Search Results (242)

This study systematically investigates the influence of 3D printing parameters on the surface morphology and coating performance of polylactic acid (PLA) substrates finished with traditional Chinese lacquer. PLA specimens were fabricated using fused deposition modeling (FDM) with varying print speeds, layer heights, and infill densities, followed by natural lacquer coating and controlled curing. Surface roughness, gloss, adhesion, and wear resistance were evaluated through standardized tests, while microstructural analysis using SEM revealed the interfacial morphology and film uniformity. Results indicate that layer height is the most dominant factor, exerting significant effects on all surface and coating properties. Increasing layer height led to higher surface roughness, which in turn reduced gloss due to enhanced diffuse scattering but improved adhesion and wear resistance through stronger mechanical interlocking. Print speed showed a secondary influence on adhesion, attributed to its effect on interlayer bonding and surface porosity, while infill density exhibited minimal influence except on wear resistance. The application of Chinese lacquer significantly reduced surface irregularities owing to its excellent self-leveling and gap-filling capabilities, producing smooth, durable, and well-adhered coatings. Overall, the study demonstrates that integrating traditional lacquer with modern FDM technology provides a sustainable and high-performance finishing solution for 3D-printed PLA, bridging cultural craftsmanship with advanced additive manufacturing for potential applications in decorative, protective, and eco-friendly products. Full article

A modified Johnson–Mehl–Avrami–Kolmogorov (JMAK) model, including the heating rates, was proposed in this study to improve the accuracy of isothermal austenite formation kinetics prediction. Since the ultrafast heating process affects the behavior of ferrite recrystallization and austenite formation before the isothermal process, which in turn influences the subsequent isothermal austenite formation kinetics, the effects of varying austenitization temperatures and heating rates on isothermal austenite formation in cold-rolled quenching and partitioning (Q&P) steel, which remain insufficiently understood, were systematically investigated. Under a constant heating rate, the austenite formation rate initially increases and subsequently decreases as the austenitization temperature rises from formation start temperature A_c1 to finish temperature A_c3, and complete austenitization is achieved more quickly at elevated temperatures. At a given austenitization temperature, an increased heating rate was found to accelerate the isothermal transformation kinetics and significantly reduce the duration required to achieve complete austenitization. The experimental results revealed that both the transformation activation energy ($Q$) and material constant ($k_0$) decreased with increasing heating rates, while the Avrami exponent ($n$) showed a progressive increase, leading to the development of the heating-rate-dependent modified JMAK model. The model accurately characterizes the effect of varying heating rates on isothermal austenite formation kinetics, enabling kinetic curves prediction under multiple heating rates and austenitization temperatures and overcoming the limitation of single heating rate prediction in existing models, with significantly broadened applicability. Full article

Inconel 625 is a nickel-based superalloy widely applied in aerospace and energy sectors due to its high strength and corrosion resistance. However, its poor machinability remains a significant challenge in precision manufacturing. This study investigates the influence of tool geometry and cutting parameters on surface roughness of Inconel 625 during turning operations under the minimum quantity lubrication (MQL) conditions. Experiments were carried out using three types of cutting inserts with distinct chip breaker geometries while systematically varying the cutting speed, feed rate, and depth of cut. The results were statistically analyzed using analysis of variance (ANOVA) to determine the significance of individual factors. The findings reveal that both the type of cutting insert and the process parameters have a considerable effect on surface roughness, which is the key output examined in this study. Cutting forces and chip type were examined to provide complementary insights and improve understanding of the observed relationships. Based on the results, an optimal set of cutting data was proposed to achieve a required surface roughness during the turning of Inconel 625 with MQL. Furthermore, a practical algorithm was developed to support the selection of cutting parameters in industrial applications. Analysis of the results showed that a cutting insert with a 0.4 mm corner radius achieved the required surface finish (Rz ≤ 0.4 µm). Furthermore, the analysis revealed a significant effect of the thermal properties of Inconel 625 on machining results and chip geometry. Full article

