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Due to their availability and ease of use, additive techniques are experiencing dynamic development. This applies to both the industrial sector and individual recipients. The authors of numerous publications address in their research the subject of the influence of selected printing process parameters on the strength of models, usually made using selected MEX (Material Extrusion) methods. Among the MEX methods, the most frequently chosen are the FFF (Fused Filament Fabrication) and FDM (Fused Deposition Modeling) methods. This is due to the high availability and low cost of devices using the methods mentioned above and the high availability of polymer materials. In their research, the authors increasingly consider the influence of the internal structure of the samples and their density on selected strength parameters, often without considering whether they affect the geometric accuracy of sample mapping. For the above reasons, it was decided in the article to conduct research covering the indicated subject using the example of standardized samples made of six selected polymers used in the FFF method.

Technological progress in the 21st century has catalysed the industrial revolution (Industry 4.0) following the development of multiple new industrial automation technologies in the manufacturing sector. Regardless, past research indicated the unsuccessful attempts in adopting Industry 4.0 technologies among manufacturing organisations. Undoubtedly, the operationalisation of Industry 4.0 in manufacturing proved challenging as organisations were required to evaluate various aspects for effective implementation. Thus, a sound understanding of constructs concerning employees’ acceptance and readiness levels towards novel automation technologies was required. Hence, this study aims to explore, develop, and validate the suggested conceptual framework by integrating the Technology Acceptance Model (TAM) and Technology Readiness Index (TRI) with Exploratory Factor Analysis (EFA). The EFA process was the first crucial step in ensuring the internal consistency and stability of the instrument across the sampling population. Consequently, the research outcome potentially enabled the manufacturing sector to identify and comprehend the key determinants in designing industrial automation technologies. This study also contributed to knowledge on technology acceptance by synthesizing TAM 3 and TRI 2.0 theories, thus constructing a new TAM in manufacturing.

In the automotive sector, poroelastic materials (PEMs) are used as trim elements to achieve the desired interior acoustics of a vehicle. This study examines the effect of manufacturing as well as measurement techniques on airflow resistivity. This property plays a key role in the acoustic behavior of PEMs. First, the importance of engineering acoustics and poroelastic materials in vehicle industry is reviewed, followed by the introduction of the most important properties and their measurement techniques. Next, the theory and the measurement techniques used to determine resistivity via direct method are detailed. Then the factors influencing the results and their quantified effects are presented. More than 10 influencing factors are identified and examined, from which the inhomogeneity, resulting from the production technology proved to be the most significant. The results obtained with direct and inverse methods are compared for validation purposes and to determine the achievable accuracy of the inverse method. The average difference between the two methods is 4.54%, which means that the inverse method can provide a good approximation. Finally, conclusions are drawn and suggestions are made for the future.

In drilling operations, hole quality is affected by factors such as drill diameter, drilling parameters, drill bit properties, workpiece material. A large portion of the drilling energy is converted into thermal energy, which can be measured as temperature. In this study, surface roughness of holes, drill tip wear and drill tip temperatures were determined using two different workpiece materials and three different drill tips. Furthermore, the costs of holes drilled with different drill bits were determined and interpreted based on drill bit characteristics. The results obtained can be optimized according to the process parameters and it has been shown that a more sustainable and much more economical manufacturing can be achieved by avoiding the use of additional reaming or internal grinding for the desired surface quality and eliminating negative environmental effects.

