Prediction Of Surface Roughness Research Articles

Surface roughness estimation using vibration characteristics extracted by variational mode decomposition in turning

Abstract This study presents an approach that combines variational mode decomposition (VMD) and relevance vector machine (RVM) as a prediction technique for surface roughness during turning. The method helps machinists detect irregularities during machining, such as looseness in machine parts or tool wear. The study uses work hardened EN8 steel and extracts vibration signal features into five modes. PSO optimized RVM prediction models are developed using these modes with and without combining cutting parameters. The most sensitive mode is selected for accurate surface roughness prediction. The results show that the first mode of decomposition when combined with cutting parameters provides the least mean square error of 0.1095. Surface roughness was found to be primarily influenced inversly by cutting speed and directly by feed rate respectively. The signal with high surface roughness value has high amplitude noise scattered over the large frequency range. The first decomposed mode of vibration signal increases noise with a large amplitude as surface roughness increases. The approach can be integrated into a microcontroller to regulate machine settings based on vibration data.

Surface Roughness Model of Ground 4H-SiC Considering Ductile and Brittle Removal

Abstract Surface roughness is a critical indicator to evaluate the quality of ground surfaces for 4H-SiC. Determining surface roughness experimentally is a time-consuming and laborious process, and developing a reliable model for predicting surface roughness is a key challenge in 4H-SiC grinding. However, the existing models for surface roughness in wafer rotational grinding fail to yield reasonable results because they do not adequately consider the processing parameters and material characteristics. In this study, a new analytical model for surface roughness in 4H-SiC wafer rotational grinding is proposed, which comprehensively incorporates the grinding conditions and material characteristics of brittle substrate. This model derives and calculates the material's elastic recovery coefficient based on contact mechanics and elastic contact theory. Subsequently, we modified the grain depth-of-cut model by incorporating elastic recovery coefficient. Additionally, we considered the co-existing of machining-induced ductility and brittleness of the substrate surface under random grain depth-of-cut distribution that conforms to the Rayleigh distribution. To validate the accuracy of the proposed model, a series of grinding experiments are conducted using various grain depth-of-cut to produce 4H-SiC wafers with different surface roughness values. These results are then compared with those predicted by both the proposed model and the existing models. The findings demonstrate that the predictions obtained from the proposed model exhibit better agreement with the experimental results. This research addresses the need for an improved surface roughness model in 4H-SiC wafer rotational grinding.

Prediction of Surface Roughness in Hybrid Magnetorheological Finishing of Silicon Using Machine Learning

Prediction of Surface Roughness in Hybrid Magnetorheological Finishing of Silicon Using Machine Learning

Enhancing machining accuracy of banana fiber-reinforced composites with ensemble machine learning

Through innovative predictive modeling, this study advances Abrasive Water Jet Machining (AWJM) for banana fiber-reinforced biocomposites. Utilizing Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) models, hyperparameters are meticulously tuned for predicting Surface Roughness (Ra), Material Removal Rate (MRR), and Kerf Angle (Ka). Optimal configurations are identified, such as 3–6-6–4-1, 3–5-5–4-1, and 3–5–4–4-1 for ANN models, and 250, 100, and 50 units for LSTM models. Beyond individual models, the study explores stacking ensemble models, merging ANN and LSTM strengths with a Linear Regression final estimator, showcasing robust performance validated with high R-squared values. Regression models from Design Expert 13 contribute to understanding process parameter-outcome relationships. Optimized input parameters offer insights into minimizing surface roughness and kerf angle while maximizing MRR. This holistic approach, integrating advanced machine learning and ensemble learning, enhances predictive accuracy for banana-reinforced biocomposites, providing a versatile framework for diverse materials processing applications.

Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning

Surface integrity is one of the key elements used to judge the quality of machined surfaces, and surface roughness is one such quality parameter that determines the pass level of the machined product. In the present study, AISI D2 steel was machined with electric discharge at different process parameters using Jatropha and EDM oil. Titanium dioxide (TiO2) nanopowder was added to the dielectric to improve surface integrity. Experiments were performed using the one variable at a time (OVAT) approach for EDM oil and Jatropha oil as dielectric media. From the experimental results, it was observed that response trends of surface roughness (SR) using Jatropha oil are similar to those of commercially available EDM oil, which proves that Jatropha oil is a technically and operationally feasible dielectric and can be efficiently replaced as dielectric fluid in the EDM process. The lowest value of S.R. (i.e., 4.5 microns) for EDM and Jatropha oil was achieved at current = 9 A, Ton = 30 μs, Toff = 12 μs, and Gap voltage = 50 V. As the values of current and pulse on time increase, the S.R. also increases. Current and pulse-on-time were the most significant parameters affecting S.R. Machine learning methods like linear regression, decision trees, and random forests were used to predict the surface roughness. Random forest modeling is highly accurate, with an R2 value of 0.89 and an MSE of 1.36% among all methods. Random forest models have better predictive capabilities and may be one of the best options for modeling complex EDM processes.

Modelling Surface Roughness of 3/1 Twill Fabric Using Weft Yarn Count and Weft Thread Density

The surface roughness of fabric is one of the fabric properties that is used to evaluate the sensorial comfort of clothes. However, its objective evaluation requires sophisticated and expensive testing instruments and skilled testing expertise. Developing a predictive model is therefore an alternative approach to overcome such limitations. This study investigated a regression model to predict the surface roughness of 3/1 twill fabric using weft yarn count and weft thread density. Nine samples were produced by varying the weft yarn count and weft thread density at three different levels, while their surface roughness was determined using a Kawabata instrument (KES-FB4) under standard testing conditions. A two-factor predictive model equation was developed using design expert software. Based on the results and findings, the effects of count and density on the roughness of 3/1 twill fabric were found to be statistically significant for the developed model at a confidence interval of 95%. The model was tested by correlating the measured and predicted surface roughness values of 100% cotton 3/1 twill fabric. The results of the model test indicate a significant correlation (R2 = 0.9644) between the measured and predicted surface roughness values of 3/1 twill fabric, with a 95% confidence interval. Model validation was performed, and the study showed that the measured and predicted values of the surface roughness of 3/1 twill fabric have a 0.828 coefficient of determination (R2). This indicates that the surface roughness of 3/1 twill fabric can be predicted well by weft yarn count and weft thread density. This model can be thus used in textile industries and by research institutes for predicting the surface roughness of 3/1 twill fabrics in the new product development process.

Modeling and experimental study of surface roughness in smart MR fluid-based finishing process

Ball-end magnetorheological finishing (BEMRF) is a high-efficiency nano-finishing technology based on smart magnetorheological fluid to polish complex materials precisely. The surface roughness can be linked with the experimental data to understand the finishing mechanism based on advancements in manufacturing automation and the prediction of material removal. This study proposes a mathematical model to predict surface roughness, helping to understand the effects of various process parameters on surface quality during the BEMRF process. This study adds value to practitioners by providing a mathematical model to predict the optimum parameters for attaining the desired surface finish in BEMRF polishing.

Fuzzy Logic Based Model to Predict Surface Roughness of Si(100) Wafer Using Preliminary Experimental Data Obtained From Single Pole Magnetic Abrasive Finishing Process

Fuzzy Logic Based Model to Predict Surface Roughness of Si(100) Wafer Using Preliminary Experimental Data Obtained From Single Pole Magnetic Abrasive Finishing Process

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.

Comprehensive stratified K-fold machine learning for surface roughness prediction with 3D printed electrodes: Analyzing dielectric variability and heat treatment effects

Electrical discharge machining (EDM) is popular for its accuracy in cutting difficult materials. Material removal rate (MRR), electrode wear ratio (EWR) and surface roughness (SR) are deemed as the success indicators in EDM. The positive attributes of sustainable manufacturing make it a suitable choice for diverse applications. EDM electrodes used in this work are prepared by 3D printing and further subjected to deep cryogenic treatment (DCT) to investigate the influence of heat treatment on EDM performance. In the second instance, the experiments are conducted with different dielectric fluids viz. conventional dielectric (CD) and lemon peel biodiesel dielectric (LPD) to analyze the impact of dielectric change. When the dielectric is changed to lemon peel biodiesel, the roughness of the electro-discharged machined surface is reduced to a maximum of about 33%. A further improvement of about 11% is possible in the surface finish of machined surfaces when the EDM electrodes are subjected to DCT under an identical operating condition. LPD as dielectric also helps in attaining the primary objective of green manufacturing by reducing specific evaporative emissions. During the EDM process, LPD adoption causes a reduction of 96% in nitrogen oxide (NO) emissions and 86% in hydrocarbon (HC) emissions. A modified adaptive neuro fuzzy inference system (ANFIS) with a stratified k-fold machine learning approach with a prediction accuracy of about 95% is also developed in this work to envisage the surface roughness of electro-discharge machined surfaces.

