored according to the manufacturers' instructions for microtensile testing. There were statistically significant differences in Ra among the groups, with Constic exhibiting the highest Ra value (0.702 µm; p < 0.05), whereas Yflow AS presented the lowest Ra value (0.184 µm). No statistically significant difference in color was observed among the groups (p > 0.05). The 50:50% perceptibility and acceptability thresholds were set at 1.2 and 2.7 for ΔEab and 0.8 and 1.8 for ΔE 00. All the results fell within the acceptable limits. The mean μTBS values of Constic, Yflow AS, and TNF were 10.649 MPa, 8.170 MPa, and 33.669 MPa, respectively. This study revealed increased Ra and comparable color stability among all the tested composite resins after abrasion. However, the SARF exhibited lower μTBS compared to conventional using an adhesive system.

Influence of electrophysical characteristics of plasma electrolytic treatment of 16MnCr5 structural alloy steel on structural and phase changes in the surface and its tribological properties

The possibility of using plasma electrolytic technologies for surface treatment of 16MnCr5 steel in order to increase its wear resistance is shown. The influence of electrophysical characteristics of plasma electrolytic treatment on structural-phase changes of the surface and its tribological properties is studied. A comparison of anodic and cathodic variants of diffusion saturation, as well as subsequent polishing on the results of surface modification is carried out. The complex effect of surface layers strengthened above 1100 HV, including iron carbide and nitride, martensite and retained austenite, to a depth of up to 250 μm with the preservation of a viscous core and the formation of a relatively homogeneous surface with reduced roughness on increasing wear resistance has been revealed. The features of tribological behavior of surfaces formed under different electrophysical parameters of processing, mainly related to the difference in surface microrelief, are shown. The correlation of the Kragelsky-Kambolov criterion as a complex assessment of surface roughness with its wear resistance is shown. The influence of changes in sliding speed and friction load on the occurrence and development of leading processes that determine the intensity and nature of destruction of friction surfaces is established.

Evaluation of surface roughness, cytotoxicity, and antibacterial effects of silver nanoparticle coating on copper nickel titanium orthodontic arch wires - in vitro study.

Surface roughness of arch wires directly impacts their corrosion behavior, friction resistance, and plaque accumulation, which may hinder tooth movement and lead to dental caries. The study aims to synthesize vanillin-mediated silver nanoparticles (AgNPs), characterize them, assess surface roughness and cytotoxicity of arch wires after silver nanoparticle coating, and test their antibacterial properties. Nine copper-nickel-titanium arch wires (CuNiTi) were cut into equal pieces. Three were sent for surface roughness assessment, three for cytotoxicity, and three for antibacterial testing. Dip coating of wires was done using the sol-gel thin film method. The surface roughness (Ra) before and after coating was evaluated using scanning electron microscopy and atomic force microscopy. Cytotoxicity testing was done with a (3- (4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay using gingival fibroblasts. Statistical analysis was done using SPSS software. Antibacterial activity against S. mutans was tested using the Agar-well diffusion method. The CuNiTi wires were coated successfully, and the coating appeared homogeneous. The mean Ra after coating (297.3+/- 30.4 nm) was significantly less than that before coating (339.7+/-49.2 nm). AgNPs showed minimal cytotoxicity against human gingival fibroblasts at different concentrations. Optical microscopy showed over 90% viability between 12.5 and 100 µg/ml. At 100 µg/ml, only 80% of cells remained viable. AgNP coating is biocompatible at concentrations up to 75 µg/ml. There was a significant intergroup difference in the zone of inhibition (antibacterial activity) noted with higher values in noncoated wires. (P value <0.007). AgNPs coated on CuNiTi arch wires showed reduced surface roughness and minimal cytotoxic effects on human gingival cells and good antibacterial activity against S. mutans compared to noncoated arch wires.

