Optimization Of Machining Parameters Research Articles

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

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.

Development and optimization of a mechanized jackfruit processing unit for enhanced efficiency and commercial viability

AbstractThe jackfruit is the largest edible fruit but remains underutilized due to challenges such as sticky latex, labor‐intensive peeling/coring, and lack of mechanization. This study developed and evaluated a jackfruit peeling, coring, and cutting machine to enhance processing efficiency. Performance was modeled using response surface methodology (RSM) and artificial neural network (ANN). Three jackfruit sizes (small, medium, and large) and three machine speeds (90, 120, and 150 RPM) were evaluated for peeling time (26.1–50.3 s), peeling efficiency (71.6%–85.3%), coring time (15.5–29.9 s), coring efficiency (74.7%–96.0%), and bulb wastage (6.2%–17.6%). RSM showed high model adequacy (R2 ≥ 0.97) and ANN confirmed prediction reliability (R2 = 0.81–0.99; mean square error = 4.4–44.9). Increasing fruit size significantly increased peeling and coring times but decreased efficiencies. Machine speeds caused minor variations. Optimized conditions of 120 RPM fruit holder speed and 150 RPM corer speed gave maximum desirability (0.869). The machine had a payback period of 2 years and benefit–cost ratio of 2.32 versus 2.66 for manual peeling/coring. The mechanized jackfruit processing will promote enhanced utilization of this nutritious fruit.Practical applicationsThe mechanized jackfruit peeling‐coring‐cutting machine developed in this study has significant practical utility. By enabling efficient and rapid processing of jackfruits, the machine can help tap the underutilized potential of this highly nutritious and functionally beneficial fruit. The optimized machine parameters allow jackfruit processing industries to achieve higher throughput with reduced wastage, thereby boosting productivity and profits. Additionally, the mechanization facilitates value‐addition by enabling jackfruit utilization in various processed products like chips, flour, jam, etc. Further, the machine helps create livelihood opportunities in jackfruit value chains, as it reduces drudgery and enhances process efficiency as compared to manual methods. The simple fabrication and operation also enable adoption by farmer‐producer organizations, self‐help groups, and community‐based jackfruit processing enterprises. Overall, the mechanized solution provides an impetus for sustainable utilization of jackfruit, while also addressing issues like food loss, nutrition security, income support, and women empowerment. The practical insights on machine performance modeling using response surface methodology and artificial neural network approaches further facilitate quality improvements in equipment design.

Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing

Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking machining energy, machining time and machining cost as optimization objectives. Next, the IHNN for multi-objective optimization of STEP-NC machining parameters combining with Pareto theory, improved immune algorithm and non-monotone activation function is designed. Based on it, the optimal Pareto solutions of STEP-NC machining parameters are obtained, which intelligently realizes the multi-objective optimization of STEP-NC machining parameters and provides a decision support for the decision-maker. Finally, performance comparisons among the IHNN, classic non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are done by classic test functions with three objectives and multiple constraints which correspond to the mathematical model established in this paper, and its effectiveness and feasibility is verified by case study.

Enhancing Machining performance in Stainless Steel Machining using MXene Coolant: A Detailed Examination

Metal cutting, a complex process in manufacturing, involves various factors that significantly affect the quality of the final product. Notably, the turning process is crucial, with outcomes that heavily depend on multiple machining parameters. These parameters encompass speed, depth of cut, feed rate, the type of coolant used (specifically, high heat transfer MXene coolant), and insert types, among others. The material of the workpiece is also a critical factor in the metal-cutting operation. This study focuses on achieving optimal surface quality and minimizing cutting forces in the turning process. It recognizes the substantial impact of numerous process parameters, directly or indirectly affecting the product's surface roughness and cutting forces. Understanding these optimal parameters can lower machining costs and improve product quality. Our research concentrates on turning a stainless-steel alloy workpiece using a carbide insert tool. We employ the Response Surface Method (RSM) to optimize cutting parameters within a set range of cutting speed (100, 125, 150 m/min), feed rate (0.1, 0.2, 0.3 mm/rev), and depth of cut (0.4, 0.8, 1.2 mm). Additionally, we use various tool geometries and the RSM design of experiments to enhance and analyze the multi-response parameters of surface roughness and tool life. Optimal machining parameters for MXene-NFC involve a cutting speed of 140 m/min, a feed rate of 0.05 mm/rev, and a depth of cut of 0.5 mm. These settings ensure minimal surface roughness, maximum tool life, and the greatest total length of cut, achieving a composite desirability of 0.695.