In swine production, it is broadly recognized that ventilation rates and indoor environmental conditions influence pig productivity. However, sparse scientific data are available on the combined effects and potential interactions of these factors in commercial production systems. This study investigated indoor environmental and management factors influencing wean-to-finish pig mortality in a commercial system. Temperature, relative humidity (RH), and carbon dioxide (CO₂) were recorded every 10 min in the front and back of 16 barns across five grow-finish sites in eastern North Carolina for two turns (four barns) or three turns (12 barns) for a total of 44 pig groups. Proportional weekly mortality was modeled using a generalized linear mixed model. Results showed that pigs in environments warmer than the desired room temperature had lower mortality (p < 0.001), suggesting cold stress was more detrimental than heat stress. Elevated RH and CO₂ at the back of the barn were linked to increased mortality (p < 0.001), highlighting air exchange rates as a key indicator. Mortality was greatest in pig groups placed during Spring and lowest in Summer (p < 0.05), and mortality declined as pigs aged (p = 0.0134). Surprisingly, greater barn occupancy correlated with lower mortality (p = 0.0012), potentially related to piglet quality at placement. The predictive power of the model varied with the turn of pigs, with R² averaging 0.24 (ranging from 0.001 to 0.61) and an average RMSE of 0.36% (ranging from 0.17% to 0.77%). Ammonia (NH₃) was recorded at the back of six barns, and concentrations were modeled. Greater NH₃ concentrations were associated with increased pig age, RH, and CO₂, as well as lower deviation from desired room temperature and lower barn occupancy. Collectively, these findings highlight the importance of proper ventilation and management on swine productivity. Full article

Even though the foundations of diamond burnishing as a research topic were laid long ago, numerous scientific papers still deal with examining various aspects of the burnishing process today. One such aspect is the investigation of the 3D roughness parameters related to the tribological characteristics of the machined surface, which is detailed in the present study. In this study, the positive properties of slide friction diamond burnishing are presented through the examination of surface quality, which plays a key role in tribological assessment. This study analyzed the surface layer condition of X5CrNi18-10 stainless austenitic chromium–nickel steel test pieces after burnishing. Among the finishing operations, burnishing is an economical and low-environmental impact process. The study includes a description of the technological characteristics of turning and diamond burnishing processes. The main characteristics of the Abbott–Firestone curve are described, and parameter improvement factors are introduced. The experimental results and their evaluations are presented by analyzing the values of the Abbott–Firestone surface curves. The study concludes that the best improvement ratios of S_a (arithmetical mean height), S_q (root mean square height), S_z (maximum height) IS_a, IS_q, and IS_z roughness improvements were achieved when using the parameter combination v₂ = 55.578 m/min, f₂ = 0.050 mm/rev and F₄ = 50 N. Full article

The high hardness and toughness of AISI 4130 alloy present significant challenges during machining, including excessive cutting forces, rapid tool wear, and poor surface finish control. To address these issues, this study combines numerical simulation with turning experiments to systematically investigate the effects of tool geometry and cutting parameters on cutting force, temperature, and surface roughness. Through Deform-3D finite element modeling, one-factor, and orthogonal simulation tests, it was found that the optimal tool geometric combination (λs = 2°, κ_r = 99°, γ₀ = 5°) reduces the cutting forces by 21.86% as compared to the baseline parameters. Experimental validation showed that the agreement between simulated and measured cutting forces was 86.73%–87.8%, with simulated values being 10%–13.27% higher due to idealized boundary conditions. Surface morphological analysis by Bruker Contour Elite K shows that the surface roughness of the workpiece decreases with an increasing cutting speed and increases with an increasing feed rate and depth of cut. The above studies provide a certain research basis for optimizing the tool angle and improving the cutting efficiency. Full article

Tool structure design methodologies predominantly rely on trial-and-error approaches or single-objective optimization but fail to achieve coordinated enhancement of multiple performance metrics while lacking thorough investigation into complex cutting coupling mechanisms. This study proposes a multi-objective optimization framework integrating joint simulation approaches. First, a finite element model for orthogonal turning was developed, incorporating the hyperbolic tangent (TANH) constitutive model and variable coefficient friction model. The cutting performance of four micro-groove configurations is comparatively analyzed. Subsequently, parametric modeling coupled with simulation–data interaction enables multi-objective optimization targeting minimized cutting force, reduced cutting temperature, and decreased wear rate. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) explores Pareto-optimized solutions for arc micro-groove geometric parameters. Finally, optimized tools manufactured via powder metallurgy undergo experimental validation. The results demonstrate that the optimized tool achieves significant improvements: a 19.3% reduction in cutting force, a 14.2% decrease in cutting temperature, and tool life extended by 33.3% compared to baseline tools. Enhanced chip control is evidenced by an 11.4% reduction in chip curl radius, accompanied by diminished oxidation/adhesive wear and superior surface finish. This multi-objective optimization methodology effectively overcomes the constraints of conventional single-parameter optimization, substantially improving comprehensive tool performance while establishing a reference paradigm for cutting tool design under complex operational conditions. Full article

