Surface roughness optimization in laser machining of stainless steel 304 using response surface methodology

Surface roughness is one of the most important requirements of the finished products in machining process. The determination of optimal cutting parameters is very important to minimize the surface roughness of a product. This article describes the development process of a surface roughness model in high-speed ball-end milling using response surface methodology based on design of experiment. Composite desirability function and teaching-learning-based optimization algorithm have been used for determining optimal cutting process parameters. The experiments have been planned and conducted using rotatable central composite design under dry condition. Mathematical model for surface roughness has been developed in terms of cutting speed, feed per tooth, axial depth of cut and radial depth of cut as the cutting process parameters. Analysis of variance has been performed for analysing the effect of cutting parameters on surface roughness. A second-order full quadratic model is used for mathematical modelling. The analysis of the results shows that the developed model is adequate enough and good to be accepted. Analysis of variance for the individual terms revealed that surface roughness is mostly affected by the cutting speed with a percentage contribution of 47.18% followed by axial depth of cut by 10.83%. The optimum values of cutting process parameters obtained through teaching-learning-based optimization are feed per tooth ( fz) = 0.06 mm, axial depth of cut ( Ap) = 0.74 mm, cutting speed ( Vc) = 145.8 m/min, and radial depth of cut ( Ae) = 0.38 mm. The optimum value of surface roughness at the optimum parametric setting is 1.11 µm and has been validated by confirmation experiments.

Parameter Optimization for Surface Roughness and Wall Thickness on AA5052 Aluminium alloy by Incremental Forming using Response Surface Methodology

Introduction: Among alloys of medium-carbon and high-strength steel, EN 24 steel is characterised by its nickel-chromium-molybdenum composition. EN 24 steel is highly suitable for application in heavy-duty projects due to its notable resilience to damage, especially when exposed to low temperatures. With the objective of minimising surface irregularities, this research endeavours to enhance the milling process of EN 24 steel by employing coated tungsten carbide (WC) tool inserts.Methods: Feed rate, cutting speed, depth of cut, and cutting fluid are all crucial process factors in the experimental investigation. Four distinct levels are applied to each factor. The research utilises the Design of Experiments (DOE)-based Central Composite Design of Response Surface Methodology. To predict output parameters, mathematical models are developed utilising analysis of variance (ANOVA) for optimisation purposes.Results and discussions: Through the utilisation of multi-objective optimisation, the optimal combination for tungsten carbide inserts was determined, which provided surface irregularities of 0.301 µm. Cutting speed (CS) of 149.507 m/min, feed rate (FR) of 340.27 mm/min, depth of cut (DOC) of 0.599 mm, and cutting fluid (CF) of 12.50 L/min are the optimal parameters. The surface morphologies of the machined workpiece at particular parameter values can be discerned through scanning electron microscope (SEM) analysis, yielding significant insights. The optimal parameters that have been identified provide practical recommendations for improving the milling method of EN 24 steel when tungsten carbide inserts are utilised. Understanding the milling process in its entirety is facilitated by SEM analysis of surface morphologies and microstructures under particular cutting conditions. The morphology and surface irregularities of the machined workpiece are evaluated using profilometry, which provides additional insight into surface integrity. The discourse investigates the potential applications and implications of the results, as well as suggests directions for further study concerning the enhancement of milling processes for similar steel alloys.

For cost reduction and quality improvement of machining products, optimum output machining parameters such as material removal rate, tool wear ratio and surface roughness is very essential. Moreover, these output parameters are strongly depends on the precision of the machine tool as well as the input machining parameters. In this paper, a hybrid model of Artificial Bee Colony (ABC), which is motivated by the intelligent behavior of honey bees with Response Surface Methodology (RSM), has been developed for optimizing the surface roughness of stainless steel during turning operation. The predicted optimal value of surface roughness of stainless steel is further confirmed by conducting supplementary experiments. Finally, the performance of this algorithm is evaluated in comparison with desirability analysis. The performance of ABC is at par with that of desirability analysis for different parametric conditions.

