Machining Parameters Research Articles

Optimization of milling parameters based on GA-BP neural network

Abstract Since the selection of machining parameters usually relies on the experience of workers or traditional calculation formulas, the traditional prediction method has the disadvantages of a complicated arithmetic process, large prediction deviation, and high consumption cost, making it challenging to meet the increasing demand of production and processing. Therefore, this paper proposes a machining quality prediction model based on the GA-BP neural network. Through experiments, it verifies the data-fitting ability of the prediction model and then takes the prediction model as the optimization objective, culminating in a multi-objective optimization model for process parameters based on the NSGA-II algorithm. Experiments demonstrate that the cutting force and surface roughness obtained by the optimization model are 3.6% and 10.0% lower than those obtained by the empirical parameters, respectively, leading to reductions of 3.6% and 10.6%, which verified the optimization effect of the model.

Enhancing machining efficiency in hybrid metal matrix composites through EDM parameter optimization via grey relational analysis

In summary, this study utilized stir casting to develop three distinct aluminium hybrid composites incorporating boron carbide and alumina. These composites were identified as Al7075 + 12%B4C + 6%Al2O3, Al7075 + 6%B4C + 6%Al2O3, and Al7075 + 6%B4C + 12%Al2O3. The novelty of this research lies in the unique combination of materials and their proportions, offering enhanced properties yet unexplored in this specific configuration. The research focused on machining these composite materials using EDM and employed a Taguchi L27 orthogonal array design to plan the machining tests. Key process parameters considered were gap voltage (V), duty cycle (%), current (A), and different percentages of reinforcement content. This aspect adds a significant novel dimension to the study, as the impact of EDM machining parameters on hybrid composites with this particular composition has not been extensively explored previously. The study applied Grey Relational Analysis to evaluate the impact of EDM machining process parameters on critical output responses, specifically material removal rate (MRR) and tool wear rate (TWR). SEM images revealed redeposited molten material, craters, and surface cracks, emphasizing the importance of controlling discharge energy and current levels for desired surface quality. An optimal configuration was identified peak current 2 A, pulse duration 100 μs, duty factor 20%, Composition 2 for improved surface attributes. These qualitative and quantitative insights inform process optimization strategies for EDM machining of aluminium hybrid composites.

Process parameters optimization of EDM for hybrid aluminum MMC using hybrid optimization technique

This study explores how machining parameters affect Surface Roughness (SR), Tool Wear Rate (TWR), and Material Removal Rate (MRR) during Electrical Discharge Machining (EDM) of a hybrid aluminum metal matrix composite (AMMC). The composite includes 6 % Silicon carbide (SiC) and 6 % Boron carbide (B4C) in an Aluminum 7075 (Al7075) matrix. A combined optimization approach was used to balance these factors, evaluating Pulse ON time, Current, Voltage, and Pulse OFF time. Response Surface Methodology (RSM) optimized single responses, while multi-response optimization employed a hybrid method combining the Entropy Weight Method (EWM), Taguchi approach, TOPSIS, and GRA. Analysis of Variance (ANOVA) assessed parameter significance, revealing substantial impacts on SR, MRR, and EWR. Based on TOPSIS and GRA, optimized parameters achieved a desirable balance: high MRR (0.4172, 0.5240 mm³/min), minimal EWR (0.0068, 0.0103 mm³/min), and acceptable SR (10.3877, 9.1924 μm) based on EWM-weighted priorities. Confirmation experiments validated a 15 % improvement in the closeness coefficient, and a 16 % improvement in the Grey relational grade, which considers combined SR, MRR, and EWR performance. Scanning Electron Microscope (SEM) analysis of surfaces machined with optimal parameters showed minimal debris, cracks, and no recast layer, indicating high surface integrity. This research enhances EDM optimization for AMMC, achieving efficiency in machining, minimizing tool wear, and meeting surface quality requirements.

Effects of current intensities on material removal rate, electrode wear rate, and surface roughness of machining high carbon high chromium steel

AbstractThe electrical discharge machining is very successful and generally recognized for producing complicated shapes and small openings with exceptional precision. The objective of this study is to assess the impact of different electrical discharge machining parameters on the attributes of the machining process. The evaluation of the machining process was conducted based on the workpiece‘s material removal rate, the rate at which the electrodes wear, and the level of surface roughness. Simultaneously achieving a high material removal rate, low electrode wear rate, and high surface roughness is not possible with a specific combination of approaches due to their contradicting nature. Frequently, it is necessary to independently apply many measures instantaneously in order to assess their impact on various responses. This research investigates the machining of high carbon high chromium steel making use of electrical discharge machining with aluminum, copper, and graphite electrodes. The study focuses on the impact of different current intensities on the rate of material removal, electrode wear, and surface roughness. The experimental results demonstrate that the highest material removal rate (mmcubicmin−1) is achieved at a current density of 12 A when using a copper electrode (54.67), as opposed to aluminum electrode (22.91) and graphite electrode (29.43).