Influence of forging pretreatments on microstructure evolution and surface roughness of Al 6061 alloy

Achieving ultra-smooth surfaces is the goal of aluminum optical manufacturing. Under certain processing conditions, improving the microstructure of aluminum and understanding its relationship with surface roughness requires systematic study. The grain structure and various types of second-phase particles are of paramount importance. This study analyzed the microstructure of 6061 alloy after undergoing severe plastic deformation under various processing conditions followed by T6 homogenization heat treatment. Utilizing a white light interferometer, a comparative analysis of the surface roughness was conducted on specimens that underwent single-point diamond turning to achieve a mirror finish. The assessment of surface roughness on machined surfaces is solely based on white light interferometry. The analysis and discussion focus on the effects of phases (causing scratches and voids), the grains and grain boundaries. Experimental findings signify: the grain size, grain boundary and residual second phase can both influence the surface quality, the increase in deformation temperature and accumulated strain both facilitate the dissolution and fragmentation of the secondary phases. However, they also contribute to some extent to grain growth, resulting in a minimum secondary phase area fraction of 0.87% and grain sizes reaching 147.8 μm. Subsequent heat treatments, while effective in reducing the negative impact of the phases, reveal noticeable step-like structures affecting the quality of surface roughness, with the lowest obtained Ra value being 0.8 nm. A proposed pretreatment method in cleaner ingot processing with lower alloy element content addresses the trade-off between reducing phases and controlling grain growth, aiming to achieve improved surface roughness, promoting the application of polycrystalline aluminum alloys in the field of optics manufacturing.

Exploring the Tribological Behavior and Surface Characteristics of Polymethyl-Methacrylate/Hydroxyapatite (PMMA/HA) Composites: Investigating the Effect of Hydroxyapatite Weight Ratios on Wear Performance, Surface Roughness, and Hardness

This study conducts an analysis of the tribological characteristics of polymethyl-methacrylate (PMMA) composites reinforced with varying weight ratios of hydroxyapatite (HA) nanoparticles. It encompasses a systematic assessment of both hardness and surface roughness using the Shore-D hardness testing method and profilometer measurements, respectively. The results reveal a correlation between the HA nanoparticles weight ratio and hardness, indicating an increase in HA nanoparticles weight ratio corresponded to enhanced hardness. Moreover, higher HA nanoparticles weight ratios result in rougher surfaces, impacting composite performance. Specifically, the composite containing 5%-HA exhibited a Rq of 6 µm, which incrementally rose to 6.3 µm for the 10%-HA composite and significantly increased to 11.1 µm for the 15%-HA composite. Increasing HA weight ratios led to higher Rz values: 28 µm for 5%-HA, 35.1 µm for 10%-HA, and 72.5 µm for 15%-HA, indicating greater surface irregularities. The wear rates and friction coefficients are examined under applied forces ranging from 5 to 15 newtons across three distinct HA weight ratios (5%, 10%, and 15%). The results show a decreasing trend in wear rates with increasing weight ratios of HA, evidenced by weight loss rates of 0.0052 gr, 0.0011 gr, and 0.006 gr for samples S.1, S.2, and S.3, respectively, under a 15 N applied force. The friction coefficient analyses highlight complex dependencies on sliding distance, applied force, and HA weight ratio. Statistical tests, such as One-way analysis of variance (ANOVA) and effect size calculations, confirm significant applied force effects on friction coefficients, offering insights for optimizing PMMA/HA composite performance in biomedical applications.

Relationship between Roughness and Wettability of Nine Types of Implant Surfaces and Potential Interference of Surface Oxygen and Carbon: In Vitro Evaluation.

