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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.

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.

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).

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.

There are currently many control methods for linear motors, and the focus of controlling the motor should be different for different application needs. In general applications, simple PID control can meet the application requirements, but in precision motion situations with high requirements for motion accuracy, response speed, and stability, PID control is often difficult to achieve satisfactory control results, which requires the application of more advanced control strategies to complete. At present, combining multiple control algorithms and concentrating the advantages of each algorithm while trying to overcome each other's disadvantages has become a major trend in the development of motor control theory. High speed, high efficiency, high precision become the development direction of the current numerical control equipment, linear motor because of its unique performance, now widely used in a variety of precision positioning occasions. Aiming at the requirements of high speed response and high precision of linear motor, the linear motor system is designed based on LabVIEW software and NI acquisition card, including hardware composition and software algorithm. In the LabVIEW simulation environment and the actual control system, the conventional PID algorithm and fuzzy PID algorithm are used to control, and the control results are compared. The experimental results show that compared with the PID control, the fuzzy PID algorithm has obvious advantages in improving the control accuracy, anti-interference ability, reducing the overshoot and improving the system response speed.

Fused silica is a key material for high-precision applications such as micro-optics and microfluidics. One route to improving direct laser writing (DLW) of fused silica is the use of shorter laser wavelengths, which enable tighter focusing and enhanced absorption. In this study, the influence of process parameters on surface quality and material removal during DLW using a deep ultraviolet (DUV) ultrafast laser (257 nm, 1 ps) was investigated. A full-factorial design of the experiment was used to identify conditions that optimise both surface quality and ablation efficiency. Surface roughness as low as Sa ≈ 200 nm and material removal rates up to 0.048 mm³∙min-1 were achieved. Conditions that led to surface degradation were also identified. Finally, the optimised parameters were applied to fabricate a microfluidic demonstrator. These results confirm that DUV ultrafast DLW is a powerful technique for fabricating high-fidelity features in fused silica with exceptional precision and quality that can be used for micro-optics or microfluidics devices.

The Mg-Y-Zn alloy system is well known for its outstanding combination of high strength and ductility, even at relatively low concentrations of alloying elements. This exceptional performance is primarily at-tributed to its characteristic microstructure, which features Long-Period Stacking Ordered (LPSO) phas-es and the distinctive Mille-Feuille Structure (MFS). Kink-induced strengthening, developed during thermomechanical processing, has emerged as a promising strategy to simultaneously enhance strength and ductility. In this study, the beneficial effect of pre-deformation aimed at introducing additional kinks into the microstructure prior to extrusion is demonstrated. The subsequent extrusion process promotes dynamic recrystallization (DRX), generating fine DRX grains while preserving kink structures in the non-DRX regions. As a result, the yield strength is enhanced by approximately 80 MPa, accom-panied by a slight improvement in ductility.

The restriction of lead content in alloys for the production of the solder based on the Directive of the European Parliament and of the Council of the European Union of 08.06.2011 which is also known as RoHS (Restriction of the use of certain Hazardous Substances in electrical and electronic equipment), had a very positive impact on the research of lead-free solder alloys as well as on the economic impact on the production of solders. It opened the door to issues relating to the mechanical properties of lead-free solders and the microhardness of formed joints, increasing their quality and efforts to reduce production costs. Lead, as an element that is part of the earth's crust, is also men-tioned in his study by u-Wook Lee, Hoon Choi at all: Toxic effects of lead exposure on bioaccu-mulation, oxidative stress, neurotoxicity, and immune responses in fish, in which he states how lead atoms can form a flexible bond with oxygen atoms and lead exposure causes a wide range of physiological effects. Besides the production efficiency increase, without the need for manual re-moval of so-called slagging, the moderation of oxide formation on the melt surface standing for the increase of the yield of the total amount of solder represents one of the many factors influencing the production of lead-free alloys for tin-based soldering. This work deals with the issues of material selection for the production of lead-free solders. Temperature affects the formation of different phases when there is the change in the concentration of the elements involved because it can be negative aspect for soldering. Therefore, it is necessary to have detailed knowledge on all the process which takes place during the temperature changes.

