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The given paper deals with the defects propagation in car tires for passenger vehicles under dynamic loading. The occurrence of defects has the significant influence on the lifetime and quality of the tire, especially during its operation as a part of the vehicle. The given defects are closely connected with a safety in road traffic. The aim of the study was to carry out a non-destructive analysis of the car tire for the purpose to analyze the defects propagation as well as to introduce the defects classification and their location along with the whole course of rupture as a result of increasing speed, loading and the number of hours or kilometers driven. During the analysis, we used a non-destructive method for detecting defects using a non-destructive analyzer that works on the principle of shearography. The experimental measurement was carried out for 12 car tires. The measurement results are displayed from the non-destructive analyzer in the form of protocols from measurement and video display. The evaluation of the results of the measurement for the propagation of defects is displayed graphically. In relation to the tire casing, the analysis of the defects propagation can help design engineers to solve critical issues by choosing the right material, modifying dimensions of individual components or even by redesigning the overall construction of the tire casing and thus to increase the safety from the as-pect of vehicle operation.

The simple geometry was investigated by analytical simulation in the article. The cylinder cross flow and heat transfer was evaluated. The different Nusselt number equations obtained from literature were mutually compared. The selected range of Reynolds number was from 5 to 2.106 with respect to laminar and turbulent regime of fliud flow. The coefficients of Nusselt number equations were also compared with respect to Reynolds number ranges. The Sieder-Tate correction for thermal boundary layer was taking into account and its effect on the Nusselt number values was also evaluated. Differences in result of selected equations are presented. Sieder-Tate correction effect is also discussed. However the equations were applied in its validity intervals of Reynolds and Prandtl numbers, the high differences up to 40 % from each other were found.

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

Vehicle Routing Problem is a common problem in logistics, which can simulate in-plant and out-plant material handling. In the article, we demonstrate a Vehicle Routing Problem, which contains period, time window and multiple depots. In this case, customers must be served from several depots. The position of the nodes (depots and customers), the demand and time window of the customers are known in advance. The number and capacity constraint of vehicles are predefined. The vehicles leave from one depot, visit some customers and then return to the depot. The above-described vehicle routing is solved with construction algorithms and Ant Colony algorithms. The Ant Colony algorithms are used to improve random solutions and solutions generated with construction algorithms. According to the test results the Elitist Strategy Ant System and the Rank-Based Version of Ant System algorithms gave the best solutions.

When analyzing the natural frequencies of a gear mechanism, it's crucial to consider the mesh stiffness, which is influenced by the number of teeth in the mesh. Mesh stiffness behaves as an internal excitation source for the dynamic system, affecting the resulting frequency spectrum. This paper presents an experimental determination of gear mesh stiffness supported by analytical-simulation models of mesh stiffness, outlining common modeling methods and detailing the experimental setup and test specimens. The obtained data are then compared with simulation models of mesh stiffness, discussing the significance of this comparison and emphasizing the role of experimental data in validating and refining existing models of mesh stiffness. The experimental measurement of mesh stiffness described here emerges as a valuable tool for accurately representing mesh stiffness during engagement.

PM2.5 and PM10 measurement technique based on light scattering usually exhibit inaccurate measurement results in their applications. For improving the reliability of this method for PM2.5 and PM10 measurement, systematic research on the structure optimization of single particle light scattering sensors (SPLSS), calibration of SPLSS, and PM2.5/PM10 monitor development are carried out. Frist, by simulating and optimizing light scattering parameters, light scattering signals varied monotonically with particle size could be obtained, and thereby capability of accurate size-identifying can be established. Then, by developing threshold comparison circuit and calibration device, particle size channel of SPLSS or monitor could be divided, and particle counting efficiency could be corrected. Finally, by obtaining empirical values of parameters, i.e., heating temperature, particle density, involved in the developed dynamic heating system and PN-PM algorithm, interference of humidity and particle characteristics can be effectively eliminated, thus particle mass concentration (PM) could be calculated according to particle number concentration (PN) in each channel. The results show that the developed monitor has good accuracy by comparing it in atmospheric air with reference methods of PM2.5/PM10.

