Optimization Of Surface Roughness Research Articles

Modeling and Prediction of Surface Roughness in Hybrid Manufacturing–Milling after FDM Using Artificial Neural Networks

Three-dimensional printing, or additive manufacturing, represents one of the fastest growing branches of the industry, and fused deposition modeling (FDM) is one of most frequently used technologies. Three-dimensional printing does not provide high-quality surfaces, so finishing is required, and milling is one of the best methods for improving surface quality. The combination of 3D printing and traditional manufacturing technologies is known as hybrid manufacturing. In order to improve quality and determine optimal machining parameters, researchers increasingly use artificial intelligence methods. In the context of manufacturing technologies, both multiple regression analysis (MRA) and artificial neural networks (ANNs) have proven to be highly reliable in predicting and optimizing machining processes. This study focuses on the use of MRA and an ANN to analyze the influence of machining parameters such as feed rate, depth of cut, and spindle speed on the surface roughness of a 3D-printed part in a milling process. The study compares the measured results with the outcomes obtained through MRA and the ANN to assess their effectiveness in predicting and optimizing surface roughness. The results show that higher accuracy was obtained from the ANN method.

Optimization of surface roughness using rotary ultrasonic machine of polyvinyl butyral glass with Taguchi and grey relational analysis

Optimization of surface roughness using rotary ultrasonic machine of polyvinyl butyral glass with Taguchi and grey relational analysis

The Impact of nanoparticles (B4C-Al2O3) on mechanical, wear, fracture behavior and machining properties of formwork grade Al7075 composites

This study explores how ageing temperature and the volume percentage of Al2O3+B4C nanoparticles influence the machinability and hardness of stir-cast Al-7075 Metal Matrix Composite (MMC). Using liquid metallurgy techniques, hybrid materials were created by reinforcing Al7075 metal matrix with varying weight percentages of nanosized B4C (1.5%, 3%, and 4.5%) and Al2O3 (1%, 1.5%, and 2%). After fabrication, the samples were subjected to five-hour ageing process at temperatures of 100, 120, and 140 degrees Celsius, followed by cooling to ambient temperature (27 degrees Celsius). Hybrid nano composites that had been heat treated were tested for wear, tensile strength, and hardness. Results shows that, the addition of nanoparticles and heat treatment considerably improves the tensile strength, hardness, and wear resistance of hybrid composites by 3%, 17%, and 10%, respectively, for samples reinforced with 4.5% B4C + 2% Al2O3. SEM analysis was used to investigate the type of wear and the tensile fracture mode of nano composite samples by analyzing the wornout surface and the surface where tensile fracture occurred. Machinability was assessed using L27 orthogonal array tests, focusing on three key process parameters: feed rate (0.1 mm/min), depth of cut (0.2 mm/min), and spindle speed (1000 rpm). Outcomes show that, increasing the wt. % of nano-Al2O3/B4C leads to higher machining force and surface roughness (Ra) of MMCs. Conversely, higher ageing temperatures result in decreased machining force and surface roughness. Optimal surface roughness and machining force were achieved with 1% Al2O3 + 1.5% B4C and an ageing temperature of 140°C. These findings offer valuable insights into the ease of machining of composite metal alloys, emphasizing the importance of parameter selection and optimization for desired machining outcomes.

Photo-Healable Fabrics: Achieving Structural Control via Photochemical Solid-Liquid Transitions of Polystyrene/Azobenzene-Containing Polymer Blends.

While polymer fabrics are integral to a wide range of applications, their vulnerability to mechanical damage limits their sustainability and practicality. Addressing this challenge, our study introduces a versatile strategy to develop photohealable fabrics, utilizing a composite of polystyrene (PS) and an azobenzene-containing polymer (PAzo). This combination leverages the structural stability of PS to compensate for the mechanical weaknesses of PAzo, forming the fiber structures. Key to our approach is the reversible trans-cis photoisomerization of azobenzene groups within the PAzo under UV light exposure, enabling controlled morphological alterations in the PS/PAzo blend fibers. The transition of PAzo sections from a solid to a liquid state at a low glass transition temperature (Tg ∼ 13.7 °C) is followed by solidification under visible light, thus stabilizing the altered fiber structures. In this study, we explore various PS/PAzo blend ratios to optimize surface roughness and mechanical properties. Additionally, we demonstrate the capability of these fibers for photoinduced self-healing. When damaged fabrics are clamped and subjected to UV irradiation for 20 min and pressed for 24 h, the mobility of the cis-form PAzo sections facilitates healing while retaining the overall fabric structure. This innovative approach not only addresses the critical issue of durability in polymer fabrics but also offers a sustainable and practical solution, paving the way for its application in smart clothing and advanced fabric-based materials.

