Optimization Of Machining Parameters Research Articles

Multi-Step Two-Dimensional Ultrasonic-Assisted Grinding of Silicon Carbide: An Experimental Study on Surface Topography and Roughness

Two-dimensional ultrasonic-assisted grinding (2D-UAG) has exhibited advantages in improving the machining quality of hard and brittle materials. However, the grinding mechanism in this process has not been thoroughly revealed due to the complicated material removal behaviors. In this study, multi-step 2D-UAG experiments of silicon carbide are conducted to investigate the effects of machining parameters on surface quality. The experimental results demonstrate that the tool amplitude and the workpiece amplitude have similar effects on surface roughness. In the rough grinding stage, the surface roughness decreases continuously with increasing ultrasonic amplitudes and the material is mainly removed by brittle fracture with different surface defects. Under semi-finishing and finishing grinding steps, the surface roughness first declines and then increases as the tool amplitude or workpiece amplitude grows from 0 μm to 8 μm and the inflection point appears around 4 μm. The surface damage contains small-sized pits with band-like distribution and localized grooves. Furthermore, the influences of cutting parameters on surface quality are similar to those in conventional grinding. Discussions of the underlying mechanisms for the experimental phenomena are also provided based on kinematic analysis. The conclusions gained in this study can provide references for the optimization of machining parameters in 2D-UAG of hard and brittle materials.

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.

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.

Electro-mechanical analysis of nanostructured polymer matrix composite materials for 3D printing using machine learning

Recently, additive manufacturing (AM) techniques like 3D printing have emerged as a potentially game-changing example of digital manufacturing. However, high entry barriers of a tiny material library, different processing defects, and unpredictable product quality are still holding back its widespread use in the industry. Due to its remarkable success in data tasks like classification, regression, and clustering, machine learning (ML) has recently gained a great deal of interest in the subject of the material library. This paper examines the current state of ML applications in several key areas of AM, including polymer matrix composite materials and machine parameter optimization. Composite filaments have been extruded using Polylactic Acid (PLA) as it is a biodegradable material and shows how High-Density Poly Ethylene (HDPE) enhances physical strength. All the parameters for the filament extruder have been designed by machine learning. Thermal stability is a significant concern for polymers that have been overcome by introducing Titanium Dioxide nanoparticles. The microstructure, surface texture, electro-mechanical behavior, and other general features of extruded filaments made from recycled plastics have been investigated. The extrusion temperature, approximated using ML, is in excellent agreement with the surface texture and microstructure of the polymers, as confirmed by FESEM, EDX, and Particle analysis. Extruded filaments experienced 2500 Vs and confirmed their non-conductivity up to 77.7GΩ. Tensile strength and elongation at break, two measures of mechanical properties, have been examined. Incorporation of Titanium Dioxide Nanoparticles improved mechanical properties significantly. When it comes to 3D printing, the physical properties and potential uses of each composite material are different.

CMGWO: Grey Wolf Optimizer for Fusion Cell-like P Systems

The grey wolf optimizer is a widely used parametric optimization algorithm. It is affected by the structure and rank of grey wolves and is prone to falling into the local optimum. In this study, we propose a grey wolf optimizer for fusion cell-like P systems. Cell-like P systems can parallelize computation and communicate from cell membrane to cell membrane, which can help the grey wolf optimizer jump out of the local optimum. Design new convergence factors and use different convergence factors in other cell membranes to balance the overall exploration and utilization capabilities of the algorithm. At the same time, dynamic weights are introduced to accelerate the convergence speed of the algorithm. Experiments are performed on 24 test functions to verify their global optimization performance. Meanwhile, a support vector machine model optimized by the grey wolf optimizer for fusion cell-like P systems has been developed and tested on six benchmark datasets. Finally, the optimizing ability of grey wolf optimizer for fusion cell-like P systems on constrained optimization problems is verified on three real engineering design problems. Compared with other algorithms, grey wolf optimizer for fusion cell-like P systems obtains higher accuracy and faster convergence speed on the test function, and at the same time, it can find a better parameter set stably for the optimization of support vector machine parameters, in addition to being more competitive on constrained engineering design problems. The results show that grey wolf optimizer for fusion cell-like P systems improves the searching ability of the population, has a better ability to jump out of the local optimum, has a faster convergence speed, and has better stability.