Leveraging IoT in Auto Component Manufacturing to Monitor Surface Roughness and Tool Temperature

Surface finish is an important requirement in Auto Component Manufacturing that defines the quality of work piece obtained from turning operations. Thus to reduce downtime due to rejection of work piece for its surface finish a continuous monitoring of surface roughness based on its cutting parameter and vibration signal is required. Similarly it is also important to monitor the cutting tool temperature to have a longer tool life. With the emerging trend of Internet-of-Things (IoT) it has become very easy to collect the required data, storing it, analysing it and also alerting the required personnel through the mobile application, e-mail and SMS. The stored data and analysis could reveals in depth the process, which leaves a large scope for improvement of the system. The paper discusses the implementation of IoT to collect data of vibration signal and cutting tool temperature of dry turning operation, for cutting parameter like 1) spindle speed 2) feed rate and 3) depth of cut for three different material mild steel (MS), brass and aluminium (Al). A correlation was obtained to predict surface roughness and tool temperature. The system developed could give a real time monitoring of surface roughness on a smart phone that can be used as a tool for optimizing the turning process to give improved quality and increased production volume.

Multi-axis CNC finishing and surface roughness prediction of TC11 titanium alloy open integral micro impeller

The titanium alloy open integral micro impeller has a strong material strength and high removal rate in the field of multi-axis CNC machining. The flow channel is tiny and the blades are thin and highly twisted. It is difficult to control the surface accuracy and prone to overcutting and undercutting. The NX2212 software post-processing module plans two distinct blade finishing process routes and verifies them using virtual machine tool simulation, taking into account the technical challenges of micro impeller machining. Following verification, the tool path machining code is imported into MATLAB for data fitting. The workpiece surface working condition is determined based on the simulation findings, the blade surface roughness value is calculated, and a physical simulation model of blade finishing is created in the finite element analysis software. The outcomes demonstrate how well the “segmented and sub-regional cutting” processing method may raise blade accuracy. The leading and trailing edges of the blade both had surface roughness increases of 4.86% and 4.19%. The surface morphology of the micro impeller is measured using a white light interferometer, and it is CNC machined using two distinct process methods. The findings demonstrate that there is a significant difference between the value calculated by the finite element analysis software and the surface roughness value measured experimentally which together make up less than 5%. An investigation of the impact of cutting parameters on the surface roughness of micro-structure components is carried out using a three factor, three-level BBD experiment that is founded on the second-order response surface method. The findings indicate that the feed per tooth influences surface roughness more significantly than cutting depth and cutting speed for a reasonable range of cutting parameters; Surface roughness will rise with lower or higher cutting speeds; Raising the feed per tooth and the cutting speed simultaneously may reduce surface roughness; Surface roughness can be accurately predicted and controlled using the second-order response surface method.

Machinability investigation of natural fibers reinforced polymer matrix composite under drilling: Leveraging machine learning in bioengineering applications

The growing demand for fiber-reinforced polymer (FRP) in industrial applications has prompted the exploration of natural fiber-based composites as a viable alternative to synthetic fibers. Using jute–rattan fiber-reinforced composite offers the potential for environmentally sustainable waste material decomposition and cost reduction compared to conventional fiber materials. This article focuses on the impact of different machining constraints on surface roughness and delamination during the drilling process of the jute–rattan FRP composite. Inspired by this unexplored research area, this article emphasizes the influence of various machining constraints on surface roughness and delamination in drilling jute–rattan FRP composite. Response surface methodology designs the experiment using drill bit material, spindle speed, and feed rate as input variables to measure surface roughness and delamination factors. The technique of order of preference by similarity to the ideal solution method is used to optimize the machining parameters, and for predicting surface roughness and delamination, two machine learning-based models named random forest (RF) and support vector machine (SVM) are utilized. To evaluate the accuracy of the predicted values, the correlation coefficient (R2), mean absolute percentage error, and mean squared error were used. RF performed better in comparison with SVM, with a higher value of R2 for both testing and training datasets, which is 0.997, 0.981, and 0.985 for surface roughness, entry delamination, and exit delamination, respectively. Hence, this study presents an innovative methodology for predicting surface roughness and delamination through machine learning techniques.

