This study examines the effects of cutting parameters on surface roughness (Ra) for three aluminum alloys AL7075, AL6061, and AL5052 using a Taguchi L16 (43) design, smaller-is-better signal-to-noise (S/N) analysis, and ANOVA. Experiments were conducted on a 3-axis CNC milling machine with a 2-flute & Oslash;6 mm carbide end mill. Overall, AL7075 produced the smoothest surface (minimum Ra = 0.143 mu m), followed by AL6061 and AL5052. The observed trends are consistent with machining mechanics: increasing cutting speed suppresses built-up edge (BUE) and vibration, whereas larger feed and depth of cut (doc) increase chip load and tool deflection. For AL7075, doc and feed were the principal factors (borderline significant in S/N-ANOVA); the mean-optimal setting was S3-F1-D3 = Spindle L3 (N = 2500 rpm), Feed L1 (f = 150 mm.min(-1)), doc L3 (0.8 mm), while the robust-optimal setting was S4-F1-D3 = Spindle L4 (N = 3500 rpm), Feed L1 (f = 150 mm.min-(-1)), doc L3 (0.8 mm). For AL6061, spindle speed was strongly dominant (approximate to 80.5% contribution; p < 0.001), with S3-F1-D1 = 2500 rpm, 150 mm.min(-1), 0.4 mm as the mean optimal and S3-F1-D2 = 2500 rpm, 150 mm.min(-1), 0.6 mm as the robust-optimal combination. For AL5052, factor contributions were relatively balanced and not significant at alpha = 0.05; the mean optimal setting was S4-F2-D3 = 3500 rpm, 250 mm.min(-1), 0.8 mm, whereas S/N indicated S1-F4-D4 = 500 rpm, 450 mm.min(-1), 1.0 mm (requiring confirmation tests). Across alloys, doc and spindle speed emerged as the most practically influential factors. These results establish ANOVA validated mean and robust (S/N) settings as process guidelines to minimize Ra; confirmation tests are recommended.
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 elemental 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 performance 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.
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.
Additive manufacturing (AM), more commonly known as 3D printing, is a highly convenient manufacturing process that enables the creation of complex 3D objects. The large body of literature proved that the choice of material, the printing technology used, and the values of certain process parameters all influence the mechanical behavior of the final part. In this study, the Fused Deposition Modeling (FDM) technology was used to manufacture standardized test specimens, which were all subjected to mechanical testing. The main aim of the research was to investigate the effects of various infill patterns, combined with various infill densities, on tensile strength, and thus determine the patterns that enhance better structural performance. Three infill density levels were analyzed along with five infill patterns. A systematic sequence of tensile tests traced the correlation between internal material distribution and measured mechanical properties. By systematically varying the infill parameters, this study provides valuable insights into selecting appropriate configurations for specific applications. The findings contribute to advancements in AM design and material utilization, leading to stronger and more efficient components.
The presented article deals with the influence of different solution annealing temperatures and times on the mechanical properties and microstructural changes of the EN AW-7022 aluminum alloy after two-stage re-aging. Following initial soft annealing, the specimens underwent solution treatment at 475 degrees C/ 20 min, 500 degrees C/20 min, 525 degrees C/30 min and 575 degrees C/30 min, followed by two-stage artificial aging at 120 degrees C/4 h and 175 degrees C/6 h. Mechanical properties were assessed using static tensile testing and Vickers hardness measurements. The highest strength levels were achieved after treatment at 525 degrees C/30 min. Metallographic observations together with SEM-EDS microanalysis confirmed a uniform distribution of strengthening precipitates of the Al2CuMg and Mg(Zn,Cu,Al)2 types at elevated temperatures, while fractographic examination indicated a predominantly ductile fracture mechanism. The results demonstrate that, even in the absence of additional plastic deformation, an advantageous combination of strength, hardness and ductility can be obtained through re-heat treatment. This is particularly beneficial for applications where dimensional stability and minimized residual stresses are required, such as in defense and aerospace components.
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.
High volume fraction SiCp/Al composites are highly valued in aerospace, automotive, and electronic packaging for their exceptional mechanical and thermal properties. To enhance the machinability of these difficult-to-cut materials, this study systematically evaluates ultrasonic vibration-assisted grinding (UAG) against conventional grinding (CG). Experimental comparisons reveal that UAG lowers grinding forces by up to 26%, a benefit primarily driven by the reduction in undeformed chip thickness (UCT). This kinematic modification optimizes the particle removal mode, thereby mitigating severe particle fracture and interfacial debonding. Crucially, the superior surface integrity observed with UAG results from extending the critical threshold for particle fracture, suppressing roughness accumulation at higher feed rates, and amplifying the load-relief effect of increased spindle speeds. These findings provide both theoretical insights and experimental validation for the high-efficiency machining of SiCp/Al composites.