Sustainability assessment of machining Al 6061-T6 using Taguchi-grey relation integrated approach

Modern machining requires reduction in energy usage, surface roughness, and burr width to produce finished or near-finished parts. To ensure high surface quality in machining processes, it is crucial to minimize surface finish and minimize burr width, which are considered as significant parameters as specific cutting energy. The objective of this study was to identify the optimal machining parameters for milling in order to minimize surface roughness, burr width, and specific cutting energy. To achieve this, the research investigated the impact of feed per tooth, cutting speed, depth of cut, and number of inserts on the responses across three intervals using Taguchi L9 array. Observing the responses by varying these parameters, underlined the need for multi objective optimisation. Machining conditions of 0.14 mm/tooth fz, 350 m/min Vc and 2 mm ap using 1 cutting insert (exp no 9) was identified as the best machining run using grey relational analysis owing to its highest grey relational grade of 0.936. ANOVA examination identified cutting speed as the leading factor impacting the grey relational grade with 31.07 % contribution ratio, with the number of inserts, depth of cut, and feed per tooth also making notable contributions. Conclusively, machining parameters identified through response surface optimisation resulted in 21.69 % improvement in surface finish, 11.39 % reduction in specific energy consumption, and 6.2 % decrease in burr width on the down milling side albeit with an increase of 9 % in burr width on the up-milling side.

Effect of crystal orientation on surface/subsurface damage characteristics of nano-cutting Ni-based single crystal superalloy

In ultra-precision machining, material anisotropy has an essential impact on surface generation and subsurface damage. In this work, the molecular dynamics method is applied to create the diamond nano-cutting models of Ni-based single crystal (NBSX) superalloy workpieces with different crystal orientations, and the surface/subsurface damage characteristics, crystal structure changes, cutting force, and atomic temperature and stress are discussed. The results show significant differences in surface/subsurface quality, crystal structure transformation, cutting force, atomic temperature and stress distributions and dislocation density obtained from nano-cutting workpieces along different crystal orientations. In four groups of workpieces, the surface/subsurface quality is used as an evaluation index, which shows that the [110](001) crystal orientation has the best cutting effect, and the [111](1‾ 10) crystal orientation is the worst cutting direction. This work reveals the damage generation mechanism of the machined surface/subsurface of NBSX superalloy on an atomic scale, which provides technical support for improving machining quality and optimizing machining parameters.

Minimization of residual stress, surface roughness and tool wear in Electro Discharge Machining of inconel 625

Electro Discharge Machining (EDM) is a well-known non-traditional machining technique which is widely used to make die castings and turbine blades out of difficult-to-cut materials like Inconel 625. The machined part's surface quality and dimensional accuracy are affected by the EDM electrode's wear. Moreover, surface roughness and residual stress augmentation of EDM-machined components can reduce the workpiece's lifespan by decreasing the fatigue life of machined parts. To enhance the precision and durability of EDM machined components, residual stress, tool wear, and surface roughness should be evaluated and minimized. The majority of published research works for the assessment and optimization of EDM machining parameters are based on experimental works which are limited to testing, workpiece materials, and machining conditions. In order to improve the quality of machined surfaces and reduce tool wear and residual stress during Inconel 625 EDM processes, a virtual machining approach has been developed in the research work. To predict the cutting temperature during EDM operations, the modified Johnson Cook model of Inconel alloys is used. The finite element approach is then used to calculate the generated residual stress during the EDM process. The proposed virtual machining approach is also applied in order to predict the surface roughness of EDM machined parts. To minimize the residual stress, tool wear, and surface roughness during EDM operations of Inconel 625, the machining parameters of gap voltage, peak current, pulse-off time, and pulse-on time are optimized using the Taguchi optimization approach. Experiments and simulations are then conducted to verify the developed virtual machining system in the study. So, using the optimal machining parameters, the residual stress, surface roughness of machined items and wear of EDM electrode are minimized by 24.5 %, 25.4 %, and 25.4 % respectively. Thus, the quality and reliability of components made using EDM processes may be enhanced by the suggested virtual machining system.