Leveraging IoT in Auto Component Manufacturing to Monitor Surface Roughness and Tool Temperature

Surface finish is an important requirement in Auto Component Manufacturing that defines the quality of work piece obtained from turning operations. Thus to reduce downtime due to rejection of work piece for its surface finish a continuous monitoring of surface roughness based on its cutting parameter and vibration signal is required. Similarly it is also important to monitor the cutting tool temperature to have a longer tool life. With the emerging trend of Internet-of-Things (IoT) it has become very easy to collect the required data, storing it, analysing it and also alerting the required personnel through the mobile application, e-mail and SMS. The stored data and analysis could reveals in depth the process, which leaves a large scope for improvement of the system. The paper discusses the implementation of IoT to collect data of vibration signal and cutting tool temperature of dry turning operation, for cutting parameter like 1) spindle speed 2) feed rate and 3) depth of cut for three different material mild steel (MS), brass and aluminium (Al). A correlation was obtained to predict surface roughness and tool temperature. The system developed could give a real time monitoring of surface roughness on a smart phone that can be used as a tool for optimizing the turning process to give improved quality and increased production volume.

Multi-objective optimization for balancing surface roughness and material removal rate in milling hardened SKD11 alloy steel with SIO2 nanofluid MQL

In manufacturing practice, manufacturers always strive to achieve both quality and productivity targets simultaneously. In the first part, this study examines the relationship between input factors, including cutting speed, depth of cut, and feed rate, and the output response, which is surface roughness, when milling hardened SKD11 alloy steel under minimum coolant lubrication conditions using SiO2 nanofluid. The input parameters are divided into four levels to determine their influence on surface roughness and to find the optimal conditions for achieving the minimum surface roughness. The experimental design was conducted using an L16 array. A second-order regression model was developed to describe the relationship between the input variables and the output response. In the second part, multi-objective optimization was performed to simultaneously achieve the minimum surface roughness and the maximum material removal rate (MRR). The Response Surface Methodology (RSM) was employed in this study. The results indicated that to achieve the minimum surface roughness, machining should be performed at a cutting speed of 100 m/min, a cutting depth of 0.2 mm, and a feed rate of 0.01 mm/tooth. With these settings, the predicted surface roughness could reach 0.0451 µm. On the other hand, for the multi-objective optimization, to achieve the minimum surface roughness and the maximum MRR simultaneously, machining should be carried out at a cutting speed of 100 m/min, a cutting depth of 0.36 mm, and a feed rate of 0.0168 mm/tooth. With this cutting condition, the predicted surface roughness could reach 0.1069 µm, and the predicted MRR could reach 775.06 mm3/min

A cutting-edge framework for surface roughness prediction using multiverse optimization-driven machine learning algorithms

A cutting-edge framework for surface roughness prediction using multiverse optimization-driven machine learning algorithms

Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression.

Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain and it is executed using Welch's method complemented by time-frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry.

A multi-target predictive model for predicting tool wear and surface roughness

To simultaneously predict tool wear and surface roughness accurately during machining, this paper develops a multi-target predictive model that leverages a novel two-step feature-integration approach and Multi-kernel Gaussian process autoregressive regression (MK-GPAR). Compared with the traditional Gaussian process autoregressive regression (GPAR) models, the MK-GPAR demonstrates an enhanced capability in capturing detailed structures within heterogeneous data originating from various sources or disparate data forms. The weight coefficients of the kernel functions in MK-GPAR are typically assigned a value of 1 to minimize their influence on the predictive model. Furthermore, when compared to conventional multi-target predictive models like Gradient Boosting, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN), the GPAR not only delivers higher-accuracy predictions for tool wear and surface roughness but also provides corresponding confidence intervals (CI). However, the CI of predictions for tool wear and surface roughness provided by MK-GPAR are inconsistent, which will seriously affect the reliability of the evaluation of the prediction results. The proposed two-step feature-integration approach is employed to enhance signal features effectively, reduce noise, and mitigate its adverse impact, thus enhancing the consistency and smoothness of the CI. The two-step feature-integration approach integrates principal component analysis (PCA) and orthogonal neighborhood preserving projections (ONPP) to merge force and vibration signal features obtained from the fixture's monitoring signals. Following the two-step feature-integration (first PCA, then ONPP) process, the MK-GPAR is utilized to build a multi-target predictive model for tool wear and surface roughness prediction. The impact of the proposed two-step feature-integration method on the prediction performance of the MK-GPAR is demonstrated revealed milling experiments. The experimental findings indicate that the proposed two-step feature-integration method significantly enhances the accuracy of predictions and the consistency of CI of the MK-GPAR. This research lays the foundation for implementing the multi-target predictive model in real industrial environments to predict tool wear and surface roughness.