In manufacturing, the demand is always for better proficiency, less operational costs, and greater effectiveness. The key in achieving these requirements through adept handling is, in fact, maintenance of machines and devices. Combinations of regular maintenance schemes, preventive and curative methods, usually tend to swing between unnecessary maintenance jobs and unexpected equipment failures. This situation calls for a need to develop a more sophisticated approach, which gives rise to Predictive Maintenance (PdM). PdM is different from the rest because it forecasts changes that lead to failure in the equipment even before they seem probable, preparing the ground for pre-emptive measures, thereby reducing downtime, and reducing maintenance costs on a really large scale. However, the introduction of PdM does not come without corresponding challenges, which include: Data compilation and management within PdM systems, difficulty in modeling machinery's nonlinear dynamics, difficulties involved in integrating PdM systems into traditional operational pipelines of manufacturing entities, as well as justification of return on investments. To these issues, this paper adopts sophisticated mathematical models that have been selected carefully for their capabilities to handle bulk data, decipher intricate interrelations, and accurately predict future failures. Examples include Time Series Analysis: ARIMA and SARIMA use sensor temporal patterns; Survival Analysis, using Cox Proportional Hazards model, to measure machinery failure survival horizons; and advanced Machine Learning algorithms such as Stochastic Forests and Gradient Boosting Machines known for their nonlinear data acuity and insight into feature significance levels. Empirical validation of the model across diverse data samples reveals that the proposed model excels on all metric levels by achieving an 8.5% improvement in predictive precision, an 8% increase in accuracy, 4.9% boost in recall, 9.5 times faster velocity, a 4.5 increment in AUC, and an impressive 10.4% shot in specificity over what is available today. The work resolves the tensions between theory and real-life application while setting a new benchmark in predictive maintenance, thereby heralding a paradigm shift in the levels of manufacturing efficiency and reliability sets.

This study experimentally compares how three common machining routes, turning, milling and grinding, affect the surface texture and tribological response of three structural steels (C45, 42CrMo4, 30CrMoV9) under conditions where the profile roughness Ra is deliberately aligned across routes. Areal topography was measured by coherence correlation interferometry and evaluated according to ISO 25178 (height metrics Sa, Sq, Sp, Sv, Sz, St). The bearing area curve (Abbott–Firestone) was used to derive functional descriptors Rpk, Rk and Rvk. Scratch resistance was determined on a UMT‑3 tribometer (Rockwell 120°, P = 50 N) as HSp = 8·P / w² in accordance with ASTM G171. The results show that surfaces with comparable Ra can differ markedly in areal extremes and BAC‑derived parameters, which is reflected in scratch response. These findings support replacing sole Ra specification with areal and bearing‑curve descriptors when functional performance is critical (friction, sealing, wear).

Usti nad Labem. Czech Republic. E-mail: jan.novotny@ujep.cz This work investigates atmospheric plasma spraying (APS) coatings on aluminum molds used for truck tire vulcanization. Cobalt-based powders (MS1) and a NiAl interlayer combined with cobalt powders (MS1 + MS2) were deposited on AlMg3Mn alloy substrates. SEM/EDS analyses revealed that the NiAl interlayer improved adhesion, reduced chromium agglomeration, and enhanced ele-mental homogeneity. Vickers microhardness testing showed higher hardness values for MS1 + MS2 (457 HV 0,3) compared to MS1 (436 HV 0,3). Despite some residual cracks and porosity, their impact on per-formance was minimal. The study concludes that NiAl interlayer coatings significantly improve mold durability and operational life. Automated application and controlled-atmosphere spraying methods are recommended to further optimize coating properties for industrial use.

Abstract- The promotion of Electrical Discharge Machining (EDM) and vibration aided Electric Arc Machining (EAM-V) processes is characterized in the study in terms of their capability for precision manufacture, mainly drawing any performance comparisons from a machine learning approach. The present machine learning study aims to predict some important metrics of machining utility, such as Material Removal Rate (MRR), Tool Wear Rate (TWR), and Surface Roughness (SR), against process parameters like current, pulse-on/off time, etc. Some advanced models like Gradient Boosting and Random Forest are used to analyze the efficacy and effectiveness of EDM and EAM-V, comparing the respective influences these parameters have on honing outcomes. The study describes an elaborate methodology: data collection, preprocessing, feature scaling, and application of multiple regression algorithms for machining performance forecasting. The experimental data for model training and testing were partitioned into 80% and 20%, respectively. The results revealed that Gradient Boosting (GB) performed better than Random Forest (RF) for all parameters. In GB, the R² values of MRR, TWR, and SR were higher; hence, its degree of accuracy was superior in comparison with RF. For instance, an R² value of 0.970, 0.994, and 0.999 was achieved by GB for MRR, TWR, and SR, respectively, thus proving its better predictive ability. Moreover, according to average predicted values, EAM-V performs better for MRR; EDM, comparatively, from TWR and SR, is more suitable for precision applications. The performance validation of GB through RMSE and MAE also confirms its efficacious predictions.