This paper details a systematic machine learning workflow designed for the prediction of surface roughness in grinding operations using key machining parameters. Those parameters are: Depth of Cut, Feed Rate, Work Speed, and Wheel Speed. The model was trained and validated on a data set which comprised experimental measurements of those parameters and their corresponding values of surface roughness. Three machine learning models, Random Forest, Gradient Boosting, and LightGBM, were developed and tested based on accuracy of prediction of the surface roughness. The validation of all three models was performed using performance metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). Among the models, LightGBM exhibited the highest value of performance with the lowest error ob-served MSE 0.0047, MAE 0.064, and RMSE 0.09 respectively while an R-squared value closest to zero. (-0.02). The moderate performance was shown by the Random Forest which presented an MSE of 0.0063, MAE of 0.085, and RMSE of 0.10 while the Gradient Boosting recorded the highest error rates which may indicate that it is the least effective model. It's an effective application of machine learning in predicting surface roughness and gives an insight into machining process optimization through predictive modelling.

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 tempering procedure of quenched 54SiCr6 spring steel was analyzed using continuous heating dilatometry, isothermal dilatometry, metallography, and hardness measurement. The dilatometry was performed on four different steel modifications with graduated Si content and with two levels of Cu. Metallography and hardness measurement were analyzed only on samples with one Si level. Two types of tempering procedures were compared in this experimental program. The first one was sim-ple one-step tempering, the other was a special procedure of strain assisted tempering (SAT), which includes double tempering and strain applications between tempering. A dilatometry analysis with the support of metallography contributes to the material behaviour explanation, which is considerably different in both processing cases.

The polyamide PA6 composites reinforced with carbon fibres (CF) are widely studied due to their properties and their high strength to weight ratio. Good adhesion between a filler and a matrix is es-sential for enhancing properties of a resulting composite. This study investigates the effect of the short CF content and the used CF treatment on mechanical properties of the PA6-CF composites. The composites were subjected to tensile, flexural, compression, hardness and Charpy tests as well as dynamical mechanical analysis. An atomic force microscopy was employed to investigate topography of the CF and the composites. Initially, the properties of the composites were improved through the oxidation of the CF in HNO₃. Subsequently, to further enhance these properties, the oxidized CF were grafted with nano-SiO₂. The CF content in the tested composites varied from 10 wt% to 60 wt%. The most significant improvement of the tested properties was observed at the CF content of 40 wt%.

The study focuses on an evaluation of mechanical properties of the H13 tool steel manufactured by the material extrusion and further comparison with conventionally produced material. Notably, for achieving sufficient surface quality of functional parts further post-processing is required. Thus, a comprehensive investigation, encompassing hardness, ultimate tensile strength (UTS) and yield strength (YS) measurement, microstructure, and machinability was performed. The material extrusion, an increasingly utilized additive manufacturing (AM) technique, offers a viable alternative to the prevalent laser powder bed fusion (LPBF) methods. This method enables a creation of complex geometries using various materials. The investigation revealed that the horizontal orientation of parts yielded the highest mechanical properties, reaching the ultimate tensile strength of approximately 1200 MPa. Additionally, the material exhibited the hardness of 47 HRC in the as-built state. The conventionally produced steel resulted in the higher UTS and YS in comparison to the AM material. The machinability of the as-built material in regard to cutting forces and surface roughness was also evaluated Lower surface roughness was achieved by decreasing feed per tooth. Optically measure material porosity was 6.13 % with maximum pore size 7.43 µm. The primary objective of this research is to optimize the mechanical properties of H13 tool steel post-printing, with a broader aim to apply the gained insights to improve other materials produced by the material extrusion.