The regulating valve is a key component of the hydraulic control system. The flow characteristics of the regulating valve are an important parameter for the structural design of the regulating valve. The flow coefficient of the regulating valve directly reflects its flow characteristics, so it is significant to calculate the flow coefficient of the regulating valve accurately. Generally, the flow coefficient is determined by the empirical formula method or experimental method, and the accuracy and efficiency of the calculation are relatively low. In this study, a method for calculating the flow coefficient of the regulating valve combined with numerical simulation using CFX is proposed, and the correctness of the method is verified by experiments. Taking the measured flow coefficient as reference, the maximum error is 4.3%. In addition, the cavitation numerical simulation of the regulating valve pipeline system based on CFX is carried out. Under inlet pressures of 1.96 MPa, 2.46 MPa, and 2.96 MPa, the volume fraction of liquid water decreased to 94.8%, 92.1%, and 88.1%, respectively. At an inlet pressure of 1.96 MPa, the maximum liquid flow exit velocity reached 400 m/s. A prediction method for the erosion amount of the bottom plate is proposed. The predicted erosion amount of the regulating valve reached 10.88 g after 110 hours of operation. The effectiveness of the proposed method is verified by calculating the actual erosion amount. In this study, the calculation method of flow coefficient and the prediction method of erosion amount can assist in improving the structural design method of regulating valve and its pipeline system, and shorten the design cycle of regulating valve.
Designing patient-specific 3D-printed splints requires accurate limb geometry acquisition. Professional 3D scanners are commonly used for acquiring the 3D geometry of limbs, but they are high-cost and less accessible in routine clinical practice. Photogrammetry, which reconstructs 3D models from 2D images captured by digital or smartphone cameras, provides a low-cost and flexible alternative. This study proposes a practical workflow for lower leg modelling using Agisoft Metashape and AutoCAD, employing two printed markers as scaling references. The two markers with a precisely defined inter-marker distance of 150 mm were printed on an A4 sheet and used as reference objects to scale the photogrammetric model to real-world size. The accuracy of the photogrammetric model was evaluated in GOM Inspect by comparing it with a reference scanned model. The results showed that the photogrammetric model has an average deviation of-0.59 mm. Additionally, a physical fit evaluation with a 3D-printed splint further confirmed good conformity between the splint and the lower leg. The findings show that the accuracy of inter-marker distance plays a critical role in photogrammetric scaling.
The aim of this study is to optimize the mass of FDM replicas of a selected part from VEX educational robotics kits (2 & times;12 Beam, 228 2500 026) while maintaining functional compatibility for school use. Replicas were printed on an Original Prusa MK4 using PLA and PETG, with four infill patterns (Grid, Gyroid, Honeycomb, and Triangular) and infill densities of 15, 25, 40, 50, 60, and 70%. In total, 720 specimens were produced, and 15 original parts were used as a reference set. The reference mass of the original part was 11.349 +/- 0.013 g. The mean mass of printed replicas increased with infill density; PLA specimens ranged from 12.590 to 16.361 g, while PETG specimens ranged from 12.996 to 16.855 g, depending on the selected pattern and density. At the same infill density, Gyroid generally showed lower mass values than Honeycomb. Functional compatibility was verified under secondary school conditions with respect to dimensional fit, connection reliability, and repeated assembly and disassembly in robotic constructions. Mechanical destructive testing was beyond the scope of the present study and will be addressed separately.
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 degrees C on the residual mechanical properties, plastic deformation, and fatigue behaviour of these materials. The results indicate that at temperatures above 300-400 degrees C, a significant degradation of mechanical properties occurs. The residual strength of P235GH steel decreases by more than 60 % at 400 degrees 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 degrees 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 degrees C compared to the original materials.
The objective of this study was to evaluate the influence of through-thickness variability of Young's modulus on the numerical modelling of adhesive joint behaviour. The study combined experimental characterization with finite element simulations. Nanoindentation measurements were used to determine the distribution of Young's modulus across adhesive layers with thicknesses of 0.05 mm and 0.1 mm. Based on these measurements, two-dimensional finite element models of double-overlap joints were developed in Abaqus. Two modelling approaches were analysed: a conventional homogeneous model with constant material properties and a heterogeneous multi-zone model in which Young's modulus varies across the adhesive thickness and is implemented using the USDFLD user subroutine. The results indicate that incorporating experimentally determined stiffness gradients significantly alters the predicted stress field, particularly in regions near the overlap ends where failure initiation is expected. The heterogeneous model provides improved agreement between numerical predictions and experimentally determined failure stresses. These findings demonstrate that accounting for stiffness heterogeneity improves the accuracy of numerical modelling of ultra-thin adhesive joints.