Observation of Gap Phenomena and Development Processing Technology for ECDM of Sapphire

The main purpose of this study was to develop observation techniques and processing technology for the electrochemical discharge machining (ECDM) of sapphire wafers. To measure the effect of gas-film thickness, discharge-spark conditions, and droplet sliding frequency on machining quality and efficiency in ECDM, this research utilized high-speed cameras to observe the gas film thickness and formation of the gas film during ECDM. Additionally, this study observed the machining-gap phenomena during ECDM. The formation mechanism and machining characteristics of the gas film were understood through experiments. The machining parameters included the liquid level, working voltage, rotation speed, and duty factor. This study analyzed and discussed the effect of each machining parameter on the gas-film thickness, current, electrode consumption, and droplet sliding frequency. Moreover, this study aimed to obtain optimized machining parameters to overcome the difficulty of machining sapphire. The experimental results indicated that utilizing a high-speed camera to capture the phenomena between electrodes during electrochemical discharge could effectively observe the gas-film thickness and the coverage of the gas film. A higher bubble coalescence rate enhanced the machining capability and reduced the lateral discharge. Therefore, this study could obtain better machining-hole depths through observation and analysis to improve gas-film stability and machining capability. This study demonstrated that a liquid level of 700 µm, a working voltage of 48 V, a duty factor of 50%, and a tool electrode rotational speed of 200 rpm could achieve a hole depth of 86.7 µm and a hole diameter of 129.5 µm.

Optimizing Machining Efficiency in High-Speed Milling of Super Duplex Stainless Steel with SiAlON Ceramic Inserts

Super duplex stainless steels (SDSSs) are widely utilized across industries owing to their remarkable mechanical properties and corrosion resistance. However, machining SDSS presents considerable challenges, particularly at high speeds. This study investigates the machinability of SDSS grade SAF 2507 (UNS S32750) under high-speed milling conditions using SiAlON insert tools. Comprehensive analysis of key machinability indicators, including chip compression ratio, chip analysis, shear angle, tool wear, and friction conditions, reveals that lower cutting speeds optimize machining performance, reducing cutting forces and improving chip formation. Finite element analysis (FEA) corroborates the efficacy of lower speeds and moderate feed rates. Furthermore, insights into friction dynamics at the tool–chip interface are offered, alongside strategies for enhancing SDSS machining. This study revealed the critical impact of cutting speed on cutting forces, with a significant reduction in forces at cutting speeds of 950 and 1350 m/min, but a substantial increase at 1750 m/min, particularly when tool wear is severe. Furthermore, the combination of 950 and 1350 m/min cutting speeds with a 0.2 mm/tooth feed rate led to smoother chip surfaces and decreased friction coefficients, thus enhancing machining efficiency. The presence of stick–slip phenomena at 1750 m/min indicated thermoplastic instability. Optimizing machining parameters for super duplex stainless steel necessitates balancing material removal rate and surface integrity, as the latter plays an important role in ensuring long-term performance and reliability in critical applications.

Performance analysis and optimization of machining parameters using coated tungsten carbide cutting tool developed by novel S3P coating method

Performance analysis and optimization of machining parameters using coated tungsten carbide cutting tool developed by novel S3P coating method

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Determination of Optimal Machining Parameters Based on Roughness and Vibration Measurements of Pieces Produced by Whirling on a Lathe Machine

Worms can be produced using special machines or standard lathes equipped with a whirling thread-cutting device. A blank is placed on the mandrel and tightened using the three-jawed chuck of the standard lathe. If the workpiece diameter is excessively large, passage through the driven pulley is not possible, and the workpiece cannot be supported. Therefore, a new tool holder for whirling devices is needed. During the whirling process, vibrations in the form of machine velocity amplitudes were measured. After whirling was complete, roughness values were calculated. Using numerical procedures of Wolfram Mathematica 10, vibration peaks were extracted, from which frequencies and maximum amplitudes were determined. The data were then inputted into Design Expert, and the rotational speed and amount of separated material were optimized. The results of the study showed that the quality of the processed surface did not improve with processing in two passes of the tool. The measured vibration amplitudes on the lathe carrier and thread whirling attachment increased with cutting speed at the same cutting depth, whereas the quality of the machined surface was best at the smallest and largest cutting depths.