The article presents the method of investigating the pressure distribution on uneven surfaces, used for the development of a new, modern series of school furniture that meets the relevant health, pedagogical and legal requirements. During the examination of pressure conditions on school chairs with a flexible tactile sensor, which was primarilly developed for this purpose, exact data on the immediate differences in contact pressures between the person sitting and the seat are obtained. Based on this information, it is then possible to optimally shape the seats during their design and subsequent production according to the age of the sitters and the needs of the organizational form of teaching from the point of view of the specific character of the teaching environment. Technical parameters of the flexible tactile sensor depend on the shape and number of electrodes, as well as on the conductive inks used, they are stated and dis-cussed within the article. Due to the large number of collected data, a robot, otherwise used in teaching, was used for obtaining of individual loading characteristics of the proposed sensor. At the end of the article, the results obtained by the statistical processing of the measurements are summarized and dis-cussed.

The paper focuses on the application of machine learning techniques and optimization algorithms in predictions and controls of grinding temperature variations. The major thrust of investigation has been on how the different input conditions such as feed, depth of cut, and cooling conditions influence grinding temperatures and the effectiveness of these conditions on the control of their thermal effects. Three machine learning models: Random Forest (RF), Gradient Boosting (GB), and Artificial Neural Networks (ANN) were then used to develop prediction models for the grinding temperature on both face and shoulder of the workpiece. Out of all the models, RF achieved a much higher R² score of 0.96 as compared to both GB and ANN, indicating its greater predictive performance. Furthermore, Bayesian optimization and genetic algorithms were employed in model optimization and grind parameters and cooling condition optimization to avoid damages caused due to temperature. MQL has been found to be highly superior to the inefficient dry cooling methods in terms of achieving lower grinding temperatures and, therefore, seems to be most suited as an eco-friendly yet practical cooling solution as based on this comparison. Altogether, these research findings indicate that AI-based techniques and traditional optimization methods can lead to much better grinding in terms of efficiency and energy consumption, as well as surface quality, and assist towards greener manufacturing altogether.

It is well known that the porosity of a product can have a negative effect on the mechanical properties of the product. For this reason, its control is very important. Porosity can be assessed by two methods - destructive and nondestructive inspection. However, the identification of very small pores is still very difficult for metallic materials, as the pore size may be below the resolution of most commonly used NDT techniques. In addition, different types of pores may be present in a single part, with one type usually dominating. Proper identification of porosity is essential to estimate the impact on structural properties. For pore assessment, as for other defects, the description of the morphology, distribution and frequency is important. This article deals with the comparison of methods designed to determine the porosity of products that have been manufactured using Laser Directed Energy Deposition – L-DED additive process. The samples were made from AISI 316L stainless steel. The porosity of these samples was assessed using destructive and nondestructive methods. Subsequently, their comparison was made in relation to the detection of different pore sizes. The samples were subsequently subjected to the HIP process (Hot Isostatic Pressing). For these samples, the changes that occurred in the material as a result of this process were subsequently quantified. This process should have a positive effect on improving the quality of the product produced by AM technologies, e.g. by reducing the number and size of pores.

The current study investigates the influence of the dressing process of the vitrified bonded microcrystal alumina grinding wheel on the roughness of the machined surfaces in cylindrical grinding of Ti6Al4V alloy using different types of stationary diamond dressing tools. For the research, four types of dressers were selected, which differ from each other by number, size and location of diamond cutting elements. Each dresser has been tested at four different dressing feed values with the same dressing depth. Two sets of experiments were conducted to determine the tendency of grinded parts roughness parameters change depending on the dressing feed for each type of diamond dressing tool at two values of grinding feed. A comparative analysis was carried out to show the dressing feed influence and the effect of the diamond dresser type select on the roughness parameters of the grinded surfaces.

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.

The present paper discusses important aspects of residual stress measurements in forged shafts with defects using the X-ray method. A random population of shafts was selected for the study, for which, depending on the type of rolling process, turning was performed, measuring stress changes after successive machining passes. In the forged shafts studied, the existence and location of internal defects were identified using computed tomography. The impact of internal defects on the stress distribution on the surface of the machined workpiece was observed. It was observed that the use of the X-ray method to measure residual stresses makes it possible to determine the state of stresses and their distribution, which is crucial for the safety and durability of shaft-type parts, and allows the impact of a defect on the distribution of residual stresses to be identified. On the basis of the results obtained, it was observed that there is a correlation between the occurrence of internal defects in forged shafts and the distribution of residual stresses in characteristic sections along the length of the shaft after machining