Aim: To assess the roughness and hydrophilicity of nine types of dental implant surfaces, while also examining the presence of contaminants carbon and oxygen on these surfaces. Furthermore, the study investigated potential correlations between these characteristics across the analyzed surfaces. Materials and Methods: The surfaces analyzed were as follows: MI: machined (turned), Implacil implant; TOI: blasted with titanium oxide, Implacil implant; TOAEI: blasted with titanium oxide and acid-etched, Implacil implant; ZAED: blasted with zirconia and acid-etched, DSP implant; CPD: coated with calcium phosphate, DSP implant; XD: subjected to an experimental treatment (patent pending), DSP implant; DAEHAS: double acid-etched and activated with hydroxyapatite nano-crystals, SIN implant; DAES: double acid-etched, SIN implant; and AMP: untreated surface of the Plenum implant, produced by additive manufacturing. Four and five disc-shaped specimens were used in the hydrophilicity and roughness assessments, respectively. Roughness was evaluated by optical profilometry and scanning electron microscopy; hydrophilicity was determined using the sessile-drop technique; and the chemical analysis was performed using X-ray photoelectron spectroscopy. The Kruskal- Wallis, Mann-Whitney, and Spearman correlation tests were employed to analyze the data (p < 0.10). Results: Significant differences were observed among the analyzed surfaces in terms of both roughness and hydrophilicity (p < 0.001). The surface exhibiting the highest roughness was AMP, whereas the greatest hydrophilicity was exhibited by CPD. Correlations between roughness and hydrophobicity were observed for MI (r = 0.936, p = 0.009), ZAED (r = 0.957, p = 0.004), and DAES (r = 0.964, p = 0.005). The carbon concentration observed on the CPD surface was lower than that observed on the other surfaces, whereas the oxygen concentrations were similar. No correlations were observed between the presence of contaminants and the roughness or hydrophilicity characteristics. Conclusion: Roughness and hydrophilicity values exhibited considerable variation among the tested surfaces. Aside from the CPD surface, comparable concentrations of carbon and oxygen were detected. Although correlations between roughness and hydrophilicity were observed only for the ZAED, DAES, and MI surfaces, these correlations were inadequate to establish a causal relationship between the two surface characteristics.

Bond strength and surface roughness assessment of novel antimicrobial polymeric coated dental cement

Resin cement integrated with zein-incorporated magnesium oxide nanoparticles has previously been found to inhibit oral microbes and decrease bacterial biofilm. However, the bond strength and surface features of this biomaterial have yet to be investigated. The objective of this study was to evaluate the shear bond strength, mode of fracture, and surface roughness of resin cement modified with zein-incorporated magnesium oxide nanoparticles. Characterization of the cement was performed by X-ray diffraction, field emission scanning electron microscopy, and Fourier transform infrared spectroscopy. 126 human teeth were divided into 3 groups and cemented to lithium disilicate ceramic using resin cement with zein-incorporated magnesium oxide nanoparticles at concentrations of 0%, 1%, and 2% (n = 42). 21 samples of each group were subjected to the shear bond strength test, while the other 21 underwent thermocycling for 10,000 cycles before the test, after which all samples were evaluated for the mode of fracture. To assess surface roughness, resin cement disks were analyzed by a profilometer before and after undergoing thermocycling for 10,000 cycles. The shear bond strength of the cement with 1% and 2% nanoparticles was significantly higher than the control before thermocycling. The mode of fracture was found to be mainly adhesive with all groups, with the unmodified cement presenting the highest cohesive failure. There was no significant difference in surface roughness between the groups before or after thermocycling. The addition of zein-incorporated magnesium oxide nanoparticles to resin cement improved or maintained the shear bond strength and surface roughness of the resin cement.Graphical abstract

Study of structural, surface energies, and tribomechanical properties of thermoplastics irradiated by electron beam

This study investigates the effects of electron irradiation on the structural, surface energy, and tribomechanical properties of two key thermoplastics: polyetheretherketone (PEEK) and polytetrafluoroethylene (PTFE). The experimental methods included electron beam irradiation using the ILU-10 pulsed linear accelerator, Fourier transform infrared spectroscopy (FT-IR), x-ray diffraction (XRD), microhardness testing, surface roughness assessment, tribology tests, and contact angle measurements.The FT-IR analysis revealed significant chemical changes on the surfaces of the polymers, including oxidation processes and the breaking of molecular bonds. XRD analysis showed an increase in the crystallinity of PTFE after irradiation, while the structure of PEEK remained stable. Microhardness testing indicated a notable increase in hardness for both polymers, particularly for PTFE, suggesting cross-linking of molecular chains. Surface roughness measurements demonstrated a decrease in roughness for both irradiated polymers. Tribology tests revealed that electron irradiation increased the coefficient of friction for PTFE and PEEK under various loads, which can be attributed to the alterations in their surface properties. Contact angle measurements indicated improved wettability of the irradiated surfaces, especially for PEEK, due to the formation of new functional groups. The total surface energy increased for both polymers post-irradiation, as determined using the Owens-Wendt-Rabel-Kaeble method. Electron irradiation leads to significant modifications in the surface and bulk properties of PEEK and PTFE, enhancing their tribomechanical and adhesive properties. These changes open new opportunities for the application of these materials in various engineering fields where specific performance characteristics are required.