Bushing fault diagnosis based on SVM and the improved sparrow search algorithm

Abstract In order to address the issue of low precision in traditional bushing fault diagnosis, a bushing fault diagnosis method based on an improved sparrow search algorithm (ISSA) and support vector machine (SVM) is proposed in this paper. Firstly, the bushing vibration signals are extracted by wavelet packet, and the feature vectors are used as inputs for the SVM. In view of the impact of support vector machine parameters on the model, a sparrow search algorithm is proposed for intelligent optimization. To prevent reaching a local optimum, adaptive inertia weight is added based on the original approach. The final bushing fault diagnosis model is established by training. Comparison experiments with three fault diagnosis models, SSA-SVM, PSO-SVM, and SVM, found that the proposed method achieves complete diagnosis in a shorter time, and the diagnostic accuracy rate is 96.5%, which verifies the feasibility and effectiveness of the model.

DEVELOPMENT AND PERFORMANCE EVALUATION OF A MODEL CUTTING FLUID DEVELOPED FROM SHEA BUTTER OIL

This research was carried out to develop a model cutting fluid from Shea Butter oil and evaluate its performance in relation to temperature and surface roughness when compared to a commercial cutting fluid. The effectiveness of the cutting fluid was evaluated on a mild steel using a turning operation on the lathe machine. Temperature variations during the turning operation were measured using an infra-red gun thermometer while surface roughness was evaluated by employing a surface roughness tester. In this work Taguchi method was used to guide the selection of machine parameters such as cutting speed (rev/min), feed rate (mm/rev), and depth of cut (mm). Using Minitab, the cutting temperature and resulting surface roughness were measured for both the developed cutting and commercial fluids. The results obtained for the average cutting temperature of ~ 30oC and overall surface roughness value of 0.32μm for shea butter oil show a significant difference from that of ~ 35oC and 0.47 μm respectively for commercial grade cutting fluids. These results show that the shea butter oil-based cutting fluid not only exhibits great potential but also surpasses the current capabilities of commercial cutting fluids in terms of its cooling and surface finishing performances.

A novel combination prediction model of ultra-short-term wind speed

The accurate prediction of ultra-short-term wind speed has important theoretical significance and practical application value. In this paper, a combination prediction model of ultra-short-term wind speed based on variational mode decomposition is proposed. Firstly, the variational mode decomposition algorithm is introduced to decompose the ultra-short-term wind speed and obtain the components of different frequency. The approximate entropy algorithm is used to calculate the complexity of each component. According to the calculation results, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is used to predict low complexity components. Then, the predicted values of each component are added to get the final prediction result. Gray wolf algorithm is used to optimize the model parameters of support vector machine and echo state network. In addition, the approximate entropy calculation results show that compared with the original ultra-short-term wind speed, the complexity of the components obtained by the variational mode decomposition algorithm is reduced, which makes the modeling of the ultra-short-term wind speed system simpler. Finally, the validity of the model is verified by taking the ultra-short-term wind speed data of 5 minutes and 10 minutes sampling period actually collected as the research object. The results show that the prediction model proposed in this paper has better performance than other single or combination prediction models.

Tool Wear in GGG50 Cast Iron Milling Environments

In the present study, the impression of manufacturing parameters on cutting tool tip wear in the milling operation of GGG50 cast iron material with carbide coated cutting tool inserts was investigated. Taguchi orthogonal L18 experimental sequence was applied as the experimental design. As processing parameters; cutting speed, coolant and feed rate were chosen. In the test results, the amount of wear on the cutting tool tips was examined. Optimum processing multiparameters were determined by the Taguchi. Analysis of variance (ANOVA) was used to analyses the effect of input parameters on the cutting tool tip. Consequently, it has been determined that the wear is high in the working environment where the coolant is open and the cutting speed is high. In order to keep the cutting tool tip wear at a minimum level, the most suitable machining parameters are; coolant = closed, cutting speed = 160 mm/min, feed rate = 0.3 mm/rev. It was determined that the tip feed rate had little impression on tool wear.