This study aimed to evaluate the change in the microstructure of MoNiCr nickel-based alloy because of specimen surface modification by laser shock peening (LSP) followed by heat treatment using the hot isostatic pressing technology (HIP). Specimens which were cut from casted ingot had 7 mm in thickness and 117 mm in diameter. LSP surface modification was performed in a 60x60 mm square grid on the central part of each of them. Different values of laser power density in combination with or without tape or underwater condition were used for that operation. Specimens were then cut in half. One part each of them was left in LSP surface treatment conditions, and the other was heat-treated using HIP. Heat treatment was done in an argon atmosphere using 1050 °C temperature and 120 MPa pressure. Several microscopy techniques were used to evaluate changes in specimen’s microstructure caused by LSP and HIP. Optical profilometer was used to evaluate change in surface roughness. Op-tical and scanning electron microscopes were used to evaluate surface microstructural changes caused by LSP and HIP. Metallography analysis was supplemented by HV0.01 microhardness measurement. The results of the experiment showed that LSP caused plastic deformation of the surface, which in-creased with the applied laser energy density and the number of passes. Microhardness increased due to LSP to a depth of 0.7 mm from the specimen's surface. The HIP process caused decrease in surface hardness and recrystallization of the grains structure in some cases

The propagation and velocity of the deformation wave in the thin isotropic plate is investigated. The deformation is induced by the stroke of impact body onto the facial surface of the plate. The plate is supported perpendicularly. The excitation of the plate oscillation is initialized by a unit force (Heavi-side’s jump). The impact body has a rounded facet by radius c = 2.5 mm. Hook's material model and Kirchhoff’s and Flüegge’s geometric model have been investigated. The analytical solutions for both models are presented. The MATLAB script has been assembled to solve material and geometrical mod-els. The results were compared for two selected points on the surface of the plate. Plate deformation was recorded at two points T1 (at a distance of 20 mm from the impact location on the x axis) and T2 (at a distance of 20 mm from the impact location on the y axis).

Aiming at the problem of feature extraction and fault recognition for rolling bearings, a fault diagnosis mthod based on multi-scale entropy and ensemble learning is proposed in this paper. Firstly, the variable mode decomposition algorithm is used to decompose the vibration signal, and then the cross-correlation number method is used to reconstruct the signal to realize the signal denoising. Subsequently, in order to improve the effectiveness of feature extraction for rolling bearings, a feature extraction method based on Refined Composite Multiscale Reverse Permutation Entropy (RCMRPE) is proposed. Then, in order to improve the accuracy of rolling bearing fault identification, this paper proposes a fault diagnosis model based on Stacking- CatBoost ensemble learning. Finally, relevant experiments were conducted on signal denoising, feature extraction, and fault recognition. The RCMRPE entropy extraction method was compared with the common entropy extraction methods, and the proposed fault diagnosis model was compared with the common machine learning models. The experimental results show that the feature extraction error based on RCMRPE is small and can comprehensively reflect the actual fault information of bearings; the accuracy and recall of the fault diagnosis model based on Stacking- CatBoost ensemble learning are both above 99%, and the diagnostic effect is significantly better than other models.

The fuel content of engine oil is a significant factor affecting its degradation processes, lubricating properties and overall service life, especially in the case of modern internal combustion engines equipped with turbocharging, direct injection and exhaust gas recuperation systems. This study analyzes the dilution of engine oil with fuel in diesel and gasoline engines of vehicles with different degrees of wear, represented by the number of kilometers driven. The main objective of the research is to identify the relationship between the fuel concentration in the oil and changes in its physicochemical properties, as well as the potential impact of this phenomenon on the service life of the lubricant and the suitability of the set replacement intervals. The fuel content was quantified using precise quantitative spectrometric analysis, which allowed comparing engine oil samples taken under different operating conditions, including hot and cold starts, urban and highway operation. The results obtained show that vehicles with higher mileage and higher frequency of cold starts exhibit significantly higher rates of oil dilution by fuel, which directly affects the reduction of its viscosity and lubricating ability. The findings of this study provide important insights for the development of recommendations in the field of engine maintenance, especially with regard to optimizing engine oil change intervals, in order to prevent excessive wear and damage to engine components due to lubricant degradation.