A Comparative Analysis of Data Source’s Impact on Renewable Energy Scenario Assessment—The Example of Ground-Mounted Photovoltaics in Germany

The German government aims to convert its energy system to renewable energy by 2045. This requires a comprehensive understanding of land eligibility for renewables, particularly land-intensive ground-mounted photovoltaics (GM-PV). Federal states must set aside at least 2% of their land for renewable energy. This target value was derived using a top-down energy demand approach. Georeferenced land use data can be used to make bottom-up estimates. This study investigates how the choice of data source influences the bottom-up evaluation of land eligibility for GM-PV installations in Germany. This study evaluates the quality of data sources and their applicability for GM-PV scenario assessment by comparing the official data source Basis-DLM as the reference with the open-access data sources OpenStreetMap (OSM), Corine Land Cover (CLC), and Copernicus Emergency Management Service (CEMS). The intersection over union (IoU) and Matthews correlation coefficient (MCC) methods were used to analyse the differences in land use and eligibility due to the quality of the data sources and to compare their accuracy. The study’s results show the crucial role of data source selection in estimating the potential for GM-PV in Germany. The results indicate that open-access data overestimate land eligibility by 4.0% to 4.5% compared to the official Basis-DLM data. Spatial similarities and discrepancies between the OSM, CEMS CLC, and Basis-DLM land uses were identified. The CLC data exhibit higher consistency with Basis-DLM. These findings emphasise the importance of selecting the appropriate data source depending on the purpose and the use of official data sources for accurate and spatially differentiated decision-making and project planning at different scales. Open-access data sources can be applied for initial orientation and large-scale rough assessment as they balance data accuracy and accessibility.

Assessment of Surface Roughness of Non-Metallic Materials during Laser Processing

Experimental studies of the surface of non-metallic materials have been carried out to determine the roughness parameters of materials such as leather, bone, wood, plastic after processing the surface of materials with a laser beam. To assess the quality of the surface layer of materials, the depth of penetration of laser radiation into the material, the average value of the micro unevenness, and the mean square deviation of the micro unevenness were used. To describe the changes in the values of the micro unevenness, graphs of the correlation of the parameters of the micro unevenness and the modulus of elasticity of the surface of various non-metallic materials were used. Approximating polynomials are used to describe the correlations with the representation in a computer. A regression model is proposed that relates the properties of the material to the magnitude of the micro unevenness. Data on the depth of ablation for wood, bone, leather, plexiglass are presented, graphs of the correlation between the amount of surface micro unevenness and the density of materials, between the amount of surface micro-roughness and the modulus of elasticity of materials, the correlation coefficient between the amount of surface micro unevenness and the ignition temperature of organic materials. The obtained models make it possible to implement the proposed principle of laser processing of non-metallic materials, which consists in measuring the modulus of elasticity of the surface of the material and, based on the measurements obtained, control the modes of laser processing of products. The installation of laser surface treatment of non-metallic materials with the implementation of the principle of control of processing modes depending on the measured values of the modulus of elasticity of the surface of the material is proposed. To measure the elastic modulus of a material, a special sensor is used with indentation of the indenter and computer evaluation of the measurements obtained with the formation of solutions for controlling the modes of laser surface treatment of the material. The experimental results obtained made it possible to manufacture a number of products to ensure a given surface quality of non-metallic materials (a writing device, a gift for the newlyweds). Conducting experiments with changes in the power of laser radiation based on the results of measuring the modulus of elasticity of the surface of non-metallic materials has shown the effectiveness of operational setting of laser operating modes to ensure the quality of the surface of non-metallic materials.

A novel interval rough model for optimizing road network performance and safety

This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model’s ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model’s effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications.

Machine learning based surface roughness assessment via CNC spindle bearing vibration

Machine learning based surface roughness assessment via CNC spindle bearing vibration

The effect of stretching on polyamide – 6: assessment of crystallinity, mechanical properties, and surface roughness

The effect of stretching on polyamide – 6: assessment of crystallinity, mechanical properties, and surface roughness