Improving the performance of deep learning models in predicting and classifying gamma passing rates with discriminative features and a class balancing technique: a retrospective cohort study

BackgroundThe purpose of this study was to improve the deep learning (DL) model performance in predicting and classifying IMRT gamma passing rate (GPR) by using input features related to machine parameters and a class balancing technique.MethodsA total of 2348 fields from 204 IMRT plans for patients with nasopharyngeal carcinoma were retrospectively collected to form a dataset. Input feature maps, including fluence, leaf gap, leaf speed of both banks, and corresponding errors, were constructed from the dynamic log files. The SHAP framework was employed to compute the impact of each feature on the model output for recursive feature elimination. A series of UNet++ based models were trained on the obtained eight feature sets with three fine-tuning methods including the standard mean squared error (MSE) loss, a re-sampling technique, and a proposed weighted MSE loss (WMSE). Differences in mean absolute error, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between the different models.ResultsThe models trained with feature sets including leaf speed and leaf gap features predicted GPR for failed fields more accurately than the other models (F(7, 147) = 5.378, p < 0.001). The WMSE loss had the highest accuracy in predicting GPR for failed fields among the three fine-tuning methods (F(2, 42) = 14.149, p < 0.001), while an opposite trend was observed in predicting GPR for passed fields (F(2, 730) = 9.907, p < 0.001). The WMSE_FS5 model achieved a superior AUC (0.92) and more balanced sensitivity (0.77) and specificity (0.89) compared to the other models.ConclusionsMachine parameters can provide discriminative input features for GPR prediction in DL. The novel weighted loss function demonstrates the ability to balance the prediction and classification accuracy between the passed and failed fields. The proposed approach is able to improve the DL model performance in predicting and classifying GPR, and can potentially be integrated into the plan optimization process to generate higher deliverability plans.Trial registration: This clinical trial was registered in the Chinese Clinical Trial Registry on March 26th, 2020 (registration number: ChiCTR2000031276). https://clinicaltrials.gov/ct2/show/ChiCTR2000031276

Performance Evaluation, Machine Parameters and Ergonomic Aspects of Palm Fruit Harvesters

Performance Evaluation, Machine Parameters and Ergonomic Aspects of Palm Fruit Harvesters

Bubble behavior of single-pulse discharge in EDM

The removal of machining debris in the narrow discharge gap primarily relies on high pressure bubble expansion and fluid agitation induced by the bubble pulsation in electrical discharge machining (EDM). Observing bubble behavior and elucidating the mechanism of bubble behavior is an increasing concern in EDM. The purpose of this study is to investigate the bubble behavior of single pulse discharge in EDM. Using a high-speed camera, this study observed the bubble evolution and measured the discharge duration dependent bubble characteristics. The results show that the bubble experiences rapid expansion, contraction, primary pulsation, intense pulsation in the post-discharge process, and subsequent bubble energy dissipation behavior. Furthermore, the arc plasma persists for a duration even after the discharge ends in positive polarity machining. The findings suggest that bubble behavior in single-pulse discharges is primarily dominated by the discharge energy, with the bubble behavior during the discharge significantly influenced by the jumping and oscillation of the arc plasma. Our research may introduce new ideas for improving the depth-to-diameter ratio of deep micro-holes in EDM and provide fresh perspectives on the mechanism analysis of how machining parameters affect machining performance. For instance, by synchronizing the pulse intervals with the bubble stabilization time, bubble coalescence can be reduced, thus increasing bubble removal in the small machining gap. The stability of deep micro-hole EDM may depend on bubble behavior, which is determined by the electrical parameters.

Experimental and statistical damage analysis in milling of S2‐glass fiber/epoxy and basalt fiber/epoxy composites

AbstractS2‐glass fiber reinforced plastics (S2‐GFRP) and basalt fiber reinforced plastics (BFRP) have emerged as crucial materials due to their exceptional mechanical properties, and milling of composite materials plays an important role in achieving desired properties. However, they have proven challenges due to relative inhomogeneity compared with metals, resulting unpredictability in quality of milling operations. The objective of this work is to investigate the effect of cutting parameters, tool geometry and tool surface materials on the surface quality of composites using burrs as a metric. S2‐GFRP and BFRP composites were produced by the vacuum infusion method. Helical and straight flute end mills were manufactured from high‐speed steel (HSS) and carbide rounds, and half of them were coated with titanium nitride using reactive magnetron sputtering technique. Taguchi L18 orthogonal array is used to determine the effect of tool material, tool angle, coating, cutting direction, spindle speed, and feed rate on the machining quality of S2‐GFRPs and BFRPs with respect to burr formations. Milling experiments were conducted under dry conditions and then the burrs were imaged to calculate the total area and length. Statistical analysis was also performed to optimize the machining parameters and tool type for ensuring the structural integrity and performance of the final composite parts. The results showed that the selection of tool material has the most significant impact on the burr area and length of the machined surface. The novel image analysis allows to analyze the extent of the burr size with a desirable operation speed for industrial applications.Highlights Aerospace grade S2‐Glass (S2‐GFRP) and basalt fiber reinforced plastics (BFRP) were manufactured. Carbide and HSS end mills were fabricated and coated with titanium nitride protective layer. FRPs were machined at various process parameters designed by Taguchi method. Distinctive image processing was firstly used to compute milling induced Burr area and length. Statistical analysis was performed to quantify the contribution of parameters and optimize milling.