This study focuses on addressing the challenges faced by individuals with physical disabilities, particu-larly lower body impairments, by developing a stump socket using Reverse Engineering (RE), 3D Printing, and QFD. The integration of these three methods is something new in product design devel-opment, especially prosthetic products. The research adopted a three-step methodology: 3D scanning the stump, obtaining precise measurements, and fabricating a stump socket using fused deposition modeling (FDM) technology. QFD will produce technical requirements (TR) derived from consumer needs and brainstorming with prosthetists. TR will be the basis for developing the socket design in the 3D Scanning phase. The scanning process utilized Polycam, and the 3D models were refined with Meshmixer. The socket was fabricated using PLA+ material to ensure cost efficiency and customiza-bility. Experimental results demonstrated the accuracy and feasibility of the designed prosthetic sock-et, with a layer thickness of 0.2 mm and printing temperatures up to 215°C. The study highlights the potential of RE and 3D Printing to address the unique anthropometric variations of Indonesian users, overcome the limitations of conventional crutches, and reduce production costs compared to imported prostheses. This approach demonstrates a scalable and innovative solution to improve accessibility and quality of life for individuals with physical disabilities while contributing to economic inclusivity.

Binderjetting technology works on the principle of line injection moulding, using metal powder and liquid binder as input material, which is uniformly applied by print heads to the previous layer using a nozzle. By successively applying each layer, the desired shape of the designed component is obtained. The technology offers a large number of advantages which include the possibility of using any printing powder that may contain functional graded materials. Furthermore, it is a green manufacturing technology where we can reuse unused metal powder in the next printing cycle after following the prescribed process. As a result, we characterize this technology as a near-waste-free production of metal parts. The research aims to analyse the impact of different orientations of printed parts within the workspace on the mechanical properties of the resultant components. Additionally, the study aims to compare these mechanical properties with the specifications recommended by the metal powder manufacturer and findings from previous research studies. Based on the experimental measurements carried out, we can conclude that the influence of the orientation of the parts in the workspace has only a minimal effect on the mechanical properties of the manufactured parts.

The influence of the casting method on the microstructures of Al-Cu-Li-Mg-Zr-Sc was examined. The techniques include mold casting, twin-roll casting, and melt spinning. Estimated solidification rates up to 107 K·s−1 produce dendritic solidification with eutectic cells ranging from 500 nm to 50 μm, decorated by primary phase particles with thicknesses from 200 nm to 3 μm. Exceeding this solidification rate results in near-diffusionless solidification, which traps more solutes in the matrix. This type of solidification yields a more supersaturated material with nearly 90% of the total Cu content in the matrix and a fine dispersion of nanoscale spherical precipitates below 100 nm in diameter. The small addition of Sc during casting primarily affects the material at low cooling rates, where primary Sc-containing particles modify the grain boundary shape.

The fatigue assessment of a Class 300 valve body with a bore diameter of 450 mm under vari-ous pressures is discussed using Section VIII, Division 2 of the ASME BPVC. Finite element analysis (FEA) results are compared to fatigue test results, and correlations are obtained. The material used for the valve is A216 WCB, which is widely used for making API ball valves. Elastic stress analysis was used to study the influence of various parameters on the results. This method is widely accepted and is used for static components. The body and flange de-signs were performed in accordance with ASME and API standards. Various pressure loads were applied to the inner surface of the valve body, ranging from 4 MPa to 6 MPa. The defor-mation, equivalent stress and stress intensity over the critical areas were analyzed using AN-SYS Workbench. As the pressure increases, the maximum compressive stress over the valve body surface also increases. However, the design of the valve for a pressure of 5.1 MPa (for a Class 300 valve) remained within the safe limit. Increasing the pressure beyond 5.1 MPa also indicates a safe design; the valve can withstand pressure up to 6 MPa (beyond the design pres-sure).

In warehousing logistics, the sorting and transportation efficiency of goods is still one of the important factors limiting the rapid development of logistics. At present, most regions still use manual sorting with low efficiency and high cost. Especially in some special work areas, such as high temperatures, severe electronic radiation, and areas with heavy long-term work tasks, urgent need for small robots to replace manual labor. In order to solve the inconvenience that small places such as logistics only use manual sorting, It is necessary to design a small-sized weight sorting and transportation robot, which can wait for receiving goods at a designated location, judge and identify the weight of goods by itself through PID intelligent control algorithm, which can move forward at a constant speed, and transport its weight to the designated position and unload it. Constant speed can make the trolley travel more smoothly and load and unload goods more smoothly, which is of great significance.