In view of the linear motor servo control system has the advantages of high speed, high response characteristics, in order to adapt to the motion precision, response speed and stability requirements of high precision movement occasions, combined with fuzzy control theory, the design of linear motor platform servo control system fuzzy adaptive PID control algorithm. At the same time, based on LabVIEW software, combined with USB-6009 data acquisition card produced by NI company, the experimental platform for linear motor motion control is designed. Through simulation experiments, the position tracking accuracy, disturbance resistance and response speed of linear motor can be greatly improved. The experimental results achieve the expected control effect, which provides a control method for related research. Therefore, the fuzzy adaptive PID composite control can combine the advantages of both and improve the control effect.

The microstructure of single-phase (as-cast) and two-phase (after heat treatment processes) Fe3Al-based alloys doped with silicon, molybdenum, and vanadium/tungsten have been studied. The grain size and phase composition have been determined. The evolution of the secondary phase amount, morphology, and distribution during the heat treatment process has been investigated. Both alloys were heat treated at 1200°C/2 h and, subsequently, at 900 °C. For both alloys, the secondary phase formation started after heat treatment at 900°C /4h, and their amount depended on the annealing time. Vanadium atoms remain in the solid solution, while tungsten atoms partially participate in sec-ondary phase formation.

Fused Filament Fabrication (FFF) printers are usually designed to create a product with one single ma-terial. Some of them allow fabricate items in multiple colours but creating a single product consisting of two different materials remains an advantage of industrial 3D printers only. The aim of this research was to develop a design of a device with two printheads which enables to print products with two different materials, compatible with a cheap and commonly available FFF device Prusa i3MK2S, and the subse-quent production of a prototype. A key aspect of the design was the hardware compatibility of the device with the given printer while maintaining the maximum possible printing area.

Unfortunately, in connection with the application of the Actavia anti-plagiarism system, we cannot accurately describe our paper, which deals with the production of non-compact materials based on zinc and tin alloys, which have a higher density than aluminium (ρ = 2700 kg.m^-3 ) and its alloys, such as zinc alloys (ρ = 6980 kg.m^-3 ) or tin (ρ = 7580 kg.m^-3 ). Test samples were prepared from these materials, which were characterized by material non-compactness based on the use of NaCl particles. For this purpose, two different size groups of NaCl particles (3 to 5 mm and 5 to 7 mm) were used. In the production of non-compact metallic materials, it is assumed that half of the volume of the workpiece cavity will be occupied by NaCl particles and half of the volume of the work piece cavity will be filled with a melt of the relevant alloy (ZnAl4Cu1 or Sn89Pb). This is different from our previous experiments [24, 25]. In the case of this paper, the fabrication consisted in the fact that in a spe-cial preparation, the melt of the respective alloy was forced between the NaCl particles. The produced samples of non-compact material were analyzed and their specific gravities were determined. In a standard manner (as may be against the findings of the Actavia system), the microstructure was observed on an electron microscope and EDS analysis was also performed. It is anticipated that the non-compact materials thus produced from these two alloys will be used to produce not only filters but also bodies for liquid hydrogen storage.

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.

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 article presents the results of the use Six Sigma DMAIC cycle to improve workplace safety and decrease the cost associated with work accidents in the company from the automotive branch. Selected tools of the DMAIC cycle were used at each stage: the project card and the Pareto-Lorenz diagram at the define (D) stage, the matrix diagram at the measure (M) stage, the Ishikawa diagram with the verification of causes at the analysis (A) stage, the 5WHY method at the improve (I) stage and the c control chart at the control (C) stage. Each of the successive stages was based on the results of the previous one in order to achieve a lasting solution for the analysed problem by the implementation of remedial measures. Because of the implementation of remedial measures, the level of work safety in the examined company was improved. The DMAIC analysis made it possible to identify the main causes (Xn) of accidents at work and to objectively evaluate them in order to discover the root cause (Xn!) of the problem. The root cause turned out to be inadequate protection of the lathe due to the protective cover installed too far away from the lathe chuck, which resulted in the catching of protective sleeves or gloves of the lathe operators and accident events in the form of upper limb damage. The solution to this problem was to reduce the gap between the guard and the lathe chuck by adjusting the guard so that no more items of workers' clothing were caught while the machine was running. The article proves the effectiveness of using the Six Sigma DMAIC cycle in analyzing and improving the state of occupational safety and is an incentive to use this cycle and a specific set of tools to analyze similar problems.