Additive Manufacturing has enabled the design of complex components in several technical fields. Considering turbomachinery components, additive manufacturing has unlocked the achievement of significant performances for dynamic rotoring components. The application of topology optimization methods is one of the main factors accelerating the technological development of this sector. This paper presents a procedure for the optimization of static turbomachinery components. The frame-work proposed compares the results obtained by introducing a lattice structure and a solid optimized shape. The procedure is presented with reference to a specific case study. To validate the proposed framework, the complete re-design of a thrust collar of a major Italian-based Oil&Gas company is carried out, demonstrating that the re-thinking of the component in terms of Topology Optimization is a straightforward approach to increase the overall performance of the produced part.

The article described a new methodology for testing the torsional resistance of a single-stage gear transmission used in agricultural machinery. The analysis encompassed the entire mechanical system rather than focusing solely on its individual components. The research identified three key ranges of structural resistance. The first range, with twist angles from 0° to 1.85° and torques up to 1050 Nm, was associated with the elimination of structural play and the alignment of contact surfaces. The second range, from 1.85° to 4.76° and torques up to 3450 Nm, confirmed the resilient behavior of the gearbox according to Hooke's law. In this range, the system worked stably and maintained repeatability of parameters. The third range, above 4.76° and 3050 Nm, showed the presence of permanent but local deformations. However, these displacements did not affect the functionality of the system in less demanding applications. The maximum torque of 5500 Nm did not cause macroscopic damage or oil leaks, which proves the high quality of the design and the effectiveness of material optimization. The developed method allows for an accurate determination of the safety factor and a detailed assessment of the strength properties. It can be used to optimize transmissions in various sectors such as agriculture, automotive and aerospace. The results also form the basis for further experiments, including fatigue tests and contact stress analyses. The proposed methodology enhances the predictive accuracy of gearbox durability under various load conditions. These advancements support the development of sustainable and efficient mechanical systems across multiple industries.

Today, machine learning regression methods are quietly but fundamentally transforming cost and time estimation in manufacturing: from early pricing to labor planning to operational order management. This survey offers a comprehensive map of approaches - from linear models, to tree ensembles (RF, GBM, XGBoost) and shallow neural networks, to multi-target and tensor regressions that can exploit data structure across BOM items and sequences of operations. With an emphasis on SME conditions, we show how to reconcile three often conflicting requirements of practice: accuracy, explainability, and integration into existing data flows (MES/ERP). The paper presents a comparative taxonomy of methods, recommended validation practices (MAE, RMSE, MAPE, R² including confidence intervals) and a pragmatic adoption trajectory: from regularized multiple regressions to tree models to multi-output formulations sharing re-presentations across operations. Consolidated findings show that modern learners consistently outperform traditional baselines when supported by careful flag engineering, drift management, and data standardization. As a major research-application contribution, we propose a unified multi-objective framework for simultaneous cost and time prediction that combines domain (queueing/simulation) features with data-driven regression to enable transparent decision making in pricing and capacity planning. The study thus creates a bridge between theory and manufacturing practice and invites the reader to systematically but achievably deploy ML in everyday decision making.

The study focuses on the evaluation of the degradation behaviour of structural materials used in a heat exchanger boiler exposed to elevated thermal, pressure, and cyclic loading conditions. The research is aimed at non alloy pressure steels P235GH, P265GH, and P355GH employed in critical boiler components. The objective of the study was to analyse the effect of temperature in the range of 250–600 °C on the residual mechanical properties, plastic deformation, and fatigue behaviour of these materials. The results indicate that at temperatures above 300–400 °C, a significant degradation of mechanical properties occurs. The residual strength of P235GH steel decreases by more than 60 % at 400 °C, while the plastic deformation of P235GH and P265GH steels is reduced to 5–8 %, representing a critical threshold from the perspective of fatigue damage. Steel grade P355GH exhibits higher thermal stability; however, at 400 °C its yield strength decreases to approximately 195 MPa. Based on the obtained results, an optimised material concept was proposed utilising heat resistant Cr–Mo steels 16Mo3, 13CrMo4 5, and 10CrMo9 10, which retain 50–100 % higher plastic deformation and significantly greater creep resistance at temperatures of 500–600 °C compared to the original materials.