Machinability of Ti6Al4V Alloy: Tackling Challenges in Milling Operations

This study investigates strategies for improving the 3D milling of Titanium Alloy Grade 5 (Ti6Al4V) by optimizing machining parameters and cutting tool engagement techniques. Ti6Al4V presents significant machining challenges due to its low machinability index (20%), which directly impacts manufacturing efficiency. High temperatures during machining, often exceeding 8820C, lead to phase transformations, creating a harder Beta lamellar equiaxed microstructure. This, coupled with the alloy's poor thermal conductivity, results in heat concentration at the cutting tool interface, accelerating thermo-chemical wear and potentially catastrophic tool failure. This study explores how controlled cooling methods, coupled with appropriate lubrication, can effectively dissipate heat and flush away chips, mitigating the detrimental effects of high temperatures. Furthermore, the selection of cutting tool materials and coatings with high thermal conductivity and chemical inertness, along with aggressive rake angles and higher relief angles, are examined as methods to improve shearing, minimize smearing, and enhance surface quality. By optimizing these parameters, this study aims to provide manufacturers with practical strategies to overcome the challenges of Ti6Al4V machining, ultimately increasing tool life and overall milling efficiency.

Multidimensional ultrasonic vibration machining modeling of SiCp/Al cutting forces with different volume fractions: Experiments and numerical simulations

The cutting properties of the particles of aluminum-based silicon carbide (SiCp/Al) and the base material are so disparate that it is challenging to ensure the surface quality of machining. The cutting force significantly influences the quality of the machined surface. Therefore, real-time cutting force prediction is paramount for ensuring the workpiece's desired surface quality. This paper presents a 3D ultrasonic milling force model, established from four aspects: cutting formation force, extrusion friction force, SiC particle crushing force, and ultrasonic impact force. The model is based on the ultrasonic action of the cutting process and the unique removal characteristics of the material. A 3D ultrasonic milling system was constructed to experimentally verify the milling force model. The effects of each parameter on the milling force were analyzed through orthogonal experiments, which resulted in the effects of depth of cut, rotational speed, volume fraction, ultrasonic amplitude, and feed rate. The ultrasonic amplitude, feed rate, and other factors were found to influence the milling force to varying degrees. The milling force was observed to be 42.47 %, 22.37 %, 10.85 %, 12.67 %, and 11.64 % affected by the factors mentioned above, respectively. The single-factor experiment was conducted to analyze the cutting force at different machining parameters. The experimental results and the MATLAB numerical simulation results exhibited a similar trend of change. The maximum absolute error between the two was 12.71 %, with an average absolute error of 3.735 %.

An enhanced proportional resonance controller design for the PMSM based electric vehicle drive system

Permanent magnet synchronous machine (PMSM) has proven to be a more economical traction drive system for electric vehicle (EV) applications owing to increased efficiency and high-power density. However, the drive system requires more efficient control schemes to deliver better dynamic performance irrespective of dynamic changes in the motor speed, machine parameters and disturbances. Hence, to tackle the dynamic changes, to enhance the wider operating speed, to achieve precise speed tracking capability, and improved efficiency, a novel control algorithm for the PMSM based EV is proposed in this paper. The control algorithm is implemented by adopting the merits of conventional proportional resonance (PR) and proportional integral (PI) controller. The proposed control strategy is designed with an outer PI speed regulator and the inner enhanced PR (EPR) current regulator. The uniqueness of the proposed EPR controller is that the controller is designed to damp the torsional mode oscillation owing to dynamic changes such as speed and torque regulation evading the additional control loop. The effectiveness of the control scheme is tested in MATLAB Simulink and hardware-in-loop (HIL) real time simulator RT5700. To validate the effectiveness of the proposed control scheme the results are compared with the conventional control schemes. The results presented show that the proposed control technique successfully enhances the static and dynamic performance, and resilience of the EV system. Also, the proposed scheme significantly reduces the flux ripples, torque ripples, current jitter, peak overshoot, undershoot compared to the conventional current controllers.