A review on optimum substrate surface roughness to create better wetting

Surface roughness or texture is one of the key parameters for liquid fluids or liquid metals to achieve good or poor wetting. In certain applications surface roughness of a smooth surface [mirror finish] is treated as a slippery surface for poor wettability however in a few applications’ roughness surfaces are considered as better wetting, especially for liquid metals. In this regard, to understand the parameters for the optimum surface roughness of the substrates to achieve good wetting is essential. In the present review paper, an attempt has been made to find the ideal roughness value of the substrate for better wetting of liquid metals or alloys based on substrates and fluids.

Fused filament fabrication using stainless steel 316L‐polymer blend: Analysis and optimization for green density and surface roughness

AbstractThis study is centered on the analysis of composite component additive manufacturing, via fused filament fabrication (FFF), comprising of 316L stainless steel particles in a polymer matrix. It examines the impact of several process factors of FFF, including extrusion speed, layer height, and extrusion temperature, on the density and surface roughness with the goal to minimize porosity. The independent and interaction impacts of these parameters were investigated using a central composite design technique. The technique of analysis of variance was utilized to determine the influence of relevant factors. Regression analysis was used to establish statistical models linking the parameters to green density and surface roughness. The green density was enhanced by reducing the layer height from 0.2 to 0.1 μm and decreasing the nozzle speed from 100 to 20 mm min−1. The surface roughness was improved by using a slow printing speed and minimum layer height. The ideal temperature range for producing favorable results in terms of green density and surface roughness during extrusion was found to be between 235 and 240°C. Significant correlations were found between the parameters and the green density and surface roughness. A genetic algorithm was used as an optimisation tool to determine the thresholds that would provide the highest green density and the lowest surface roughness values. The porosity of the samples generated with the optimized settings was evaluated using microtomography scans. The methodology presented here can be applied to composite and standard polymeric filament printing to determine optimal printing parameters.Highlights Blend of stainless steel 316L and polymer was used for fused filament fabrication. Printing speed and layer height were the dominating for density and roughness. Height 0.2 mm, speed 20 mm s−1, and temperature 239°C resulted 4.73 g cm−3 density and 21.09 μm roughness Microtomography revealed porosity of 1.7% in the optimized sample.

Formation mechanism of surface gradient microstructure and mechanical properties evolution of Mg–Y-Nd-Gd-Zr alloy by ultrasonic rolling

The evolution mechanism of the microstructure and mechanical properties of a Mg–Y-Nd-Gd-Zr alloy subjected to different numbers of passes of the ultrasonic surface rolling process (USRP) was investigated. The results demonstrated an initial decrease followed by an increase in the surface roughness of the alloy. The optimal surface roughness was achieved after four passes, with a contour mean deviation of 0.06 μm and micro-roughness 10-point height of 0.55 μm. After the USRP, the alloy exhibited a composite gradient structure characterized by increased grain size and decreased dislocation, lamination, and twin densities. The depth of the gradient layer increased with each additional USRP pass, ranging from 650 μm (1 pass) to 910 μm (8 passes). Furthermore, the USRP treatment effectively enhanced the alloy strength; compared with the untreated samples; the yield strength increased by 34.2% in the gradient sample after one pass. No significant change was observed in the YS with an increasing number of rolling passes for Mg alloys, whereas both the tensile strength and elongation showed improvements. High-density twinning played a crucial role in enhancing the YS of the gradient samples without significant variation across different numbers of rolling passes, thus following a consistent pattern. The formation of more nanocrystals on the surface of Mg alloys after multiple rolling cycles is key to preventing premature fracture failure and improving the work-hardening rate.

Experimental Analysis of Surface Roughness Optimization of EN19 Alloy Steel Milling by the Cuckoo Search Algorithm

In the present paper, end milling has been performed on EN19 alloy steel by selecting cutting speed, feed rate, & depth of cut as input parameters and surface roughness (SR) as a response. EN19 alloy steel milling is widely used in various sectors, such as the automotive, defence, construction, aerospace, and nuclear sectors. A parametric study of EN19 alloy steel is needed for better machining. The central composite design was used for designing the experiments & modeling the surface roughness as a response. A cuckoo search algorithm was applied to minimize the surface roughness. It was found that feed rate is the most important factor affecting surface roughness (SR). The Cuckoo Search Algorithm also reveals that a minimum SR 1.8576 micrometer has been achieved at a higher speed of 765 RPM, a lower feed rate of 55.9516 mm/min., & a lower depth of cut of 0.4846 mm. The experiment concludes that it is so that the optimum SR is exhibited at both lower feed rates & high speeds. This, in turn, indicates that our implementation of CCD-based SR, followed by the real cuckoo search algorithm optimization, provides similar results and a good model to the practical results we would expect.

