Milling Process Parameters Research Articles

Response surface methodology and process optimization of spark plasma sintered parameters of Ti20Al20Cr5Nb5Ni19Cu12Co19 high entropy alloy

Study was carried out on the effect of milling and sintering process parameters on the relative density and microhardness property of a septenary non-equiatomic Ti20Al20Cr5Nb5Ni19Cu12Co19 high entropy alloy fabricated via spark plasma sintering (SPS) process at 100 °C/min constant heating rate, 5 min dwell time, and at a pressure of 50 MPa. A predictive model was developed through response surface methodology (RSM) using the SPS sintering temperature (ST) and milling time (MT) as the process variables. In order to reduce the number of experimental runs, the design of experiment (DOE) technique was used, to eventually avoid the trial-and-error process associated with conventional experimental procedures. The user-defined design (UDD) of the RSM was used to forecast the optimal operating parameters, and the outcome was validated by experiments. Findings indicate that MT and ST are important in achieving high densification, which leads to high densification and mechanical properties. Optimization model shows that, at MT of 7.20115 h and ST of 899.742 °C, desirable responses of 99.9 % relative density, percentage porosity of 0.0999994 % and a micro-hardness value of 835.21 HV can be attained.

Autoencoder-based inverse design and surrogate-based optimization of an integrated wet granulation manufacturing process

In pharmaceutical manufacturing, integrating model-based design and optimization can be beneficial for accelerating process development. This study explores the utilization of Machine Learning (ML) techniques as a surrogate model for the optimization of a three-unit wet-granulation based flowsheet model for solid dosage form manufacturing. First, a reduced representation of a wet granulation flowsheet model is developed, incorporating a granulation and milling process, along with a novel dissolution model that accounts for the effect of particle size, porosity, and microstructure on dissolution rate. Two optimization approaches are compared, including an autoencoder-based inverse design and a surrogate-based forward optimization. Both methods address the bi-objective problem of maximizing dissolution time and product yield by identifying the optimal granulation and mill process parameters. For this case study, both approaches were effective and incurred a similar computational cost, averaging under 4 s. However, the autoencoder approach offers an advantage through dimensionality reduction, a feature not available in surrogate-based optimization. Dimensional reduction is particularly beneficial for complex process designs with numerous inputs and outputs. The lower dimensional representation helps improve process understanding through enhanced visualization of the process design space and facilitates feasibility studies involving multiple constraints. The autoencoder-based inverse design introduced in this work showcases an implementation of AI and ML in pharmaceutical process development, demonstrating the potential to enhance process efficiency and product quality in complex manufacturing scenarios.

Impact of Toolpath Pitch Distance on Cutting Tool Nose Radius Deviation and Surface Quality of AISI D3 Steel Using Precision Measurement Techniques.

The selection of the right tool path trajectory and the corresponding machining parameters for end milling is a challenge in mold and die industries. Subsequently, the selection of appropriate tool path parameters can reduce overall machining time, improve the surface finish of the workpiece, extend tool life, reduce overall cost, and improve productivity. This work aims to establish the performance of end milling process parameters and the impact of trochoidal toolpath parameters on the surface finish of AISI D3 steel. It especially focuses on the effect of the tool tip nose radius deviation on the surface quality using precision measurement techniques. The experimental design was carried out in a systematic manner using a face-centered central composite design (FCCD) within the framework of response surface methodology (RSM). Twenty different experiment trials were conducted by changing the independent variables, such as cutting speed, feed rate, and trochoidal pitch distance. The main effects and the interactions of these parameters were determined using analysis of variance (ANOVA). The optimal conditions were identified using a multiple objective optimization method based on desirability function analysis (DFA). The developed empirical models showed statistical significance with the best process parameters, which include a feed rate of 0.05 m/tooth, a trochoidal pitch distance of 1.8 mm, and a cutting speed of 78 m/min. Further, as the trochoidal pitch distance increased, variations in the tool tip cutting edge were observed on the machined surface due to peeling off of the coating layer. The flaws on the tool tip, which alter the edge micro-geometry after machining, resulted in up to 33.83% variation in the initial nose radius. Deviations of 4.25% and 5.31% were noted between actual and predicted values of surface roughness and the nose radius, respectively.

