Optimization Of Machining Parameters Research Articles

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.

A Comparative and Experimental Study of Parametric Influence on Drilling Process of Hybrid AMMC

Abstract: To aim for the highest quality product quality and a high metal removal rate, metal cutting is crucial. The primary obstacle to attaining optimal quality and high production is the short tool life. The hybrid aluminum metal matrix AA 6082, with reinforcement consisting of titanium dioxide (7.5%) and cenosphere (2.5%), was optimized using the Modified Taguchi optimization method for simultaneous minimization and maximization of surface roughness (Ra), machining time, and material removal rate. The surface finish was affected by different types of geometrical precision values of the CNC drilling process parameters in order to both minimize and maximize the responses. The experiments, including call for the machining of hybrid AMMC and the use of an HSS drill bit, will be performed using a CNC with different process parameters. The L16 orthogonal array will be used for the examinations, and each experiment will be run under a distinct set of circumstances, including feed, speed, and pecking. Using the TAGUCHI design, the optimal geometrical and machining parameters were determined from the experimental output responses, and significant influences were discovered for the corresponding specific output responses.

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.

Detection of electrolyte supply state in the electrochemical discharge machining of micro hole based on deep learning

Electrochemical discharge machining (ECDM) is an effective technique for machining hard and brittle insulating materials due to its characteristics that the occurrence of electrochemical discharging is independent of the electrical conductivity of the workpiece. In ECDM, the machining performance is significantly influenced by the real-time machining state. However, the real-time machining state still cannot be effectively detected and fed back until now. To improve the performance of ECDM and establish a useful closed-loop control strategy, it is essential to find a new signal that characterizes the machining state. Electrolyte supply is critical for ECDM and can be used as a closed-loop control factor. In this paper, a novel method for detecting the electrolyte supply state in the machining zone based on current signals was proposed. To achieve the method, a R-BiLSTM-A (ResNet-BiLSTM-Attention) model was designed for electrolyte supply state detection. The designed model was demonstrated to perform better than the other general artificial intelligence models. The relationship between the contact force and the electrolyte supply state detection results was investigated. It was found that the electrolyte supply state was insufficient when the tool-workpiece contact occurred. This state was successfully detected by the model, which verified the effectiveness of the designed model. The proposed model was then applied to detect the electrolyte supply state under different machining conditions. The tool electrode feed rate and electrode retreat parameters were optimized based on the detection results of the proposed model. The hole depths were improved by 126.50 % and 301.69 % with optimized machining parameters at the voltage of 39 V and 43 V, respectively.

Experimental Investigation on Surface Characteristics of Finish Hobbed Gear using Grey Relational Analysis

Introduction: Spur gears are important for transferring motion and power among shafts. The surface characteristics of these gears are important for their life and smooth operation. To improve profitability and customer satisfaction, a study was conducted to analyze the surface quality of spur gears produced through CNC hobbing machining. Objectives: The objective this investigation is to conduct experimental trials to estimate the best hobbing parameters to minimize surface roughness and microgeometric deviations. In this investigation, effect of varying cutting speed and feed rate on surface finish have been reported. Methods: An experiment are conducted according to Taguchi L9 experimental design. Grey Relational Analysis is performed to estimate the optimum machining parameters. Results: The experimental finding reveals, at cutting speed 400 rpm, the feed rate 8 mm/min, and depth of cut 3.91 mm gives good quality of surface (Ra 0.5530). The gear hobbing process has increased to a greater level of performance. The roughness of the flank surface and microgeometry deviations decreases with an increase in hob cutter speed. Conclusions: This ultimately reduces the inherent vibrations between the hob cutter and the gear blank, thus minimizing microgeometric deviations. The variation in feed rate was noticed and had a very significant impact on the efficiency and surface quality of the gear. The study recommends that a cutting speed of 400 rpm is the most effective for obtaining the best results.

MULTI-OBJECTIVE OPTIMIZATION OF MACHINING PARAMETERS FOR EDM OF MAGNESIUM ALLOY- ZE41 USING MARCOS ALGORITHM AND DIFFERENT WEIGHING METHODS: SD, CRITIC AND MEREC

This paper presents multi-objective optimization of machining parameters, i.e., electrode materials, peak current, pulse on time and pulse off time in EDM of magnesium alloy-ZE41. The objectives such as metal removal rate, tool wear rate, surface roughness, recast layer thickness and radial overcut were optimized using MARCOS algorithm and three objective weighing methods, i.e., SD, CRITIC and MEREC. Taguchi’s L27(34) orthogonal array was used for conduction of experiments. ANOVA results showed that electrode material, interaction of electrode material and peak current and pulse on time were the most significant parameters for all weighing methods. Comparative study indicated that the percentage improvement in performance at optimal parameter setting were 37.21%, 32.30% and 10.68% for SD, CRITIC and MEREC respectively. Confirmation results indicated that all objectives except tool wear rate were improved positively for SD and CRITIC, but all objectives except metal removal rate deteriorated for MEREC.

Research on machining Technology of Electrospark-electrochemical Hybrid Energy Field with tungsten hole

The machining technology for tungsten hole faces considerable challenges. Electrical Discharge Machining (EDM), as a non-traditional machining technique, is widely applied due to its capability to process conductive materials. This technique is characterized by high precision metal processing, high material removal rate, and low tool wear rate. Molecular dynamics simulation technology is employed in this study to model the single pulse discharge process, with the aim of elucidating the erosion mechanism of different melting point metal materials in the melt pool during spark discharge and the effect of pressure difference inside and outside the melt pool. Moreover, for the efficient machining of tungsten materials, Electrospark-electrochemical Hybrid Energy Field Machining (ECEM) method is proposed, which is conducted under a certain conductivity salt solution. The simultaneous high-speed electric spark discharge and electrochemical action effectively promote the rapid formation of spark plasma discharge channels. Orthogonal experimental data and images are utilized in this paper to study the machining of tungsten micropores, with the effects of pulse width, pulse interval, peak current, and concentration of NaNO3 being analyzed, and the optimal machining parameters being determined.

Effect of Cutting Speed and Feed per Tooth on the Trimmed Surface Roughness and Tool Wear During Milling of CFRP: Aerostructural Part

The machining quality plays a pivotal role in determining the service life of components. In this context, the primary focus of the experimental investigation is centered on the impact of cutting conditions during edge trimming of carbon fiber reinforced plastics (CFRP) material. The ultimate objective of this experiment is to pinpoint the most effective machining parameters, specifically the cutting speed, Vc and the feed per tooth, fz, to minimize damages during the edge trimming process using actual aerostructural sample components composed of CFRP material. Furthermore, the study took into account the issue of tool wear on cutting tools. The Design of Experiment (DoE) technique, specifically the Taguchi L9 orthogonal arrays method, was used to conduct this research. It was shown that the feed per tooth, fz, and the cutting speed, Vc, had the biggest effects on the trimmed surface quality as the longitudinal surface roughness increased. It was demonstrated that the fracture of the pyramidal tooltips in router-type tools happened more frequently at higher cutting speeds, Vc. The optimal machining parameters for minimizing damages during CFRP edge trimming were the combination of the feed per tooth, fz at 0.05 mm and cutting speed, Vc at 50 m/min. The most significant element influencing the tool’s wear was the cutting speed, Vc.

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.

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.

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.

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