An Investigation and Machining Parameter Optimization on Inconel 713 by Using DOE Technique

An Investigation and Machining Parameter Optimization on Inconel 713 by Using DOE Technique

Effects of machining parameters, ultrasonic vibrations and cooling conditions on cutting forces and tool wear in meso scale ultrasonic vibrations assisted end-milling (UVAEM) of Ti–6Al–4V under dry, flooded, MQL and cryogenic environments – A statistical analysis

Ti–6Al–4V, an alloy of titanium has recently gained focus of research to overcome its machinability challenges. Miniaturization in devices has forced a shift towards micro and meso-scale machining. Sustainability and green manufacturing concepts have shifted focus in machining from cutting fluids towards modern cooling approaches to minimize or eliminate them. In present research, cutting forces & tool wear in Ultrasonic Vibrations Assisted End Milling of Ti–6Al–4V at meso-scale under Dry, Flooded, Minimum Quantity Lubrication & Cryogenic cooling have been analyzed to develop optimum machining parameters. Taguchi L16 orthogonal array was designed to perform experiments keeping Cutting Speed, Feed per tooth, Depth of cut, Ultrasonic vibrations amplitude & Cooling environment as inputs. ANOVA was used to analyze contribution ratios by these parameters towards cutting forces & tool wear. Results indicated that machining conditions, cooling environment and Ultrasonic Vibrations influence cutting forces & tool wear. Depth of cut had the highest influence with 63.41% contribution towards cutting forces while cutting speed remained the highest influencing factor with 39.86% contribution towards tool wear. Cutting forces were reduced by 33.49%, 16.93% and 4.91% while tool wear was reduced by 26.43%, 9.48% and 5.17% under Minimum Quantity Lubrication environment compared to dry, flooded and cryogenic cooling respectively. Cutting forces and tool wear under ultrasonic vibrations were reduced by 24.52% and 13.16% respectively as compared to Conventional Machining (CM). For optimum results, Minimum Quantity Lubrication with low Cutting Speed, Depth of cut, Feed per tooth and high amplitude of ultrasonic vibrations is recommended to machine Ti–6Al–4V.

Effective optimization strategies for abrasive water jet machining of glass-carbon fiber reinforced composites: A comparative study of evolutionary optimization techniques

This study applied evolutionary optimization techniques and neural networks to predict optimum machining parameters of Abrasive Water Jet Machining (AWJM) for machining Glass-Carbon Fiber Reinforced Composite (GCFRC) materials. Several researchers have employed different optimization techniques; however, evolving computational capabilities further open avenues to optimize such parameters. Five evolutionary techniques, namely Artificial Neural Network (ANN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Nonlinear Least Square Error (LSE), were applied to minimize surface roughness (Ra) and maximize kerf width (Kw) and material removal rate (MRR). The output performance parameters (Ra, Kw, and MRR) were formulated as a linear mathematical function of machining parameters: tool feed rate (TFR), cutting speed rate (CSR), and stand-off distance (SOD). Though this study solves linear optimization problems, the proposed technique will be a strong tool for solving complex associations of machining and performance parameters in the future. A dataset of machining parameters and subsequent performance parameters was adopted from the available literature. The results indicated that the LSE method outperformed other techniques, yielding the lowest Root Mean Square Error (RMSE) in predicting Ra, Kw, and MRR, thus ensuring high machining accuracy. LSE technique reported relatively least RMSE values of 0.37 µm, 0.149Mm, and 237.23 mm3/min for Ra, Kw, and MRR, respectively. SA and PSO displayed identical and competitive RMSE values, slightly higher than LSE (up to 20 % higher). ANN and GA techniques were not effective relative to other considered techniques. LSE, SA, and PSO provide superior performance in optimizing AWJM parameters. The significant contribution of this research is the proposed optimization technique, offering a clear direction for solving complex associations between the performances and machining parameters of AWJM. This work also provides a foundation for future research to optimize such associations for other machining setups.

Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.

Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) involves machine learning through extensive trial-and-error, demonstrating performance exceeding humans, and existing algorithms in several domains. To develop and evaluate an RL approach for VMAT MPO for localized prostate cancer to rapidly and automatically generate deliverable VMAT plans for a clinical linear accelerator (linac) and compare resultant dosimetry to clinical plans. We extended our previous RL approach to enable VMAT MPO of a 3D beam model for a clinical linac through a policy network. It accepts an input state describing the current control point and predicts continuous machine parameters for the next control point, which are used to update the input state, repeating until plan termination. RL training was conducted to minimize a dose-based cost function for prescription of 60Gy in 20 fractions using CT scans and contours from 136 retrospective localized prostate cancer patients, 20 of which had existing plans used to initialize training. Data augmentation was employed to mitigate over-fitting, and parameter exploration was achieved using Gaussian perturbations. Following training, RL VMAT was applied to an independent cohort of 15 patients, and the resultant dosimetry was compared to clinical plans. We also combined the RL approach with our clinical treatment planning system (TPS) to automate final plan refinement, and creating the potential for manual review and edits as required for clinical use. RL training was conducted for 5000 iterations, producing 40 000 plans during exploration. Mean±SD execution time to produce deliverable VMAT plans in the test cohort was 3.3±0.5 s which were automatically refined in the TPS taking an additional 77.4±5.8 s. When normalized to provide equivalent target coverage, the RL+TPS plans provided a similar mean±SD overall maximum dose of 63.2±0.6Gy and a lower mean rectum dose of 17.4±7.4 compared to 63.9±1.5Gy (p=0.061) and 21.0±6.0 (p=0.024) for the clinical plans. An approach for VMAT MPO using RL for a clinical linac model was developed and applied to automatically generate deliverable plans for localized prostate cancer patients, and when combined with the clinical TPS shows potential to rapidly generate high-quality plans. The RL VMAT approach shows promise to discover advanced linac control policies through trial-and-error, and algorithm limitations and future directions are identified and discussed.

Examining the Impact of Metallurgical and Optimised Machining Parameters on Inconel 713C Superalloy Using the Taguchi Technique

Examining the Impact of Metallurgical and Optimised Machining Parameters on Inconel 713C Superalloy Using the Taguchi Technique

Experimental analysis and optimization of machining parameters for Nitinol alloy: A Taguchi and multi-attribute decision-making approach

Abstract The automotive and aerospace sectors have a strong demand for Nitinol alloy machined parts; therefore, optimizing machining parameters is essential to achieving better process performance results in terms of cost and product quality. In general, the process variables that influence machining include feed (f), depth of cut (t), and spindle speed (S). Material removal rate (MRR), tool wear (TW), and surface roughness (Ra) are pertinent output performance indicators. Analysis of variance has been performed to assess the effect of process variables on the aforesaid output performance. It has been found that feed has a significant effect on MRR and surface roughness with a contribution of 50.65 and 33.62%, respectively, whereas spindle speed has a major contribution on TW with a contribution of 51.9%. This study assesses how well the Nitinol 56 machining process works overall. In this work, the Taguchi method has been used to determine the effect of aforesaid process variables on the output performance indices. To satisfy previously stated conflicting performance indices, a variety of multi-attribute decision-making approaches were used, such as utility, TOPSIS, and grey, to determine the optimal process variables. The optimal process variable combination has been achieved as f = 0.133 mm·rev−1, d = 0.06, and S = 835 RPM. This combination has been achieved using all methods.

Multi objective optimization of process variables using grey relational analysis for drilling of hybrid composites (LM6/B4C/fly ash)

Aluminium alloys continue to be an effective material for the aviation and automotive sectors in spite of the development of composites and other lightweight materials because of their sophisticated manufacturing techniques, strong durability against fatigue fracture propagation, and excellent impact endurance. As numerous holes are needed for creating rivets, drilling is the most difficult of all the machining operations. The primary problems with drilling these aluminium alloys are represented by a low hole quality, which increases the risk of airframe construction defects and lowers reliability. As a result, parts get discarded throughout the process of assembling, which affects the overall cost of production. To satisfy the needs of machined parts, the selection of Feed rate (F), Spindle Speed (S), Drill Material (D) and percentage of reinforcements (R) as well as drilling equipment, is necessary. The objective of this research work is to study the impact of cutting parameters on surface roughness (SR) and thrust force (TF) during drilling Hybrid MMCs, which were prepared by stir casting. The reinforcements used for fabrication were fly ash and boron carbide. A computer numerical control VMC with a cutting tool dynamometer for measuring TF was used for the experiments. The investigations used the L18 orthogonal array as their base of operation. The multi response optimization considering TF and SR was carried out using grey relational analysis (GRA) and the optimal level of cutting parameters like ‘F’, ‘S’, ‘D’ and ‘R’ were identified to obtain low TF and SR. The optimal machining parameters which give low TF and low SR are at 6 % reinforcement, coated carbide drill, S 3000 rpm and F 50 mm/min. Feed Rate (67.20 %) has the most significance on GRG trailed by S (15.88 %), Drill Material (6.39 %) and reinforcement percentage (3.65 %). Confirmation experiments demonstrated that grey relational analysis precisely optimized the process variables in drilling of hybrid MMCs.