Powder mixed micro-electric discharge milling of Ni-rich NiTi SMA: an investigation on machining performance and biocompatibility

ABSTRACT Nickel and titanium (NiTi) based shape memory alloys (SMAs) are novel materials endowed with exceptional super-elasticity, corrosion resistance, and shape memory capabilities, making them well-suited for biomedical and industrial applications. The current study explores the impact of process parameters in powder mixed micro-electrical discharge milling (µ-EDM) on NiTi SMA, focusing on pulse on time (TON), voltage, pulse off time (TOFF), and RPM individually, employing a one factor at a time (OFAT) methodology to analyze surface characteristics, surface roughness (Ra), and material removal rate (MRR). The impact of black phosphorus powder combined with sesame oil (dielectric) on the surface of fabricated channels in commonly used biomaterials was investigated. The cytotoxicity test for biocompatibility was carried out to enhance the cell viability up to 79.89% after machining, which showed enhanced cell viability. The highest MRR the lowest Ra were obtained at the best input factors combination of 24 µs (TON), 6 µs (TOFF), 240 V, and 50 RPM tool rotation. Improved micro-channel quality was observed with lower TON, voltage, and RPM.

Integrated multi-objective optimization of rough and finish cutting parameters in plane milling for sustainable machining considering efficiency, energy, and quality

Plane milling is one of the most prevalent methods in metalworking, recognized for its potential to uncover energy-saving opportunities by optimizing cutting parameters. However, existing research on cutting parameters optimization primarily adopts a phased independent optimization approach, with some studies focusing on rough milling parameters and others on finish milling parameters under predetermined allowances. These research methods lack integrated optimization for both rough and finish milling parameters, leading to suboptimal outcomes. To address this issue, this paper proposes an integrated multi-objective optimization method for both rough and finish cutting parameters in plane milling. The aim is to achieve sustainable machining by considering efficiency, energy consumption, and quality. Specifically, the research accomplishes three key objectives: (1) developing detailed models for the efficiency, energy, and quality of plane milling; (2) presenting an integrated multi-objective optimization model and approach for determining rough and finish cutting parameters in plane milling; (3) conducting a specific case study to validate the effectiveness and feasibility of the proposed model and approach. Experimental findings demonstrated that optimizing cutting parameters in the plane milling process through an integrated approach can reduce processing time by 19.37%, decrease energy consumption by 16.88%, and significantly improve the surface roughness of the workpiece by 27.07% compared to traditional empirical methods. Furthermore, compared to independent optimization schemes, processing time is reduced by 2.99%, energy consumption is lowered by 4.72%, and surface roughness is improved by 13.16%.

Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning

This study begins by conducting side milling experiments on SKD11 using tungsten carbide TiAlN-coated end mills to compare the surface roughness performance between two combinations of milling process parameters (feed rate and radial depth of cut), along with three ultrasonic-assisted methods (rotary, dual-axis, and rotary combined with dual-axis). The results suggest that the rotary (z-axis oscillation) ultrasonic-assisted method may provide better performance. Subsequently, this superior ultrasonic-assisted method was applied both with and without laser locally preheating assistance, respectively. Using a Taguchi orthogonal array, milling process parameters (spindle speed, feed rate, and radial depth of cut) were planned for experiments with the same cutting tool and the workpiece just mentioned above. The surface roughness serves as the objective function while being constrained by cutting-tool life. The characteristics of the smaller-the-better in the Taguchi method were applied to determine the optimal combination of process parameters. Based on the optimal milling process parameters obtained and the superior hybrid-assisted method adopted, milling experiments were repeatedly performed to collect the data on cutting force and cutting-tool wear. Feature engineering was performed on the cutting force signals, and different domain characteristics from both the time and frequency domains were extracted. Hereafter, feature selection by random forest and data standardization were further applied to feature extractions, and the data processing was thus completed. For the processed data, a cutting-tool wear prediction model was constructed by ensemble learning. This method leverages various machine learning regression models, including decision tree, random forest, extremely randomized tree, light gradient boosting machine, extreme gradient boosting, AdaBoost, stochastic gradient descent, support vector regression, linear support vector regression, and multilayer perceptron. After hyper-parameter tuning, the ensemble voting regression prediction was performed based on these ten mentioned models. The experimental results demonstrate that the ensemble voting regression model surpasses the performance of each individual machine learning regression model. In addition, this regression model achieves a coefficient of determination (R2) of 0.94576, a root mean square error (RMSE) of 0.24348, a mean squared error (MSE) of 0.05928, and a mean absolute error (MAE) of 0.18182. Therefore, the ensemble learning approach has been proven to be a feasible and effective method for monitoring cutting-tool wear.