The objective of this paper is the evaluation of dimensional and geometric accuracy and surface to-pography of milled parts from plastic. This evaluation was done on 10 samples from various thermo-plastics made by extrusion and FDM 3D printing. The samples were then milled. One side was milled dry while the other was milled with cutting fluid, which has improved the texture of the result-ing machined surfaces in most cases, for example with printed PLA, where Ra was reduced by 1.8 µm. For determining the dimensional and geometric accuracy, two parameters were chosen, those being distance and parallelism. For evaluating the surface topography, 4 parameters were measured using 2D profile roughness and 3D surface texture. The surface of the prints was greatly improved by machining. The paper ends with practical recommendations for choosing different plastic materials for applications, requiring high dimensional accuracy and low surface roughness.

To enhance the effective penetration of cutting fluid into the depth of the cutting joint, a nano-cutting liquid atomization method has been proposed to improve the cutting quality of diamond wire sawing. A six-inch large diameter silicon wafer (150 mm diameter) diamond wire saw cutting experimental platform was constructed. The base liquid, nano SiO2, and nano SiC cutting liquid were utilized as the cutting fluids, and various cutting solutions were employed to compare the cutting quality of large diameter silicon wafers. The temperature field change, surface roughness of the silicon wafer, surface morphology, and warping of the silicon wafer were measured as evaluation indexes, and the impact law of different cutting solutions on the cutting quality of diamond wire saw was analyzed. The results indicate that nano-cutting fluid can reduce the roughness of silicon wafers and improve the surface morphology of silicon wafers. Mixing multiple nanoparticles can produce cutting fluids that further enhance wire saw cutting performance in actual diamond wire saw cutting technologies.

Aluminium beverage-can are common material for secondary utilization. Their recycling is slightly com-plicated by presence of organic materials in them and lack of collection points (although the legislative in this case is rapidly changing to be more friendly). Another approach to deal with beverage-cans is using them as a source of hydrogen. In this manuscript, the evolution of hydrogen in 10 wt. % NaOH solution is described. The leaching residues were analysed in detail. Their chemical and phase composi-tion were measured, and the amount of trapped hydrogen was also analysed. The beverage-cans are usually composed of 3004 and 5182. The residues after leaching contained beside organic residues oxides and hydroxides in both cases of initial alloys. Surprisingly, the amount of hydrogen in leaching residues is almost negligible.

Optimising the mechanical properties required for biomedical applications is something that porous Ti-6Al-4V structures offer the opportunity to do. Triply periodic minimal surface (TPMS) structures, such as the Diamond and Gyroid structures, provide interconnected pores that can be used to adjust strength, stiffness and deformation. The mechanical behaviour of these two architectures under compressive and bending loads is compared in this study, with the use of additively manufactured samples. The results demonstrate that pore geometry significantly impacts mechanical behaviour. Diamond structures exhibit higher stiffness and strength, whereas Gyroid structures provide a more isotropic and flexible response. These findings emphasise the importance of architecture when designing implants and other components for which optimised mechanical properties and geometry are essential.

Cobalt-based alloys are widely used for orthopedic implants due to their excellent mechanical properties and corrosion resistance. Biomedical Co-Cr-Mo alloy, commonly applied in knee replacements, is typically produced by precision casting. However, in cases requiring patient-specific geometries, additive manufacturing technologies, such as Selective Laser Melting (SLM), offer promising alternatives. This study compares the microstructure and mechanical properties of Co-Cr-Mo alloy in the as-cast state and after SLM processing. The SLM-produced samples exhibited a fine, cellular microstructure and superior mechanical strength. Specifically, the printed alloy achieved a yield strength of 688 ± 8 MPa and an ultimate tensile strength of 994 ± 11 MPa, exceeding that of the cast material by about 495 ± 1 MPa. These results demonstrate the potential of SLM technology for manufacturing customized orthopedic implants with improved mechanical properties and dimensional accuracy.