In the present investigation, a new hybrid technique response surface methodology (RSM) based simulated annealing (SA) algorithm approach is proposed to predict surface roughness for stainless steel material in traverse cut cylindrical grinding process. Experiments are designed as per full factorial design, wherein infeed, longitudinal feed and work speed have been considered as the important input parameters. Analysis of variance and graphical main and interaction plots have been plotted for analyzing the experimental data for identifying the relationships between grinding parameters and surface roughness. The variation of the performance parameter (surface roughness) with grinding parameters has been mathematically modeled by RSM. SA algorithm has been employed for solving the obtained mathematical model. Finally, the validation exercise is performed with optimum levels of grinding parameters. The results confirm the efficiency of the approach employed for prediction of surface roughness in this study.

Experimental investigations on the laser-assisted machining of single crystal Si for optimal machining

Optimization of Surface Roughness and MRR in Electrochemical Machining of EN31 Tool Steel Using Grey-taguchi Approach

Experimental study of heat affected zone for CO2 and fiber laser machining of SS 316L material

Modelling of kerf width and surface roughness using vibration signals in laser beam machining of stainless steel using design of experiments

Hybrid-Electrochemical Magnetorheological (H-ECMR) Finishing process is an advanced surface finishing technique that produces surface roughness (Ra) up to a few nanometers required for the implants. In this study, duplex stainless steel (DSS), a biomaterial constituting ferrite and austenite in equal proportion, is considered as the workpiece. DSS is widely used for its high corrosion resistance compared with other grades of stainless steel biomaterials. Furthermore, optimization of the process parameters for the trochoidal, a high-speed finishing toolpath, is carried out to achieve optimum surface roughness parameters during H-ECMR finishing. The surface roughness parameters (i.e., average Surface roughness (Ra). Kurtosis (Rku), and Skewness (Rsk)) are attained from the integration of the Design of Experiment (DOE), Response Surface Methodology (RSM), and Machine Learning Genetic Algorithm (ML-GA). DOE is utilized to plan the experiments based on Central Composite Design (CCD) using input process parameters of the H-ECMR finishing process. The regression equation is developed with the RSM, and its optimization study is performed with ML-GA. This paper aims to analyze the impact of the process parameters for the H-ECMR finishing process on different surface roughness parameters.

The paper addresses the assessment, modeling, and optimization study of surface roughness in finish dry hard turning (FDHT) of AISI 4340 steel with coated ceramic tool by considering the cutting speed, axial feed, depth of cut, and nose radius as machining parameters. Thirty sets of longitudinal turning trials based on central composite (CCD) design of experiments (DOEs) are performed, and response surface methodology (RSM), particle swarm optimization (PSO), and finally, Gilbert’s approach are subsequently applied for mathematical modeling, response optimization, tool life estimation, and economic analysis. Additionally, various diagnostic tests have been executed to check the statistical significance and validity, adequacy, effectiveness, and fitness of data of the proposed model using analysis of variance (ANOVA) and Anderson-Darling normal probability test. Results indicated that nose radius and feed are the most significant controlled as well as dominant factors for hard turning operation if the minimization of the machined surface roughness is considered. The RSM model combined with PSO technique leads to minimum surface roughness value similar to cylindrical grinding of 0.2021 μm, corresponding to optimum process parameters: 220 m/min of cutting speed, 0.05 mm/rev of feed, 0.193 mm of depth of cut, and 1.6 mm of tool nose radius. Finally, under pre-cited optimum machining conditions, tool life was evaluated to perform cost analysis for the economical justification and feasibility of coated ceramic tools in hard turning. The total machining cost per piece is ensued to be lower ($0.34) as a consequence of higher tool life (44 min), reduction in downtime, and enhancement in savings, which finds economical benefits in hard turning. The novelty aspects of the present work are that (i) it demonstrates the replacement of expensive, time-consuming conventional cylindrical grinding process and proposes the alternative of costlier CBN tool by utilizing coated ceramic tool in hard turning processes considering technological, economical, and ecological aspects, which are helpful and efficient from industrial point of view and (ii) it contributes to practical industrial application of finish hard turning for the shaft and die makers to select the optimum cutting conditions in a range of hardness of 45–55 HRC.