Optimization of Milling Process Parameters for Fe45 Laser-Clad Molded Parts Based on the Nondominated Sorting Genetic Algorithm II

The milling process parameters of laser-clad molded parts have an essential influence on improving the surface quality of the coating. Generally speaking, optimizing a single property often leads to a reduction in another property. In this paper, we systematically investigated a milling process parameter optimization method for Fe45 laser-clad molded parts, and designed L9 (33) sets of orthogonal experiments by taking the spindle speed, feed rate, and cutting depth as input variables, and taking the milling force and material removal rate as optimization indices. The significance ranking of the milling process was analyzed by using the extreme difference method. Then, the multi-objective optimization of the milling process was realized by using the NSGA-II algorithm with the empirical index model as the objective function. The optimum milling parameters obtained were N = 2000 r/min, V = 120.0266 mm/min, and P = 0.45 mm. Finally, the reliability of the optimization results of the algorithm was proved by comparing and verifying the optimal results obtained from the algorithm with the optimal process obtained from the extreme difference analysis. The results provide a theoretical basis for the selection of milling parameters and parameter optimization of laser fusion-coated Fe45 alloys.

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.

BHARAT: A simple and effective multi-criteria decision-making method that does not need fuzzy logic, Part-2: Role in multi- and many-objective optimization problems

A simple and effective multi-attribute decision-making method, named as BHARAT method, is proposed in Part-1 of this paper and the same method is used now as a multi- and many-objective decision-making method for evaluating the Pareto optimal solutions. The proposed BHARAT method is used to identify the best compromise Pareto solution. Based on their importance for the given optimization problem, the objectives are ranked, and the weights are assigned. The weights of the objectives and the normalized values of the objectives for different Pareto optimal solutions are used to compute the total scores. The total scores are used to differentiate the alternative optimal solutions and an alternative solution that gets the highest total score is suggested as the best compromise solution. Three case studies are presented to illustrate and validate the proposed BHARAT method. The case study 1 is a multi-objective optimization problem related to cloud manufacturing with 3 objectives and 20 alternative solutions; case study 2 is a many-objective optimization problem of electro-discharge machining process with 4 objectives and 50 alternative solutions; case study 3 is a many-objective optimization of milling process parameters with 4 objectives and 100 alternative solutions. The outcomes of the suggested BHARAT method are compared with those of the other popular decision-making approaches for each of the three case studies considered. The suggested simple and more logical BHARAT method can be used in multi- and many-objective optimization problems to select the best compromise solution.

Optimization of the cutting process on machining time of ankle foot as transtibial prosthesis components using response surface methodology

The purpose of this research is to determine the best milling process parameters for minimizing machining time of the ankle-foot as a component of transtibial prostheses. The ankle foot prosthesis is of the energy storing type with Al 6061 material. The experimental design employed the Box Behnken technique, with four factors and three levels for each factor, and machining time as a response. The machining parameters assessed in this study are spindle speed, feed rate, step-over, and toolpath technique. The physical trials were carried out using a three-axis CNC milling machine with a flat endmill cutting tool (diameter 5.5 mm, 2 flute with HSS material). The experiment outcomes were assessed using variance analysis, graphs, and numerical approaches. The response surface method (RSM) calculates the mathematical model of the ideal cutting combination parameters based on machining time. Based on the findings, the ideal cutting parameters in the ankle-foot prosthesis production process on a CNC milling machine were spindle speed of 6500 rpm, feed rate of 800 m/min, step over of 0.2 mm, and flowline toolpath strategy. To establish the machining time value of the ankle-foot prosthesis, the ideal circumstances were identified. The findings of this study revealed that CNC milling gave the quickest time for machining transtibial prosthesis components, which can be used to guide the development of machining methods for ankle-foot prostheses constructed of Al 6061 material. The optimal machining time is 424.4601 min.