The use of supercritical water in energy applications is motivated by the aim of increasing the thermal efficiency of power systems. However, structural materials exposed to this environment may undergo corrosive degradation. The objective of this study was to conduct experiments on samples exposed to simulated operational conditions in supercritical water, steam, and air. The material surfaces were sub-sequently analyzed using optical microscopy and scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS). Particular attention was given to the formation of oxide layers on the nickel-based alloy Inconel 718 produced by additive manufacturing by PBF-SLM technology. The corrosion behavior was evaluated by monitoring mass gains. The results were compared with materials manufactured using conventional techniques.

An agricultural tractor equipped with appropriately rated guards can often replace specialized forestry machinery. Currently, few authorized dealers on the Polish market offer tractors adapted to harsh forest conditions, so this work involved designing a tubular guard for the Kubota M135GX-VI agricul-tural tractor. The aim of this work was to develop a conceptual design for a tubular guard, together with a strength analysis based on FOPS procedures, dedicated to the KUBOTA M135GX-IV agricul-tural tractor. To properly design the tubular guard, applicable standards and regulations regarding the construction of cabs and tubular guards for agricultural and forestry machinery were first analyzed. Subsequently, the available solutions were analyzed and two original concepts were developed. These concepts were evaluated based on the adopted criteria, selecting the variant with the highest score. Furthermore, the most advantageous variant was subjected to a strength analysis using the finite el-ement method (FEM) in accordance with the FOPS procedure. The test results showed that all nodes included in the developed concept met the strength requirements.

This article presents the development and validation of SBRI (Small Business Digital Maturity Assessment and Road to Industry 4.0), an innovative methodology for assessing digital maturity and supporting digital transformation specifically designed for small manufacturing enterprises in the context of Industry 4.0. Unlike existing models, which are often too complex or unsuitable for smaller organizations, SBRI considers the unique characteristics and constraints of small businesses. The methodology includes five key dimensions: Strategy, Technology, Process, People, and Organization, elaborated into 25 subdimensions with specific maturity criteria and indicators. The SBRI includes a structured roadmap for digital transformation through a proposed digital maturity continuous improvement cycle. An empirical study involving 23 small manufacturing enterprises in the Czech Republic has demonstrated the validity and practical applicability of the methodology. The results showed an average level of enterprise digital maturity of 0.9 on a scale of 0 – 4. These findings suggest that small businesses are just at the beginning of their digital transformation journey. Therefore, the SBRI methodology represents a valuable tool for navigating small businesses through their digital transformation journey, contributing to academic discourse and practical application of Industry 4.0 principles in the small business segment.

The study analyses current trends in the manufacturing of highly stressed components used in the defence industry, with a particular focus on handgun components. These parts are commonly produced from low-alloy steels that are refined to achieve medium strength levels. To enhance their resistance to mechanical and thermal loading, conventional production routes typically involve surface hardening followed by tempering. This study investigates the potential of replacing these established methods with a chemical–thermal surface treatment, specifically boriding, in which the surface layer is enriched with boron atoms. The mechanical properties of selected materials used for test samples are described and compared after treatment by induction surface hardening and boriding. The results provide a basis for evaluating the applicability of boriding as an alternative surface treatment for highly stressed components in the defence industry.

This article deals with the experimental investigation of the influence of tire pressure on fuel consumption and tire life in passenger cars. Using laboratory and real-world operational measurements, the dependence between tire pressure and temperature, contact patch size, tread wear, and changes in driving characteristics was analyzed. The results show that even slight deviations from the prescribed pressure can lead to increased fuel consumption, shortened tire life, and reduced driving comfort and safety. The article also draws attention to the insufficient use of pressure monitoring systems in practice and points to the economic and ecological impacts of underinflation. The experimental data are supplemented with graphs and tables that demonstrate the influence of pressure on tire behavior during driving.