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 analyse 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.
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 & times;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.
A single-stage evaporator with natural circulation was used to densify the plasticizing bath through continuous evaporation and to prepare a solution used in the production of viscose fiber. During the process, sodium calcium sulfate salts were formed, leading to fouling of the heat transfer surfaces in the heat exchangers. This fouling created a layer of deposits that gradually reduced the efficiency of the evaporation process in the evaporator. It was determined that a processing medium with a volumetric flow rate of 6 m3 & centerdot;h-1 required a heat exchanger power of 1448 kW. A fouling layer with a thickness of 0.1 mm reduced the heat exchanger's performance by approximately 40%. When the fouling layer increased to 0.5 mm, the heat exchanger power decreased by nearly 74%, down to 889 kW. The purpose of this paper was to analyze the process parameters of the densification technology in order to identify potential optimizations that could increase equipment availability and reliability. Alternatively, the study aimed to provide recommendations for design modifications to the existing technology.
The paper deals with the influence of various quenching media based on polymer aqueous solutions on the quenching process of carbon steel C45, connecting theoretical knowledge about heat transfer, surface phenomena and phase transformations with experimental verification. Surface phenomena at the interface of the hardened sample and the quenching medium were monitored using a high-speed camera. Cooling curves of the samples were obtained using the method according to ISO 9950 (Determination of cooling characteristics-Nickel-alloy probe test method). The paper contains practical recommendations for optimizing industrial hardening processes, especially when choosing polymer hardening baths as an alternative to water hardening baths and confirms their ability to ensure a more controlled cooling process, reduce the risk of cracks and deformations, and achieve higher hardness of hardened parts.
Wire Arc Additive Manufacturing (WAAM) based on Gas Metal Arc Welding (GMAW) has emerged as a cost-effective, high-deposition technique for fabricating large-scale aluminum components. However, its application to non-heat-treatable aluminum alloys, particularly AA5052 substrates with ER5083 filler, is often limited by thermal instability, porosity, and non-uniform mechanical properties. This study proposes an integrated experimental and artificial intelligence (AI) framework to optimize key GMAW parameters-welding current, wire-feed speed, and welding speed-for improving the mechanical performance of WAAM-fabricated aluminum walls. An L9 Taguchi design and analysis of variance (ANOVA) were employed to evaluate parameter significance. The results showed that welding speed had the greatest influence on tensile strength (approximate to 58.8%), while wire-feed speed and current primarily affected hardness through thermal input and solidification behavior. An Artificial Neural Network (ANN) model was developed to predict tensile strength and hardness with high accuracy (R > 0.99; MAPE < 1%), outperforming conventional models. The trained ANN was integrated with a Genetic Algorithm (GA) to identify optimal parameters of 85.3 A, 7.7 m/min, and 3.8 mm/s, corresponding to predicted values of 242.5 MPa and 108.4 HV. Experimental validation showed deviations below 1%, confirming model reliability. The proposed ANN-GA framework effectively captures nonlinear process-structure-property relationships and provides a robust approach for optimizing WAAM processes and improving aluminum component performance.
To achieve precise finite element simulation of the vibration-assisted cold upsetting forming process of titanium alloys, this study focuses on Ti-45Nb titanium alloy as the research object. A constitutive model for vibration-assisted cold upsetting forming is established, incorporating both viscoelastic and viscoplastic deformation. The model is transformed into a programmable incremental form, and the control equations for elastic-viscoplastic deformation are derived. Secondary development is conducted using the VUMAT interface of ABAQUS, and the model is applied in simulation. Multi-condition simulations of Ti-45Nb titanium alloy cold upsetting forming are performed, and the results are compared with experimental data. The average relative error is found to be within 5%, verifying the accuracy of the finite element numerical simulation based on the secondary development. The developed constitutive model is used to simulate the cold upsetting forming process of Ti-45Nb titanium alloy internal wire joint components. The significant effects of vibration assistance in reducing maximum stress, optimizing stress distribution, and improving material flow are intuitively observed. This study provides technical support for the application of vibration-assisted cold upsetting forming technology in the forming of difficult-to-deform materials.
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 subsequently 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.