The paper presents work aimed at building practical applications of virtual reality (VR) in manufacturing environments. It contains studies of the optical properties of cameras and lenses aimed at the selection of an optimal set (camera, adapter, lens) for the realization of recordings and video transmissions in stereoscopic format for VR. In response to the increasing trend in the number of applications of VR systems in the industry, works have been initiated with the purpose of building a system levelling image noise identified thus far as an obstacle to the effective utilization of VR in production systems. It was considered that picture error correction can significantly increase an already big data stream from the recordings. Based on it, a set of parameter values was defined which determined the selection of study equipment. Three research areas were set: the verification of the optical correctness, the study of image defects and their correction and the determination of the maximum optical resolution and the achievable image parameters in various lighting and environmental conditions. An example was presented for the application of a projected system for the monitoring of undesirable events/movement at work stands and key areas of production halls as well as training in the high-risk production zones.

Currently, there is a demand in the aerospace industry for a more effective and non-invasive milling technique for powder metallurgy γ-TiAl alloy. The primary objective of this research is to examine the surface milling process of a γ-TiAl alloy generated by powder metallurgy. The primary objective of this study is to examine the impact of process parameters on the surface roughness and cutting force of the alloy, with the aim of optimizing both surface roughness and cutting force. The response surface method was implemented to examine the milling process, and the NSGA II algorithm was employed to optimise surface roughness, cutting force, and material removal rate. The findings indicate that the cutting depth exerts a significant impact on both the surface morphology and surface roughness. The available data indicates a clear correlation between the depth of cutting and the rate of feed, as well as the resulting assessment of surface roughness. Nevertheless, the first rise in spindle speed is associated with a subsequent increase in surface roughness, followed by a subsequent drop of a lesser magnitude. A minimal threshold for surface roughness has been established at 0.203μm. The spindle speed exerts the primary impact on the cutting force. There exists a positive link between the cutting force value and both the cutting depth and feed speed, as the cutting force value has a positive correlation with the incremental changes in these variables. Nevertheless, the relationship between cutting force and the observed trend is non-linear, exhibiting an initial decrease followed by a rise when cutting force is augmented. The minimal cutting force necessary was quantified as 112.3 N. Subsequently, a regression analysis was employed to develop a correlation model between surface roughness and cutting force. and machining parameters. The confirmation of the coefficients' validity in the model was achieved via the utilisation of analysis of variance (ANOVA) and residual analysis. The main goal of developing a machining parameter optimisation model is to limit surface roughness and cutting force, thereby improving operational efficiency. The NSGA-II method is utilised to tackle the problem of multi-objective optimisation, leading to the attainment of the optimal parameter solution. The purpose of the verification test is to evaluate the precision of the forecasts generated by the optimised model. The work holds importance in its analysis and juxtaposition of diverse processing factors, alongside the use of multi-objective optimization methodologies.

This study examines the two-pass hot rolling process of 6061 aluminum alloy wire, focusing on forming load measurement to evaluate process stability and its effects on dimensional accuracy and mechanical property uniformity. Using response surface methodology (RSM), process parameters and forming loads were analyzed to assess their influence on mechanical property distribution and verify the applicability of load measurement in process quality evaluation. A full-factorial finite element simulation was conducted to investigate the effects of pre-forming section reduction rate, material temperature, roll speed, and friction coefficient. Experimental results indicate that forming load measurements effectively capture variations in initial wire temperature and reveal the influence of material velocity and roll speed. Load data also identify the Spike phenomenon caused by improper roll positioning, leading to abnormal load surges and reduced mechanical property uniformity. The strong agreement between experimental forming loads and FEM simulations validates the reliability of the proposed measurement method. This study provides a basis for wire rolling process design and machine learning-based quality prediction, supporting advancements in smart manufacturing applications.