Article deals with analysis on influence of post-process settings profiles in the Polyworks software and its influence on measuring bias (difference between average surface profile deviation and artifact reference value) and standard deviation of measured data. The comparison was evaluated on glossy artifacts with freeform surfaces. Setting with least bias and standard deviation was than used to evaluate repeatability and systematic measurement error and minimum tolerance bandwidth Tmin according to VDA 5 and MSA 4, respectively for three conceptions of laser scanning technologies available on today’s market. Cartesian CMM LK Altera S with laser scanner Nikon LC15Dx (automated technology), Measuring arm Nikon MCAx S30 with laser scanner Nikon H120 (manual technology) and optically tracked handheld device Metronor M-Scan with laser scanner Nikon H120 (manual technology). The conclusions of the study can serve as a guide in technology selection for reverse engineering input data acquisition. Subsequently, the optimal parameters of the post-process settings (for glossy surfaces) in the Polyworks software are listed.

When diamond scribing knives are used to grind silicon wafers at ultra-high speeds, slight changes in the structure of the diamond scribing knives and changes in the grinding parameters will have a large impact on the processing accuracy and appearance of the silicon wafers. In order to reduce the defective rate of silicon wafers, improve the service life of diamond scribing knives and grinding efficiency. To address this issue, the working mechanism of the scribing knife grinding is analysed in the paper, the influence of spindle speed and feed rate on the quality of the silicon wafer slit when the scribing knife is grinding is studied, and the chipping of silicon wafers is observed through the scanning electron microscope and optical microscope, so as to analyse the shape of the cross-section, length of the cutting edge, concentration of diamond particles in the cutting edge, thickness of the cutting edge and determine the structure of the scribing knife, and to test its influence on the silicon wafer slit by means of the grinding experiments. The structure of the scribing knife was determined, and its influence on the quality of silicon wafer slit was tested by grinding experiment. The results show that the wear rate of diamond particles, slit quality and processing efficiency of the scribing knife are optimal when grinding silicon wafers at 50,000 r/min and 60-80 mm.sec^-1. The above study can help to further understand the wear mechanism of the scribing knife in the process of ultra-high-speed grinding of silicon wafers, improve the machining efficiency, and prolong the service life of the tool.

This article is dedicated to exploring the potential enhancement of mechanical properties, such as hardness and tensile strength, in selected Al-Si alloys (AlSi7Mg0.3, AlSi7Cu4, and Al-Si10.5Cu1.2Mn0.8Ni1.2). High-melting-point elements, such as chromium and molybdenum, are rarely utilized as additives in Al-Si alloys. However, the article demonstrates the feasibility of improving the mechanical properties of these alloys through the addition of high-melting-point elements. High-melting-point metals, often referred to as refractory metals, typically have melting points above 2000 degrees Celsius. Common refractory metals include tungsten, molybdenum, tantalum, niobium, rhenium, and others. These metals exhibit excellent mechanical properties at elevated temperatures and often possess high density and good corrosion resistance. All casts were made using by gravity casting with different heat treatment conditions at 740 °C. The microstructures, hardness, microhard-ness and tensile strenght of the samples were analyzed. Hardness measurements were conducted using two types of hardness testers according to ČSN EN ISO 6506-1 for the Brinell hardness test method and ČSN EN ISO 6507-1 for the Vickers hardness test method. A static tensile test was performed on a universal testing machine, Inspekt 100, in accordance with the standard ČSN EN ISO 6892-1. The measured data demonstrated that high-melting-point metals affect each alloy differently. In some alloys, mechanical properties improved after heat treatment, while in others, a significant deterioration was observed, particularly in tensile strength.