Optimization of Surface Roughness and Cutting Temperature in Turning of 1.4534 Stainless Steel under Sustainable Conditions

1.4534 stainless steel, which is produced especially for aerospace applications, is frequently preferred in aircraft landing sets under high load and in highly corrosive environments. In addition to its superior properties, its machinability rate is low compared to other stainless steels. Moreover, improving 1.4534 stainless steel's machining performance is crucial since its formability problems. In this study, 1.4534 stainless steel was tested in a series of experiments under sustainable conditions (hBN, CO2, and hBN+CO2). Taguchi techniques were used in the experimental design to save cost and time. Three cooling levels (hBN, CO2, and hBN+CO2), three cutting speeds (140, 200, and 260 m/min), three feed rates (0.12, 0.16, and 0.20 mm/rev), and a constant cutting speed (0.8 mm) were used in the current study. Analysis of variance (ANOVA) was performed in the current study to determine the extent of the components' effects since cutting temperature and surface roughness were chosen as the performance standard. According to the test results obtained; hBN+CO2 condition showed the best performance for surface roughness and cutting temperature.

Optimization of Process Parameters for Turning Operation on D3 Die Steel

This research aims to determine the optimal Surface Roughness for machining D3 die steel alloy with uncoated carbide inserts. It will do this by studying the most efficient turning parameters, such as cutting speed, feed, and depth of cut. Models have been generated using a variety of statistical modeling approaches, including Genetic Algorithm with Response Surface Methodology. This research aimed to use the regression technique to develop a model that could predict surface roughness. It has also been investigated if the Taguchi Technique may be used to optimize process parameters. To decide the primary boundaries affecting Surface Unpleasantness, we used Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA) tests. This paper aims to contribute valuable insights into achieving the best Surface Roughness outcomes in the machining process for D3 die steel alloy with Uncoated Carbide Inserts. The utilization of Genetic Algorithm and Response Surface Methodology showcases a robust approach for modelling intricate parameter interactions. If you know the values of the parameters, you may use the Regression Technique to forecast the surface roughness. Process parameter optimization may be made more systematic with the use of the Taguchi Technique.

Optimizing surface roughness of workpieces made of stainless steel 316L with DMLS technology: a Taguchi method approach

This study employs Direct Metal Laser Sintering (DMLS) technology to investigate surface roughness in stainless steel 316L 3D printing processes. Utilizing the Taguchi method in experimental design, we examine the influence of independent variables—laser power, scan speed, and hatch spacing - on surface roughness quality. Results indicate that laser power has the greatest impact, followed by scan speed and hatch spacing. Notably, both laser power and hatch spacing positively affect surface roughness, while scan speed adversely affects the top surface quality of printed components. This research enhances comprehension of the intricate relationship between process parameters and surface quality in DMLS-based 3D printing, offering insights for optimizing surface roughness in stainless steel 316L applications. The study holds practical significance for enhancing the quality and performance of 3D-printed components across diverse engineering and manufacturing sectors.

Optimization of surface roughness and tool wear in laser-assisted machining of alumina ceramics using Taguchi method

Optimization of surface roughness and tool wear in laser-assisted machining of alumina ceramics using Taguchi method

Prediction and Multiparametric Optimization of the Machined Surface Quality of Tool Steels in Precise Wire Electrical Discharge Machining

This article describes a proposed procedure for multiparametric optimization of the quality of machined surfaces, including mathematical models that can predict the high quality of a precisely machined surface and, at the same time, the high productivity of the process in WEDM of tool steels. The experimental research was carried out using the full DoE factorial design method, which has four technological parameters. The measured output qualitative parameter Surface Roughness (SR) and the output quantitative parameter Material Removal Rate (MRR) were evaluated using the Grey Relational Analysis (GRA) and Analysis of Variance (ANOVA) methods. Multiple Regression Models (MRM) were developed to represent the multiple responses of the investigated tool steels using a regression tool set. The results of the multiparametric optimization revealed a correlation between the input variable parameters of the electrical discharge process, while the favorable results of the observed output-dependent parameters SR and MRR were coupled to the parameters of low peak current I, low value of pulse on-time duration ton, low voltage of discharge U, and high value of pulse off-time duration toff. Based on the multiparametric optimization, key results were obtained that demonstrated the mutual dependence of the observed output process parameters. An optimum SR value of 1.50 μm was obtained with L8-level settings for the input variable parameters I, ton, U, and toff (2 A, 32 μs, 90 V, and 20 μs, respectively) and an MRR value of 12.50 mm3·min−1 was achieved.