Retracted: Optimization of CNC End Milling Process Parameters of Low-Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis

Retracted: Optimization of CNC End Milling Process Parameters of Low-Carbon Mold Steel Using Response Surface Methodology and Grey Relational Analysis

(Invited) The Laser-Integrated FIB-SEM for Faster 3D Packages Characterization and Failure Analysis

The semiconductor industry is constantly looking for new technologies and materials to provide more functionality and performance in a smaller volume.Modern innovations in the development of 3D advance packaging require new methods of materials research and failure analysis. Three-dimensional multi-level electronic components, whose packages contain a stack of chips and interconnections, require visualization and analysis of undersurface structures withhigh resolution and performance [2].Modern methods of electron microscopy based on ion beam (FIB-SEM) sample preparation provide a high quality of the cross-sectional surface and accurateinformation about the subsurface structure, however, FIB-SEM does not provide access to deep buried structures and high speed of sample preparation, what is required by the modern electronics industry. A unique solution for cross-sectional access to deeply buried structures is the integration of a laser beam into the FIB-SEM (laser FIB), which provides fast removal of large volumes of material with high speed. Laser FIB provides all advantages of integrates a fs-laser for speed, a Ga+ beam for accuracy, and a SEM for high resolution imaging to enable the fastest workflows. The isolated laser chamber prevents contamination of the electron column and detectors, while ensuring sample integrity with automated transfer between the SEM and laser chamberunder vacuum. Integration of the laser and FIB into a single system, automation of process parameters and laser shuttling and correlative workflows provide the fastest results and highest success rates. Fs-laser ablation is a thermal so the laser affected zone is minimal, and it is often possible to image cross sectional structure immediately after laser ablation, without FIB polishing [1].The latest developments, implemented in the laser-integrated FIB-SEM, such as an automated shuttle, NavCam integration, preset recipes, and the Cross-jet option for cover glass protection, improve the cross-sectioning process and ensure the automation, repeatability, and efficiency of the sample preparation. The combination of batch mode and other parameters of the laser milling process improves the high ablation rate and surface quality, which is especially effective for wide-gap semiconductor cross-sections. A combination of 3D X-ray microscopy (XRM) techniques and integrated laser scanning electron microscopes with a focused ion beam for sample preparation provides a workflow to improve precise targeting for specific analysis of buriedfeatures and correlation with additional equipment to provide subsurface analysis [3]. We describe a workflow using 3D XRM and Laser FIB for precise targeting and high throughput results and demonstrate the application examples. Correlated workflows accelerate results by connecting tools to quickly navigate between multiple length scales and imaging modes.[1] B.Tordoff, C. Hartfield, A. Holwell, S.Hiller, M. Kaestner, S. Kelly, J. Lee, S. Müller, F. Perez-Willard, T.Volkenandt, R. White, T. Rodgers; The LaserFIB: new application opportunities combining a highperformance FIB-SEM with femtosecond laser processing in an integrated second chamber; AppliedMicroscopy 50 (2020), 24.[2] C.Hartfield, W.Harris, A. Gu, M.Terada, V.Viswanathan, L. Jiao and T. Rodgers; Emerging Technologiesfor Advanced 3D Package Characterization to Enable the More-Than-Moore Era; ECS Transactions 109(2022), 15.[3] A. Gu, M. Terada, H. Stegmann, T. Rodgers, C. Fu, Y. Yang; From System to Package to Interconnect:An Artificial Intelligence Powered 3D X-ray Imaging Solution for Semiconductor Package StructuralAnalysis and Correlative Microscopic Failure Analysis; 2022 IEEE International Symposium on thePhysical and Failure Analysis of Integrated Circuits (IPFA) (2022)

Process Parameter Optimization for Hybrid Manufacturing of PLA Components with Improved Surface Quality.

This paper presents a new method of process parameter optimization, adequate for 3D printing of PLA (Polylactic Acid) components. The authors developed a new piece of Hybrid Manufacturing Equipment (HME), suitable for producing complex parts made from a biodegradable thermoplastic polymer, to promote environmental sustainability. Our new HME equipment produces PLA parts by both additive and subtractive techniques, with the aim of obtaining accurate PLA components with good surface quality. A design of experiments has been applied for optimization purposes. The following manufacturing parameters were analyzed: rotation of the spindle, cutting depth, feed rate, layer thickness, nozzle speed, and surface roughness. Linear regression models and neural network models were developed to improve and predict the surface roughness of the manufactured parts. A new test part was designed and manufactured from PLA to validate the new mathematical models, which can now be applied for producing complex parts made from polymer materials. The neural network modeling (NNM) allowed us to obtain much better precision in predicting the final surface roughness (Ra), as compared to the conventional linear regression models (LNM). Based on these modelling methods, the authors developed a practical methodology to optimize the process parameters in order to improve the surface quality of the 3D-printed components and to predict the actual roughness values. The main advantages of the results proposed for hybrid manufacturing using polymer materials like PLA are the optimized process parameters for both 3D printing and milling. A case study has been undertaken by the authors, who designed a specific test part for their new hybrid manufacturing equipment (HME), in order to test the new methodology of optimizing the process parameters, to validate the capability of the new HME. At the same time, this new methodology could be replicated by other researchers and is useful as a guideline on how to optimize the process parameters for newly developed equipment. The innovative approach holds potential for widespread equipment functionality enhancement among other users.