The aim of this study is to evaluate the effect of various lubrication systems (dry cutting, MQL, and nano-MQL) on the machinability of AISI 1040 medium-carbon steel. By dispersing titanium carbide (TiC) nanoparticles into environmentally friendly sunflower oil, a new type of nano-MQL fluid was developed. Machinability parameters such as surface finish, cutting force, energy consumption, chip structure, and tool degradation were examined through scanning electron microscopy (SEM). Based on experimental observations, the use of the nano-MQL technique led to a notable enhancement in machining performance when compared to both dry and traditional MQL machining. In addition, surface roughness was substantially reduced with the nano-MQL, suggesting more effective lubrication and cooling. Reductions in cutting forces and energy consumption were also observed, indicating more efficient material removal and lower mechanical resistance. The SEM examination of the cutting tools proved the low wear rate of the nano-MQL, which exhibited less adhesion and more abrasion wear, and of dry cutting, which showed the most serious wear. Furthermore, chip morphology illustrations indicated that the chips of nano-MQL were relatively uniform and segmented, indicating superior chip breaking quality and cutting stability. The results suggest that employing TiC nanoparticles in MQL offers a clear enhancement of cutting performance in terms of process efficiency, surface quality, and tool wear. These results validate the capability of nano-MQL as an environmentally friendly and high-performance lubrication method for turning medium-carbon steels, supporting more sustainable and efficient manufacturing operations. Full article

AlSi10Mg has been one of the most studied and employed aluminum alloys for additive manufacturing via laser powder-bed fusion (L-PBF). The optimization of manufacturing parameters is important for reducing internal defects, including porosity and inadequate surface finishes. In addition, heat treatments, such as T6, are often applied to this alloy, but they degrade the characteristic microstructure obtained via L-PBF additive manufacturing—the fine cellular structures—which may, in turn, detrimentally affect the material’s properties. In this context, a new alternative to this treatment, direct aging (DA), has shown promise in improving the mechanical properties of AlSi10Mg parts produced via L-PBF, since it preserves the cellular microstructure, precipitating silicon-rich nanoparticles within the cells. Understanding how different temperatures and heat treatment times influence the microstructure and, consequently, the properties remains a field to be explored in order to optimize the treatment conditions and achieve better mechanical properties. Thus, the objective of this study was to evaluate the influence of manufacturing parameters and heat treatments on the microstructure and mechanical properties of AlSi10Mg alloy. The optimized manufacturing conditions were 300 W power, 800 mm/s scan speed, 30 µm layer thickness, and an argon atmosphere, which led to lower porosity and better finishing. Samples were heat-treated via DA at 150 °C and 170 °C for different times, as well as undergoing a T6 treatment (solution at 520 °C followed by aging at 150 °C and 170 °C). Initially, the aging curves show higher hardness values for the direct aging condition, compared to the T6 and as-built conditions, reaching a peak hardness of 195 HV for 6h of direct aging. In this way, it was followed with microstructural characterization, which demonstrated that DA maintained the fine cell microstructure of L-PBF and promoted the precipitation of Si nanoparticles, which certainly contributed to the increase in hardness compared to T6, which promoted a structure with coarser precipitates. DA at 170 °C for 6 h increased the tensile strength to 430 MPa, compared to the as-built condition, with a slight loss of ductility. Full article

Interest in surface integrity has grown in the manufacturing industry; indeed, it has become an integral part of the industry. It can be studied by examining surface roughness parameters, hardness variations, and microstructure. However, evaluating all these parameters together can be a challenging task. To address this multi-criteria decision-making model (MCDM), techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis (GRA) provide a suitable solution for optimizing the machining parameters that lead to improved product quality. This work investigated surface roughness parameters, including arithmetic average surface roughness (2D) (Ra), mean surface roughness depth (2D) (Rz), area arithmetic mean height (3D) (Sa), and maximum surface height (3D) (Sz), in conjunction with Vickers macrohardness (HV) and optical micrographs, to analyze machined surfaces during the turning of X5CrNi18-10 steel. The results suggest that machining with a spindle speed (N) of 2000 rpm or v_c of 282.7 m/min, a feed rate (f) of 0.1 mm/rev, and a depth of cut of 0.5 mm yields the best surface, achieving an “A” class surface finish. These parameters can be applied in manufacturing industries that utilize chromium–nickel alloys. Additionally, the method used can be applied to rank the quality of the product. Full article