Intelligent prediction using AI-based modeling and optimization of surface roughness in Al7049 end milling with coconut oil under minimum quantity lubrication

Intelligent prediction using AI-based modeling and optimization of surface roughness in Al7049 end milling with coconut oil under minimum quantity lubrication

Surface Roughness Analysis and Optimization for ST 37 Steel in Lathe Operation using Design of Experiments Method

The machining process is one of the processes used to process steel materials. The value or level of material roughness is one of the values that is considered in the machining process, according to the desired roughness value. One example is in the turning process, spindle speed, depth of feed, and feed motion are factors that can affect the level of roughness of a material. The level of roughness that is not according to demand is one of the problems in the manufacturing industry, especially in the results of the turning process. One of the causes of the discrepancy in the roughness value is because the parameters used are not suitable. Therefore, this study aims to determine the optimal parameters in the turning process to obtain the optimal roughness value, and to determine the parameters that most influence the roughness of the turning process. In this study, ST 37 steel was used. This study used an experimental design method with Orthogonal Array L8. The experiment was carried out with 5 control factors and 3 noise factors. Each factor has 2 levels and is repeated 8 times. In this study using a conventional lathe and for roughness testing using a Surface Roughness Tester. Based on the results of data analysis, the movement factor has the highest influence on the surface roughness value. The ranking factors that affect the roughness value are as follows: spindle speed of 345 rpm, feed motion of 0.051 mm/rad, depth of cut 1 mm, dry cooling, and automatic sledding. The Design of Experiments (Taguchi) method use quality characteristics to determine the results of the analysis. In this study, the quality characteristic is smaller the better. Smaller the better characteristics is where the smaller value, then better the quality. In variations of the spindle rotation speed and feed motion, we get where if the spindle speed is high and the feed motion is low it will produce a low level of roughness, whereas if the spindle speed is low and the feed movement is high it will produce a high level of roughness.

Optimization of cutting force and surface roughness in end milling of AA-TiCp composite

Nowadays Aluminium alloy (AA) based composites are used in many fields such as automotive, aerospace, electronics, and marine industries. But for the utilization of these composites for different applications with different shapes, machining especially end milling is the essential process. Hence, this work describes the optimization of surface roughness and cutting forces during end milling of A356 aluminium alloy reinforced with titanium carbide particle (TiCp) metal matrix composites (MMCs). A356 alloy with 0, 3 and 6 vol% of TiCp (average size of 50 µm) composites were prepared using a stir casting process. The effects of milling process factors, namely cutting speed, feed, depth of cut, and volume % of TiCp, on average surface roughness (Ra) and cutting forces have been studied using a Box-Behnken design of the experiment technique. The end milling experiments were performed in a CNC vertical machining center without any cutting fluid using uncoated cemented carbide inserts. Using Design Ease software, the predictive model analysis and change in performance measurements for process variables were provided. The results showed that the depth of cut, followed by cutting speed, is the most important criterion and finally optimal levels of process variables were also identified to improve the machinability of the composites.

Optimizing SKD-61 steel machining for marine component manufacture: A grey-Taguchi study on nanofluid minimum quantity lubrication

SKD-61 steel finds extensive application in various sectors, including the marine industry where materials must withstand harsh aquatic conditions. In this study, we employ the Grey-Taguchi approach to optimize surface roughness (Ra), cutting force (Fc), and material removal rate (MRR) when hard-milling SKD-61 steel in pure minimum quantity lubrication (MQL) and nanofluid MQL environments, aligning with the demands of marine engineering. The orthogonal array method generates a set of experiments based on four input parameters: cutting speed, depth of cut, feed per tooth, and cutting condition, which are essential factors in marine component manufacturing. Our approach combines Grey relational analysis (GRA) with the Taguchi method to enhance multi-objective outcomes in machining. The results of this marine-focused study reveal that the lowest surface roughness, cutting force, and highest material removal rates are attained when the cutting conditions align with the principles of nanofluid MQL at a cutting velocity of 80 m/min, a depth of cut of 0.2 mm, and a feed per tooth of 0.01 mm/tooth. Keywords: hard milling, nanofluid MQL, optimization machining parameters; Grey relational analysis; Taguchi