Process-property correlations for spherical composite Al·Ti powders prepared by emulsion-assisted milling

Spherical composite powders desired in many applications can be produced using emulsion-assisted milling. This study is focused on finding correlations between the milling process parameters and characteristics of the produced spherical powders. A composite Al·Ti powder served as the test material system. Spherical powders were prepared using a planetary mill and using an emulsion formed by hexane and acetonitrile as continuous and droplet phases, respectively. Milling conditions including the ratio of immiscible liquids (LR), ball to powder mass ratio (BPR), rotation rate (RPM) and milling time were varied. Optical microscopy images of the powders produced served to recover descriptors of the product particle shapes and sizes. Analysis of the generated image-based data enabled classification of the produced powders, separating spherical and nonspherical particles. This made it possible to recover characteristics of spherical particles, which were correlated with the milling conditions. The quality of the produced spherical powders was assessed using a parameter, Q, maximized when a large fraction of spherical particles with the narrowest size distribution was produced. A correlation was found between Q and a compounded milling process parameter taken as product of grouped parameters: X=BPR∙RPM∙time, Y=BPR∙RPM∙LR, and Z=RPM/BPR, each raised in a separate power, x, y, and z, respectively. The correlation formed a maximum around the conditions leading to formation of the spherical powders with the best quality (highest Q). For the same milling conditions, for which the highest quality spheres were formed, the refinement of the material was maximized, based on the smallest crystallite sizes for both Al and Ti.

Study on Preparation of Homogenized Highly Dispersible AlON Powder

The quality of Aluminum oxynitride (AlON) powder is the main factor affecting the properties of ceramics. To accurately control the nitrogen AlON content and improve the dispersion degree of powder, AlON powder was prepared from γ-Al2O3 and activated carbon powder by carbothermic reduction and nitridation. The uniformity of distribution of raw materials and the dimension and microstructure of AlON powder was controlled by changing the time and rotation rate of the ball milling process. The influences of ball milling process parameters on the microstructure and distribution uniformity of Al2O3/C mixed powder, microstructure, and particle size of AlON powder were studied. The results show that powder agglomeration was effectively reduced, and the uniformity of raw material distribution was increased by controlling the time and rotation rate of the ball milling process. The Al2O3/C mixed powder has good particle size and uniform distribution with roller ball milling for 24 h. After holding the powder at 1700 °C for 60 min, the homogenized AlON powder was obtained, and the residual carbon content was about 0.04 wt%. When the time of planetary ball milling at 250 rpm was 16 h, highly dispersible AlON powder was obtained.

Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission

Research on Optimization of CNC Milling Process Parameters Based on Carbon Emission

Analysis and Optimization of Milling Deformations of TC4 Alloy Thin-Walled Parts Based on Finite Element Simulations

TC4 (DIN3.7164/5) alloy thin-walled parts are widely used in aviation and aerospace industries. However, due to their special structure, shape and poor machinability, large milling forces and milling deformation often occur in the milling process, which cannot guarantee the machining quality and accuracy. The milling processing parameters and milling geometric parameters have a significant impact on the milling force and the deformation, and optimization of the influence factors of milling deformations is important for milling quality. Considering that performing milling experiments under multiple conditions is often costly and time-consuming, this paper provides a finite-element-simulation-based method to study effects of the factors on the forces and deformations during milling thin-walled parts. Firstly, using ABAQUS, a finite element simulation model of the milling process of thin-walled parts is established. Additionally, an orthogonal experimental scheme is designed for optimization of the milling parameters, so as to determine the optimized experimental scheme, and then the optimized experimental scheme is verified to reduce the milling force and deformation by finite element simulations. The optimal parameters for a minimal milling force are a spindle speed of 2000 r/min, a feed rate per tooth of 0.04 mm/z, a milling depth of 1.6 mm, a milling width of 1 mm, a diameter of 6 mm, a rake angle of 20°, a tilt angle of 45°, and two teeth. Similarly, the optimal parameters for minimal node deformations are a spindle speed of 4800 r/min, a feed rate per tooth of 0.18 mm/z, a milling depth of 1 mm, a milling width of 1 mm, a diameter of 16 mm, a rake angle of 20°, a tilt angle of 40°, and four teeth. In addition, this paper uses an optimization algorithm to fit the empirical function with a certain practical value, which can provide a reference for the machining of TC4 titanium alloy. By doing so, we can optimize the milling parameters to obtain the desired machining quality and accuracy, while also saving on time and resources.