Traditional assembly sequence solving methods often face problems such as combinatorial explosion and low efficiency in solving complex products with multiple parts. To improve the level of assembly sequence planning (ASP), an interference matrix is established to express the basic assembly information of a product. Taking the stability of the assembly sequence, the number of assembly direction changes, and the number of assembly tool changes as evaluation indicators, a fitness function is constructed. An improved particle swarm optimization (IPSO) approach for ASP is developed based on the peculiarities of the ASP issue. Redefining particle positions, velocities, and their update operations, and introducing mutation operators in genetic algorithm (GA) to improve the ability of PSO algorithms to jump out of local optima. Furthermore, the algorithm's convergence speed is enhanced by adjusting the value of the inertia weight. Finally, an example is provided to demonstrate the IPSO algorithm's usefulness and efficiency.

The article deals with sinterhardening process of lean Cr-Mo prealloyed steel for moderately loaded applications. New material Astaloy CrS with low alloying volume of chromium and molybdenum was analyzed as possible basis for sinterhardening process. Standard mechanical properties of frequently used and more expensive materials such as DistaloyDH and Astaloy CrM are chosen as a compara-tive criterion. Astaloy CrS+0.85%C samples with different compaction densities and Ni content were studied, mechanical properties and hardness after sinterhardening process were compared. The influ-ence of additional high-temperature sintering on mechanical properties was assessed. The micro-structure of the sinterhardening (SH) and high-temperature sintering + sinterhardening (HTS+SH) samples was studied quantitative analysis of the phase was given. As result, tensile strength greater than 900 MPa and hardness greater than 33 HRC can be obtained for investigated material.

In the actual factory production, there are often cases of liquid flow accumulation, and most of the controllers used in the project are PLC. Usually, the flow meter is used to measure the instantaneous flow, and then the analog (4-20mA) signal is transmitted to the PLC, and the PLC accumulates the cumulative flow within a certain period of time according to the instantaneous flow transmitted by the flow meter. Due to the floating point type of PLC, the direct accumulation will not reach the accuracy standard, and the cumulative error will occur. In order to eliminate the cumulative error, this paper proposes an improved algorithm based on Kahan 's algorithm. The improved algorithm greatly reduces the error of the cumulative flow than the original Kahan algorithm. The reduction of error is of great significance to the data analysis and production calculation of liquid or solid flow in the field of process industry control.

Hollow TiO₂ architectures are attractive for catalysis and sensing but typically produced by wet-chemical templating and sub-micron sizes. Here we demonstrate a dry, template-free route to nanoscale hollow shells by combining DC magnetron sputtering with in situ TEM heating. Heating to 900 °C produces sub-50 nm TiO₂ hollow shells with ~20 nm compact walls via oxidation-driven Kirkendall hollowing. The oxide evolves from amorphous at low temperature to anatase locally (~500 °C) and then to a rutile/brookite mixture by ~600 °C. The hollow architecture withstands a temperature of 900 °C without measurable sintering. Beam-off regions and ex-situ air annealing show the same hollowing and phase evolution, confirming a thermally driven, not beam-induced, transformation reproducible in air.

This paper explores the investigation of a natural alloy processed using the rapid solidification tech-nique. The study involves the reduction of manganese nodules through aluminothermy with a 20 wt. % excess of aluminum, followed by further processing of the resulting alloy using the melt-spinning process. The obtained melt-spun ribbons were subjected to a comprehensive analysis, including X-ray diffraction, scanning electron microscopy for microstructure observation, and EDS analysis for local chemical composition. The research unveiled that the rapidly solidified ribbons consist of several key phases, including β-Mn, the Heusler phase Mn2FeSi, and an intermetallic phase (Cu,Mn)3(Al,Si). Im-portantly, the phase composition exhibited notable differences from that of the as-reduced alloy, with a reduced number of phases in the rapidly solidified ribbons. Notably, the phase composition re-mained stable even after annealing, demonstrating the robustness of the rapidly solidified material. Impressively, the material exhibited a remarkable hardness of approximately 800 HV 0.1, even after 100 hours of annealing at temperatures of 500 and 750°C.

Rising energy demands together with environmental concerns have spurred increased focus on renewable energetics, leading to the widespread installation of photovoltaic power plants worldwide. Due to the unique solar dispersion angle over the world, different racking systems are also the subject of keen interest in mechanical support equipment. Different constructions require welded joints of construction parts with massive strain while exposed to weather conditions. Therefore mechanical properties of the joints of these systems are also being studied. This research is focused on the mechanical properties (such as microstructure and microhardness) of aluminium laser welded joints under different welding parameters. The main aim of this paper is to find ideal parameters of laser welded aluminium profiles ensuring durable construction for PV panels.