Numerical investigation on milling performance and damage response of UD-CFRP laminates

Carbon fiber-reinforced polymer (CFRP) is prone to machining defects, including burrs and tearing, during milling. These defects are closely associated with interface delamination while milling force plays a crucial role in their occurrence. In this paper, a nonlinear explicit finite element (FE) model is developed to predict the milling force and analyze the delamination damage occurring during the milling of unidirectional CFRP (UD-CFRP). The proposed FE model is validated through numerical comparison with experimental data for milling force. Moreover, a detailed study is conducted to examine the influences of spindle speed, feed rate and depth-of-cut on the side-milling performance and damage response of UD-CFRP. This study provides valuable insights into the effects of machining parameters on milling force and delamination damage in CFRP, thereby supporting the optimization of machining technology for this composite material system.

Intelligent method framework for 3D surface manufacturing in cloud‐edge collaboration architecture

AbstractLarge and complex workpieces are core components in fields, such as aerospace, shipbuilding, and other industrial applications. However, the main challenge of curved plate processing comes from the difficulty in determining the nonlinear rebound features with structural design parameters. An intelligent method framework is proposed for 3D surface manufacturing in cloud‐edge collaboration environment. With the construction of an intelligent generation method for machining parameters, a unified data model is effectively integrated with various discrete data, and an intelligent processing mechanism based on 3D point clouds is constructed. In particular, a prediction method for curved panel rebound is constructed to reduce the manual dependency of the manufacturing process. Finally, a related case study is conducted to verify the framework, and the result shows accuracy, interpretability and reusability advantages over other similar methods.

Analysis and optimization of machining parameters of AZ91 alloy nanocomposite with the Influences of nano ZrO2 through vacuum diecast process

The magnesium alloy composite is a vital material for automotive applications due to its features like high stiffness, superior damping resistance, high strength, and lightweight. Here, the motto of research is to establish the AZ91 alloy nanocomposite with the exposures of 0, 1, 3, and 5 volume percentages (vol%) of nano zirconium dioxide (ZrO2) particles (50nm) through fluid stir metallurgy route associated with 1x105 Pa vacuum die cast process. Exposures on structural morphology, hardness, and impact toughness of composite are analyzed and identified as the nano AZ91 alloy composite enclosed with 5vol% is homogenous particle dispersion, enhanced hardness (97.6HV), and optimum toughness of 21.2J/mm2. However, composite faces machining difficulties due to the hard abrasive particles with higher hardness, resulting in tool wear. This experiment predicts the optimum mill parameters during the end mill operation of magnesium alloy nanocomposite (AZ91/5vol%) by using a tungsten carbide coated end mill cutter to attain the maximum metal removal rate with low surface roughness and tool wear analyzed via the general linear model (GLM) ANOVA approach. The input conditions for end milling operation vary, like feed rate (0.1 -0.4mm/rev), depth of cut (0.05 -0.2mm), and spindle speed (250-1000rpm). During the ANOVA GLM approach, the L16 design experiment is fixed for further interaction analysis. The results predicted by the depth to cut and feed rate were dominant and played a major role in deciding the tool wear, surface roughness, and MRR.

Robotical Automation in CNC Machine Tools: A Review

Abstract Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

Advancing Dental Implant Design: A Comprehensive Study of Machining Parameters, Corrosion Behavior, and Microstructural Evaluation.

In this research, we investigate the impact of varying machining parameters [depth of cutting (mm) and spindle rotation speed (rpm)] on the microstructure and electrochemical behavior of Ti6Al4V-ELI dental implants. This comprehensive study employs an approach, leveraging potentiodynamic methods and electrochemical impedance spectroscopy, to analyze corrosion behavior in a phosphate-buffered saline solution. To further deepen our understanding of corrosion kinetics, we used an alternating current circuit model, based on a simple Randles equivalent circuit. This model elucidates the corrosion interface interactions of the Ti6Al4-V-ELI alloy implant within the PBS solution. In addition, our research delves into the microstructural implications of different machining parameters, utilizing scanning electron microscopy and X-ray diffraction (XRD) techniques to reveal significant phase changes. The changes in texture were examined qualitatively by comparing the intensities of the peaks of the XRD pattern. A detailed correlation analysis further links the machining parameters with the corrosion properties of dental implants, offering a comprehensive perspective rarely explored in the existing literature. The results obtained for the three samples showed that the corrosion resistance would be higher by increasing the machining depth and the spindle rotation and that the corrosion current would be lower. As a result, a lower corrosion rate was obtained. Finally, experimental results from electrochemical analyses are compared and discussed.