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.

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.

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.

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.

This research focused on the production of brake pads reinforced with bronze fibers to see the anticipated performance principles for braking systems. Three unique amalgamated formulations, labeled BRZ-I, BRZ-II, and BRZ-III, were set by varying the bronze fiber content to 5%, 10%, and 15% by weight. The tribological characteristics of these composites were systematically evaluated to determine their effectiveness. Traditional manufacturing processes were used in developing the brake pad. Various properties such as physical, chemical, mechanical and tribological possessions were assessed by means of chase test rig. Worn-superficial examination stayed carried out by using chase test rig. Base results it was evident that the 10 weight percentages of the bronze fibers showed better physical, chemical, mechanical and tribological properties. Chase test results confirmed that the composite brake pad developed with 10 weight percentages of bronze showed better results at higher pressure-speed conditions than others due to better plateau formation and less wear rate. The results obtained after performing various performances such as physical, chemical, mechanical and tribological properties concluded that the bronze fiber possessed lesser wear and stable coefficient of friction.

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

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.

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.

Benzoylation treatment represents a promising strategy to enhance adhesion between plant fibres and polymer matrices. This study aims to improve the properties of epoxy composites using benzoylated fibres of Demostachya bipinnata (Darbha). The analysis focuses on the impact of chemical treatment on the physicochemical and mechanical characteristics of the fibres and resulting composites. After retting and alkaline pre-treatment of the fibres, benzoylation using 10 wt% concentrated benzyl chloride is applied to introduce benzene groups, thereby enhancing the density and chemical stability of the fibres. Results highlight increased density (1869 kg.m-3) and enriched crystalline cellulose composition in benzoylated fibres (BDE) compared to untreated fibres (UDE). FTIR analysis confirms significant structural modifications with the introduction of additional carbonyl and carboxyl groups, reinforcing essential interfacial bonds. Mechanical tests reveal 30% higher tensile and flexural strength for epoxy/BDE composites compared to epoxy/UDE composites, demonstrating the effec-tiveness of benzoylation in improving mechanical properties. Thermal analysis also shows improved thermal stability of BDE fibres, crucial for demanding industrial applications. In summary, this study demonstrates significant enhancement in performance of benzoylated Darbha fibres as composite reinforcements, opening new avenues for their use in sectors such as automotive and construction, where high mechanical strength is crucial.

To design an engine intake system that complies with FSC racing regulations while achieving enhanced operational stability, this study conducts a comprehensive review of domestic and international research advancements in racing engine intake systems. Through computational fluid dynamics simulations performed in Workbench Fluent, critical structural parameters of the restrictor valve were optimized, resulting in a 12.06% improvement in outlet mass flow rate compared to the baseline design. A three-dimensional parametric model of the racing intake system was developed using Siemens NX platform. Taking the intake plenum chamber as a representative component, this research systematically analyzes the CNC machining process for the mold of the pressure stabilization chamber. The investigation encompasses toolpath generation, cutting simulation verification, and ultimately implements the optimized NC program on machining centers for physical manufacturing. The fabricated mold exhibits high dimensional accuracy and superior surface finish, providing both theoretical guidance and practical manufacturing references for intake system development. This integrated approach combining numerical optimization with advanced manufacturing techniques demonstrates significant potential for performance enhancement in motorsport engineering applications.