This study examines the influence of FDM printing parameters on replica parts for an educational robotics kit, targeting functional compatibility without post-processing. A VEX Robotics 2×12 Beam (228‑2500‑026) was used as the reference part. Reference dimensions were obtained as mean values from 10 original VEX IQ parts. Replicas were printed from PLA and PETG on Original Prusa MK4 printers using four infill patterns and six infill densities (15–70%). For each material–pattern–density combination, 10 parts were produced, resulting in 480 printed samples. Width, length, and height were measured with a Mitutoyo MiSTAR 555 CNC CMM in accordance with ISO 10360-2. Results are expressed as mean deviations from reference dimensions, standard deviations, and expanded uncertainty of the mean. Maximum deviations reached 0.062, 0.092, and 0.032 mm for PLA, and 0.046, 0.090, and 0.028 mm for PETG. The results provide guidance for selecting non-solid infill settings that reduce material use and printing time while maintaining dimensional compatibility

Investigation of cutting forces in metal cutting is of great importance for defining the effectiveness of the production as well as its impact on product quality. Several researchers studied the effect of cutting parameters on the cutting forces through statistical analysis; however, very few studies use the normalization of the data. Normalization reduces the skewness in the data and increases the accuracy of the results, which can be beneficial in modern industry where AI is being integrated with manufacturing. This research aimed to study the statistical analysis of cutting parameters and cutting forces using log-normalization and compare the accuracy of results with absolute data. The study uses a three-axis piezoelectric dynamometer to measure the cutting forces in the turning of X5CrNi18-10 steel. The results suggested that feed influences the cutting forces during machining. Coolant helps to reduce the cutting forces during the turning of hard steel. Log-normalization increases the accuracy of the results. These results can be used to predict cutting forces during the turning of chromium-nickel alloy steel.

The push-type rotary steerable core bearing has high load capacity and high precision, and has been widely used in oil and gas drilling field. Its service life is difficult to predict due to various complex working conditions. Based on the finite element method, this paper establishes a three-dimensional rotating guide mandrel model to calculate and analyze the mechanical simulation of the guide mandrel under different working conditions, and establishes the corresponding life prediction model to predict its life. The results show that reducing the torque and speed in the range of drilling requirements is conducive to improving the overall life of the spindle, and the life matrix and life distribution are consistent with the characteristics of S-N curve, which is consistent with the characteristics of high cyclic stress of the spindle. The research results can be used to reliably predict the life of the push-type rotary steering mandrel and simulate its working state with high precision. This data is critical for reliability analysis and design optimization.

This paper presents the design and analysis of a 3-PRS mechanism for positioning cutting heads in CNC machines, addressing limitations of traditional rotary mechanisms such as hose twisting, wear, and limited modularity. Kinematic and dynamic analyses guided actuator selection and confirmed bearing durability. The mechanism achieves a favourable load-to-weight ratio and integrated Z-axis movement, making it suitable for simpler gantry CNC machines. Though programming is complex due to multiaxis synchronization, the modular design supports easy adaptation to different tools. Future research will focus on reducing the eccentric torch offset and refining dimensions to enhance versatility. The mechanism has strong potential in sectors like automotive, aerospace, and construction.

The aim of this work is to verify the reliability of optical inspection of tires during their transport on a roller conveyor, with an emphasis on the accuracy of 3D scanning in real and simulated operating conditions. A measuring box was designed and constructed to eliminate environmental interference, and measurements were subsequently compared with different degrees of site coverage. Testing was carried out using a 3D sensor O3D302 operating on the Time-of-Flight principle, and spatial data in the form of point clouds were obtained and compared with the reference dimensions of the Nokian WR D4 tire. The effects of solar IR radiation, rain, surface moisture, and natural lighting conditions were analyzed, which caused different levels of deformation, noise, and measurement deviations. The results show that significant errors occur without coverage, while the measuring box significantly reduces these deviations and increases the stability of point data. Complete coverage from above and below proved to be the most effective solution, but the wet tire surface remains a significant source of interference. The work further proposes structural modifications to the box and recommends the application of a matte surface and the expansion of tests to include the effects of vibrations and real conveyor operation. The result is a technical evaluation of the measurements and recommendations for improving optical tire detection in the industrial process.