Response Surface Methodology and Genetic Algorithm used to Optimize the Cutting condition for Surface Roughness Parameters in CNC Turning

Polyethylene Terephthalate Glycol (PETG) is a fused deposition modeling (FDM)-compatible material gaining popularity due to its high strength and durability, lower shrinkage with less warping, better recyclability and safer and easier printing. FDM, however, suffers from the drawbacks of limited dimensional accuracy and a poor surface finish. This study describes a first effort to identify printing settings that will overcome these limitations for PETG printing. It aims to understand the influence of print speed, layer thickness, extrusion temperature and raster width on the dimensional errors and surface finish of FDM-printed PETG parts and perform multi-objective parametric optimization to identify optimal settings for high-quality printing. The experiments were performed as per the central composite rotatable design and statistical models were developed using response surface methodology (RSM), whose adequacy was verified using the analysis of variance (ANOVA) technique. Adaptive neuro fuzzy inference system (ANFIS) models were also developed for response prediction, having a root mean square error of not more than 0.83. For the minimization of surface roughness and dimensional errors, multi-objective optimization using a hybrid RSM and NSGA-II algorithm suggested the following optimal input parameters: print speed = 50 mm/s, layer thickness = 0.1 mm, extrusion temperature = 230 °C and raster width = 0.6 mm. After experimental validation, the predictive performance of the ANFIS (mean percentage error of 9.33%) was found to be superior to that of RSM (mean percentage error of 12.31%).

The present paper aims to analyze the surface finish of polished silicon wafer by chemically etching with potassium hydroxide (KOH) and mechanical polishing by Double Disk Magnetic Abrasive Finishing (DDMAF) process. The study is emphasized to study the effect of process parameters i.e. polishing speed, working gap, abrasive mesh number and percentage weight of KOH on the surface roughness (Ra). Response surface methodology (RSM) has been used to plan the experiments and Analysis of variance (ANOVA) has been used to analyze the impact of each process parameter on surface roughness. Regression equation for surface roughness in terms of significant process parameters has been developed to determine the surface roughness of polished silicon wafer. The equation has been further optimized using optimizer available with Minitab 17 and Genetic Algorithm (GA) tool box available with MatLab 16, to obtain the optimum process parameters and to prefigure the minimum surface roughness. The confirmatory experiment was carried out at optimum parameters and the prefigured results were found to be closely matched with the experimental findings. The communication further vocalizes the study of surface integrity for the unpolished sample to polished sample at optimum parameter using SEM and AFM images.

The paper discusses the symmetric balanced incomplete block designs (SBIBD’s) and further verifies whether treatment adjustment in SBIBD is necessary using analysis of variance (ANOVA) method. Fortunately, the data collected for the analysis were in the same group of the balanced incomplete-block designs (BIBD’s), that is, symmetric balance incomplete block design (SBIBD).The ANOVA method is applied differently on the two sets of data arising from the symmetric balanced incomplete block designs (SBIBDs). In this work, the basic interest is to verify empirically whether treatment adjustment is necessary in SBIBDs with data from National Root Crop Research Institute (N.R.C.R.I), Umudike, AbiaState. To achieve this, we considered treatment (not adjusted), treatment (adjusted), and block (adjusted) in the ANOVA method. The study discovered that if ANOVA method is applied on the symmetric balanced incomplete block design (SBIBD) when a treatment is not significant, it turns out to be significant when the same treatment is adjusted. It obvious from the experiments that were carried out that the adjustment of the treatment in SBIBD’s is very necessary when the ANOVA method is applied. Apart from the improvement in the level of significance of the result, the error is also greatly reduced when treatment is adjusted.