Machining chromium–nickel alloy steel is challenging due to its material properties, such as high strength and toughness. These properties often lead to tool damage and degradation of tool life, which overall impacts the production time, cost, and quality of the product. Therefore, it is essential to investigate early signs of tool damage to determine the effective machining conditions for chromium–nickel alloy steel, thereby increasing tool life and improving product quality. In this study, the early signs of tool wear were observed in a physical vapor deposition (PVD) carbide-coated tool (Seco Tools, Björnbacksvägen, Sweden) during the machining of X5CrNi18-10 steel under both dry and wet conditions. A finish turning operation was performed on the outer diameter (OD) of the workpiece with a 0.4 mm nose radius tool. At the early stage, the tool was examined from the functional side (f–side) and the passive side (p–side). The results indicate that dry machining leads to increased coating removal, more heat generation, and visible damage, such as pits and surface scratches. By comparison, wet machining helps reduce heat and wear, thereby improving tool life and machining quality. These findings suggest that a coolant must be used when machining chromium–nickel alloy steel with a PVD carbide-coated tool. Full article

Fused Filament Fabrication (FFF) enables the manufacturing of complex polymer components. However, surface finish and dimensional accuracy remain key limitations for their integration into functional assemblies. This study explores the potential of conventional turning as a post-processing strategy to improve the geometric and surface quality of PLA reinforced with carbon fiber (CF) parts produced by FFF. Machinability was evaluated through the analysis of cutting forces, thermal behavior, energy consumption, and surface integrity under varying cutting speeds, feed rates, and specimen slenderness. The results indicate that feed is the most influential parameter across all performance metrics, with lower values leading to improved dimensional accuracy and surface finish, achieving the most significant reductions of 63% in surface roughness (Sa) and 62% in cylindricity deviation. Nevertheless, the surface roughness is higher than that of metals, and deviations in geometry along the length of the specimen have been observed. A critical shear stress of 0.237 MPa has been identified as the limit for interlayer failure, defining the boundary conditions for viable cutting operation. The incorporation of CNC turning as a post-processing step reduced the total fabrication time by approximately 83% compared with high-resolution FFF, while maintaining dimensional accuracy and enhancing surface quality. These findings support the use of machining operations as a viable and efficient post-processing method for improving the functionality of polymer-based components produced by additive manufacturing. Full article

This study investigates the effect of ultrasonic vibration applied in the cutting speed direction on surface quality during face turning of C45 steel. The experiments were performed using an ultrasonic generator operating at a frequency of 20 kHz with an amplitude of approximately 10 µm. The cutting parameters used in the experiments included spindle speeds of 700, 1100, and 1300 rpm, feed rates of 0.1 and 0.15 mm/rev, while the depth of cut was fixed at 0.2 mm. Surface quality was evaluated based on the roughness parameters R_a and R_z, as well as surface topography was observed using a Keyence VHX-7000 digital microscope. The results show that ultrasonic-assisted face turning (UAFT) significantly improves surface finish, particularly in the central region of the workpiece where the cutting speed is lower and built-up edge (BUE) formation is more likely. The lowest R_a value recorded was 0.91 µm, representing a 71% reduction compared to conventional turning (CT). Furthermore, at the highest spindle speed (1300 rpm), the standard deviations of both R_a and R_z were minimal, indicating improved surface consistency due to the suppression of BUE by ultrasonic vibration. Topographical observations further confirmed that UAFT generated regular and periodic surface patterns, in contrast to the irregular textures observed in CT. Full article

At the stage of the final processing of surfaces, the quality indicators of the surfaces of parts—size, shape in cross-section and longitudinal section, mutual arrangement of surfaces, and roughness—are obtained. This includes technical and organizational measures and activities that are laid down or taken into account in the applied technology. This publication constructs cause-and-effect diagrams of the factors influencing the achievement of each of these accuracy indicators. Ways to reduce the negative impact of some factors are indicated. Errors related to the components of the technological system are analyzed and grouped. Tasks related to accurate process design are defined. Guidelines related to structural accuracy design are given. The technological conditions for ensuring the accuracy of finishing operations when processing parts by turning are formulated. Full article

One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modeling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L₂₇ orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learned well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14%, a coefficient of determination (R²) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalization on the testing data, with 94.36% accuracy and 0.9391 R² and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS. Full article