Optimization of Machining Parameters of Natural/Glass Fiber with Nanoclay Polymer Composite Using Response Surface Methodology

Machining processes are one of the most important finishing operations in the fabrication of composites, which contain natural fibers. However, it is difficult to attain a better fishing on the final components. Hence, an attempt has been made in the work to achieve a good surface finish in compression-molded hybrid fiber composites containing nanoclay particles by optimizing the milling parameters. Experiments were conducted by using Box–Behnken design (response surface methodology (RSM)) to optimize the milling process parameters such as spindle speed (16, 24, and 32 rpm), feed rate (0.1, 0.2, and 0.3 mm/rev.), and depth of cut (1, 1.5, 2 mm) along with different vol% of nanoclay content (3%, 6%, and 9%). The surface roughness of machined fiber composite was measured, and the most influential parameters were analyzed by analysis of variance, evaluation of signal-to-noise ratio, and mathematical models of responses were developed by RSM. The experimental results (A2B1C4D3) indicated that the feed rate is one of the most significant parameters, followed by nanoclay content, depth of cut, and spindle speed. Surface roughness was found to decrease continuously (2.18–2.08 µm) with increasing nanoclay content (up to 6%) at a certain limit and further addition of clay content (above 6%); the results were declined (2.42 µm) for the same levels of other parameters.

Adaptive elevator kinematics optimization based dual response algorithm for determining proper levels in plaster milling process parameters

This study proposes a novel hybrid approach, called Adaptive/Elevator Kinematics Optimization algorithm based on dual response algorithm (A/EKO-DRA), to enhance the robust parameters estimation and design of the plaster milling process. The A/EKO-DRA method reduces variability while maintaining the desired output target, thereby minimizing the impact of variance on the expected stucco combined water. The performance of the A/EKO-DRA is compared with conventional processes through numerical examples and simulations. The results show that the A/EKO-DRA method has the lowest mean absolute errors among other methods in terms of parameter estimation, and it achieves the response mean of 5.927 percent, which meets the target value of 5.9 percent for industrial enclosures, with much reduction in the response variance. Overall, the A/EKO-DRA method is a promising approach for optimizing the plaster milling process parameters.

Effect of additive and subtractive hybrid manufacturing process on the surface quality of 18Ni300 maraging steel

The effect of process parameters on the surface quality of 18Ni300 maraging steel formed by selective laser melting (SLM) was investigated. Surface modification of SLM specimens was performed using milling as a subtraction method to investigate the effect of milling process parameters on the surface quality of SLM specimens. Comparing and analyzing the surface quality after additive and subtractive processes, the results show that the increase of laser power during SLM can improve the surface morphology, but there is always a balling effect. The surface quality deteriorates when the scanning speed increases. When the laser power increases or the scanning speed decreases, the microhardness increases and the error decreases. The residual stress does not vary linearly with the change of laser power or scanning speed, and the scanning speed has a greater effect on the residual stress than the laser power. The best surface quality was achieved with a laser power of 180 W and a scanning speed of 300 mm s−1. The laser power and scanning speed did not significantly affect the microstructure of the SLM-formed specimens. In the milling process, an increase in the feed rate will make the surface quality worse, and an increase in the cutting speed will make the surface quality better. The best surface quality was obtained with a cutting speed of 10 m min−1 and a feed rate of 36 mm min−1. The grain refinement effect is weakened when the feed rate is increased, and the grain refinement effect is enhanced when the cutting speed is increased. The surface quality of SLM-formed maraging steel specimens improved somewhat after milling.