Evaluation and Performance Improvement of Environmentally Friendly Sustainable Turning of 6063 Aluminum Alloy in Dry Conditions Using Grey Relational Analysis

Sustainable machining has gained importance in recent years due to its environmental, economic, and societal implications. Aluminium (Al) 6063 alloy involves turning operation to make it suitable for various applications. The work's novelty is assessing the machining characteristics along with sustainability indicators. This study aims to find the best-turning parameters for machining Al 6063 alloy. The turning parameters considered were cutting speed, feed rate, and depth of cut. A cutting speed of 200 m/min, feed rate of 0.05 mm/rev, and depth of cut of 0.25 mm were the best parameter combinations for achieving a good machining response. From the response value of mean grey relational grade (GRG) and analysis of variance (ANOVA), the depth of cut ranks one with 34.38%, which is the most dominating parameter in achieving the sustainable machining of Al 6063 alloy. Through grey relational analysis, optimized machining parameters resulted in a 72.84 percent reduction in carbon emissions, 72.82 percent reduction in energy consumption, 18.58 percent reduction in cutting power, and 6.83 percent reduction in surface roughness considering the initial parameter settings and best machining parameters. The enhancement in total GRG was 0.1702, indicating improvement in the desired responses. As a result of this study, it is clear that appropriate machining parameter selection aids sustainable machining of Al 6063 alloy.

Thermo-metallo-mechanical based phase transformation modeling for high-speed milling of Ti–6Al–4V through stress-strain and temperature effects

The optimization of machining parameters, tool longevity, and surface quality in High-Speed Milling (HSM) of Ti–6Al–4V relies immensely on understanding the local phase transformation. This study endeavors to build a Finite Element (FE) model capable of forecasting phase alterations during the rapid thermal fluctuations intrinsic to Ti–6Al–4V machining. Dynamic phase transformation models were initially introduced to capture rapid heating and cooling phenomena. Using a user-defined subroutine, the phase transitions predictive models were integrated into the HSM simulation within Abaqus/Explicit. Simulation outcomes unveiled phase transitions primarily occurring within the serrated chip and at the tool-workpiece interface. Notably, during rapid heating, when the cutting speed increased to 350 m/min, the β-phase volume fraction surged from 7.5 to 96.38%. A similar trend was observed with feed rate adjustments (i.e., 0.15–0.25 mm/tooth), where β-phase increased from 7.5 to 67.84%. Rapid cooling facilitated the reversion of the transformed β-phase back into the α'-phase. Finally, some advanced characterization techniques were employed to validate the developed thermo-metallo-mechanical coupled FE model for phase transformation. The simulation results verified by the experimental data promotes a better understanding of phase alteration mechanisms and microstructural evolution in HSM of Ti–6Al–4V. The current research is also beneficial for crucial insights into optimizing the machining conditions and their impact on tool-material interactions and surface integrity.

Optimization of Surface Roughness of Aluminium RSA 443 in Diamond Tool Turning

Context—Rapidly solidified aluminium alloy (RSA 443) is increasingly used in the manufacturing of optical mold inserts because of its fine nanostructure, relatively low cost, excellent thermal properties, and high hardness. However, RSA 443 is challenging for single-point diamond machining because the high silicon content mitigates against good surface finishes. Objectives—The objectives were to investigate multiple different ways to optimize the process parameters for optimal surface roughness on diamond-turned aluminium alloy RSA 443. The response surface equation was used as input to three different artificial intelligence tools, namely genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), which were then compared. Results—The surface roughness machinability of RSA443 in single-point diamond turning was primarily determined by cutting speed, and secondly, cutting feed rate, with cutting depth being less important. The optimal conditions for the best surface finish Ra = 14.02 nm were found to be at the maximum rotational speed of 3000 rpm, cutting feed rate of 4.84 mm/min, and depth of cut of 14.52 µm with optimizing error of 3.2%. Regarding optimization techniques, the genetic algorithm performed best, then differential evolution, and finally particle swarm optimization. Originality—The study determines optimal diamond machining parameters for RSA 443, and identifies the superiority of GA above PSO and DE as optimization methods. The principles have the potential to be applied to other materials (e.g., in the RSA family) and machining processes (e.g., turning, milling).