Surface Roughness Evaluation of Pre- Versus Post-Crystallization Polish of Two High-Strength Silicate Ceramics for Chairside CAD/CAM Technology

This in vitro study evaluated the surface roughness (Sa) of two high-strength silicate ceramics, lithium disilicate IPS e.max CAD, Ivoclar Vivadent (LDS group), and zirconia-reinforced lithium silicate Vita Suprinity, VITA Zahnfabrik (ZLS group). The surface roughness was investigated before and after milling using different polishing systems and timings relative to the final crystallization of the ceramics. Forty-eight samples per group were polished by a single calibrated operator using two polishing systems: Dialite LD (Brasseler) and Lithium Silicate Polishers (Meisinger) for the LDS group and Dialite LD (Brasseler) and Vita Suprinity Polishing Set Technical (VITA Zahnfabrik) for the ZLS group, both pre- and post-crystallization. Surface roughness was measured using a confocal laser microscope (OLS4000 LEXT/Olympus), with scanning electron microscopy (SEM) used to evaluate surface morphological changes. Significant differences in Sa values were found between baseline groups, with ZLS exhibiting lower values. All polishing methods significantly reduced surface roughness compared to baseline (p ≤ 0.05). No significant differences were found in LDS samples when polishing pre- or post-crystallization (p = 0.129), while for ZLS samples, post-crystallization polishing achieved significantly smoother surfaces (p < 0.001). The study concluded that the choice of polishing system and timing did not significantly affect surface roughness for LDS. However, it is recommended that post-crystallization polishing be performed for the optimal smoothness of ZLS. This study aimed to evaluate the post-milling polishing procedures of CAD/CAM high-strength restorations, emphasizing the importance of an optimal surface roughness to prevent issues such as increased risk of abrasion on opposing teeth, enhanced plaque adhesion, and mechanical failures. Investigating these polishing techniques enables clinicians to optimize clinical performance, thereby improving the quality and longevity of high-strength silicate ceramics.

Surface roughness optimization of hybrid PBF-LB/M-built Inconel 718 using in situ high-speed milling

We report on the optimization of the surface roughness of hybrid additive manufactured Ni superalloys, combining a conventional laser powder bed fusion process with in situ high-speed milling. This remarkable hybrid approach has only recently been applied to different steel types and barely to Ni superalloys which opposite to steel appear to be challenging for milling processes, particularly within the powderbed of laser powder bed fusion. Different influencing factors on the surface roughness are varied in this study, following the Taguchi method. Their effect is evaluated with respect to the average surface roughness and the maximum surface roughness. The signal-to-noise ratio for the varied parameters infeed, z-pitch, feed rate, and spindle speed is calculated, determining their relevance on the surface roughness, and defining an optimal parameter combination. As the surface quality is optimized to Ra=0.47μm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{R_a=0.47\\, \\mu m}$$\\end{document}, the definition of the optimal parameter combination is of the highest relevance for the application of this novel manufacturing approach for Inconel. Using linear regression, the resulting surface roughness of these parameters is predicted, getting validated by the experimental evaluation. Due to a further analysis, including EDX analysis and a quantitative element analysis at different positions of the flank of the milling cutter, wear characteristics as well as the dissipation of the coating of the milling cutter are detected. The flank wear and the resulting breakage of the cutting edge are defined as the main reasons of a rising surface roughness.

Research on High‐Efficiency Curved Surface Polishing with Even Distribution of Slurry Based on Dielectrophoresis

To overcome the problems of low slurry utilization and poor surface uniformity in conventional small tool polishing of curved surfaces, a small tool polishing method based on slurry dielectrophoresis (STP‐DEP) is proposed. The dielectrophoretic effect distributes the slurry evenly and increases its dwell time in the processing area, enabling more abrasives to participate in polishing, thereby improving the material removal efficiency and surface uniformity of the workpiece. The observation experiment indicates that dielectrophoresis can efficiently distribute the slurry evenly. Compared with the traditional small tool polishing, the results show that the polishing efficiency of the STP‐DEP is improved by 34% on the premise of achieving the same surface roughness Ra of the workpiece. The Taguchi experiments are carried out on K9 convex lens. Results show that the optimal surface roughness Ra 3.3 nm (initial Ra 2400 nm, processing time 120 min) is obtained when the tool speed is 400 rpm, the pressing amount is 1 mm, the infeed angle is 15°, and the abrasive size is 3 μm. The research proves that the STP‐DEP successfully solves the problem of slurry uneven distribution, and realizes efficient surface polishing with high surface uniformity.