A novel comprehensive framework for surface roughness prediction of integrated robotic belt grinding and burnishing of Inconel 718

Surface roughness serves as a critical metric for assessing surface integrity, directly influencing the service performance of components. Hardening burnishing is a regularly used post-grinding procedure for Inconel 718 that can simultaneously reduce surface roughness and improve surface mechanical characteristics. A novel comprehensive framework is proposed to predict the surface roughness of Inconel 718 processed by the integrated robotic belt grinding and burnishing system developed in this study. Accounting for abrasive grain conditions and material removal rate during grinding, the L-BFGS-B optimization algorithm and image processing are utilized to predict the ground surface roughness first. Building upon this foundation, theoretical models are applied to ascertain the critical burnishing force needed to enter the hardening burnishing stage. Following this, the Expansion Cavity Model and Hertz theory are used to predict the roughness of the hardening burnished surface, which is only applicable when the step-over value exceeds a critical value. The proposed framework not only enhances our comprehension of the mechanisms underlying material surface deformation but also offers a novel avenue for predicting surface roughness during integrated robotic belt grinding and burnishing with high accuracy.

Полуэмпирическое моделирование температуры резания и шероховатости поверхности при точении конструкционных материалов твердосплавным инструментом с покрытием TiAlN

Introduction. In manufacturing, obtaining a specified surface roughness of the machined components is of great importance to fulfill functional requirements. However, this is significantly affected by the heat generated during processing, potentially causing variations in dimensional accuracy. The surface roughness significantly affects the fatigue performance of the component, while the cutting tool's lifespan is dictated by the generation of cutting temperatures. The purpose of the study is to create semi-empirical models for predicting surface roughness and temperature of different work materials. Improved cutting performance is achieved by precisely determining the cutting temperature in the zone being machined. However, calculating the cutting temperature for each specific case is fraught with difficulties in terms of labor resources and financial investments. This paper presents a comprehensive empirical formula designed to predict both theoretical temperature and surface roughness. The methodology. The surface roughness and temperature values were evaluated for EN 8, Al 380, SS 316 and SAE 8620 materials using TiAlN coated carbide tool. The TiAlN coating was formed using Physical Vapor Deposition (PVD) Technique. The response surface methodology was used to prepare predictive models. Cutting speed (140 to 340 m/min), feed (0.08 to 0.24 mm/rev) and depth of cut (0.6 to 1.0 mm) was used as input parameters for measuring the performance of all material in terms of surface roughness and cutting temperature. The tool-work thermocouple principle was used to measure the temperature at the chip-tool interface. A Novel Calibration Setup was developed to establish a connection between the electromotive force (EMF) generated during machining and the cutting temperature. Results and Discussion. It is observed that the power required for machining was largely transformed into heat. The highest cutting temperature is recorded when machining of SS 316 followed by SAE 8620, EN 8. However, low temperature is reported during machining of Al 380 and it is mainly governed by the thermal conductivity of the material. The lowest surface roughness is observed in SAE 8620, EN 8 material followed by SS 316 and Al 380. Semi-empirical method and regression model equations show a good agreement with each other. Statistical analysis of nonlinear estimation reveals that the cutting speed, feed, and density of the material have a greater effect on surface roughness, whereas the depth of cut has a greater effect on temperature generation. The study will be very useful for predicting industrial productivity when machining of EN 8, Al 380, SS 316 and SAE 8620 materials with TiAlN-coated carbide tool.