A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models

Due to the manufacturing sector's digitalization and ability to combine quality measurement and production data, machine learning and deep learning for quality assurance hold enormous potential. In this situation, industries may process data to inform data-driven estimates of product quality, thanks to predictive excellence. This research investigates the machinability of Laser Powder Bed Fusion (LPBF) − 316L stainless steel specimens, focusing on the impact of cutting parameters and cooling conditions (Dry, MQL, CO2 and CO2 + MQL) on surface roughness. The research employs advanced data augmentation techniques, incorporating TransGAN and multi-head attention (MHA) based Alexnet model for surface imperfection classification. The results highlight the effectiveness of the proposed methodology in accurately classifying surface conditions and underscore the superior performance of the MHA-Alexnet algorithm compared to alternative models (Alexnet and AE-Alexnet). Overall, the study contributes valuable insights into optimizing machining parameters and cooling strategies for enhanced surface finish in additively manufactured alloys.

An Experimental Study on Nodular Iron Machined Surfaces Utilizing a Capable 2D Finite Element Model for Precise Surface Roughness Estimation

Nodular iron plays a crucial role in various industries, especially in large-scale applications such as gearboxes. Ensuring that nodular iron remains free from oil leakage and that contact surfaces are properly aligned is essential, given its operational requirements. Achieving flat contact faces through precise machining is therefore of utmost importance. As surface roughness and flatness are closely linked, it is vital to investigate the machining process parameters involved. This study focuses on addressing surface quality issues with EN-GJS-600-3 cast iron by optimizing machining parameters. CMM measurements were utilized to analyze the relationship between surface roughness and flatness, with a surface profile used to assess flatness. Furthermore, a new 2D surface roughness estimation method (2D-SRET) was created and tested with experimental data in order to improve the precision of assessing the discrete flat surface machining procedure.

Multi-objective optimization of machining parameters in complete peripheral milling process with variable curvature workpieces

The mechanism of peripheral milling with variable curvature has yet to be revealed. There is an urgent need to develop a composite optimization approach for the full milling process, including rough and fine machining. The influence of curvature on the energy consumption mechanism in peripheral milling is investigated in this paper. Firstly, the geometric parameters of peripheral milling are analyzed, and the material removal rate calculation method with variable curvature is proposed. The effects of workpiece curvature, machining parameters, and tool wear on energy consumption and surface quality are revealed. Secondly, prediction models for machine tool specific energy and surface roughness are developed in accordance with the examined material removal mechanism. Then, the carbon emission calculation method for the complete peripheral milling process is put forward based on the developed energy consumption mechanism. Finally, a genetic algorithm with elite strategy is used to optimize the multi-dimensional and multi-objective machining parameters. The objective is to achieve the maximum machining efficiency, minimize carbon emissions, and attain excellent surface quality during the complete milling process. The research aims to concurrently optimize machining parameters for rough and fine machining processes, thereby enabling effective and environmentally friendly peripheral milling while maintaining machining quality. The determination coefficient R2 for the proposed model exceeds 0.97, while the average accuracy of predictions surpasses 96 %. The research holds particular importance in providing guidance for peripheral milling operations involving workpieces with variable curvature.

Investigation on the optimal ultra-precision machining of nitriding mold steel by in-situ laser assisted diamond cutting

S136 (STAVAX ESR) mold steel is an advanced material for the optical mold industry. The precisely machined surface quality is essential for guaranteeing the excellent performance of molds. Diamond cutting is a high-efficiency machining technique in optical mold manufacturing. However, the chemical wear of diamond tool is a huge challenge in precisely machining steel mold due to the reaction between atoms C and Fe. In this study, the hybrid machining technique which combined the in-situ laser assisted diamond cutting (LADC) and plasma nitriding was used to machine S136 mold steel. Furthermore, the impact of the various machining conditions on the machined workpiece surface roughness Sa was comprehensively investigated by the Taguchi method (TM) and Response surface methodology (RSM). The experimental results of material characterization indicated that a 90 μm nitride layer was generated on the workpiece surface by plasma nitriding. Analysis of variance (ANOVA) results demonstrated that the feed rate and laser power had an important impact on the machined surface roughness Sa. According to the RSM, a precise mathematical model was developed which is explored by experimental investigations. The results showed that the optimal machining parameter combination by RSM generated the better surface quality of 6.393 nm in in-situ LADC of nitriding steel. This study provides a reference for mold steel ultra-precision machining technique and parameter optimization method for surface integrity.