Optimization Of Machining Parameters Research Articles

Automated VMAT treatment planning using sequential convex programming: algorithm development and clinical implementation

Objective. To develop and clinically implement a fully automated treatment planning system (TPS) for volumetric modulated arc therapy (VMAT). Approach. We solve two constrained optimization problems sequentially. The tumor coverage is maximized at the first step while respecting all maximum/mean dose clinical criteria. The second step further reduces the dose at the surrounding organs-at-risk as much as possible. Our algorithm optimizes the machine parameters (leaf positions and monitor units) directly and the resulting mathematical non-convexity is handled using the sequential convex programming by solving a series of convex approximation problems. We directly integrate two novel convex surrogate metrics to improve plan delivery efficiency and reduce plan complexity by promoting aperture shape regularity and neighboring aperture similarity. The entire workflow is automated using the Eclipse TPS application program interface scripting and provided to users as a plug-in, requiring the users to solely provide the contours and their preferred arcs. Our program provides the optimal machine parameters and does not utilize the Eclipse optimization engine, however, it utilizes the Eclipse final dose calculation engine. We have tested our program on 60 patients of different disease sites and prescriptions for stereotactic body radiotherapy (paraspinal (24 Gy × 1, 9 Gy × 3), oligometastis (9 Gy × 3), lung (18 Gy × 3, 12 Gy × 4)) and retrospectively compared the automated plans with the manual plans used for treatment. The program is currently deployed in our clinic and being used in our daily clinical routine to treat patients. Main results. The automated plans found dosimetrically comparable or superior to the manual plans. For paraspinal (24 Gy × 1), the automated plans especially improved tumor coverage (the average PTV (Planning Target Volume) 95% from 96% to 98% and CTV100% from 95% to 97%) and homogeneity (the average PTV maximum dose from 120% to 116%). For other sites/prescriptions, the automated plans especially improved the duty cycle (23%–39.4%). Significance. This work proposes a fully automated approach to the mathematically challenging VMAT problem. It also shows how the capabilities of the existing (Food and Drug Administration)FDA-approved commercial TPS can be enhanced using an in-house developed optimization algorithm that completely replaces the TPS optimization engine. The code and pertained models along with a sample dataset will be released on our ECHO-VMAT GitHub (https://github.com/PortPy-Project/ECHO-VMAT).

Modeling and optimization of WEDM of monel 400 alloy using ANFIS and snake optimizer: A comparative study

The exploration of the prediction of wired electro-discharge machining parameters is becoming progressively more crucial in order to choose the ideal parameters for making products with impeccable designs. The objective of this work is to develop an augmented adaptive neuro-fuzzy inference system (ANFIS) model by encrypting the snake optimizer for estimating the surface roughness, material removal rate, and residual stress of Monel 400 alloy in wire electric discharge machining (WEDM). The performance of the indicated augmented model called ANFIS-SO was evaluated using 64 trials of WEDM experimental data. As a means of emphasizing the performance of the presented model, estimation results were compared to those from existing models, which included integration of ANFIS and beetle antennae search capabilities, reptile search algorithms, and COOT algorithms. The ANFIS-SO model showed excellent performance based on the statistical benchmarks tested for the developed models. The model achieved RMSE of 0.0793, 0.2781, and 16.3286 with performance errors of 0.3203, 1.9687, and 1112.529 along with an accuracy of 99%, 98%, and 95% for surface roughness, MRR, and residual stress, respectively, which are extremely more accurate than the ANFIS, ANFIS-BAS, ANFIS-RSA, and ANFIS-COOT. As a result of these findings, it is evident that the developed ANFIS-SO model might be used with reliability when designing future model forecasting approaches for optimizing machining parameters. Therefore, it is discovered that using ANFIS-SO is an innovative method for determining WEDM machining parameters which are very promising.

Measurement and analysis of cutting forces using dynamometer in turning of EN-8 steel

This experimental study determines the cutting forces and optimum machining parameters identified in the turning operation on the lathe machine. The advantage of the present experiments is to improve the machining performance by high material removal rate and minimum tool bear, surface roughness and power. These experiments use EN8 steel workpieces and carbide as a cutting tool. Machining input parameters are speed of spindle (rpm), Feed (mm/rev) and depth of cut (mm). A triaxial dynamometer has been used to measure the thrust, feed, and cutting forces by the adaptive measurement of orthogonal components of cutting force acting on the workpiece. The digital data is transferred to the computer with LAB-VIEW software to compute the forces acting on the EN8 steel workpiece. The LAB-VIEW software displays the real-time cutting forces acting on the workpiece by the data acquisition program. Taguchi method is evaluated on output parameters of machining and the ANOVA method illustrates the error percentage from experimental values to predicted values. Finally, the range of inputs, the effect of input parameters and various cutting forces such as feed force, thrust force and resultant force have been analyzed.

Study on surface quality and mechanical properties of micro-milling WE43 magnesium alloy cardiovascular stent

Traditional laser processing technique is mainly applied for cardiovascular stent manufacture, especially for materials with high mechanical properties and high melting point, while it usually produces problems such as material ablation and defects during finish machining process. Magnesium alloy, as material candidate for bioresorbable cardiovascular stent, has low melting and ignition point, high oxidizability property. Laser processing technique scarcely meets the requirements of high surface quality of magnesium alloy cardiovascular stents, this paper firstly develops a five-axis micro-milling technique to fabricate magnesium alloy cardiovascular stents. Milling orthogonal test was carried out to obtain the optimal finish machining parameters, and then a novel stent machining process was established. The processed stent has an inner diameter of 1.5 mm, a thickness of 80 μm and a length of 8 mm with a U-bend width of 100 μm and a link strut width of 70 μm, and the optimal finish processing parameters was set up with the main spindle speed of 30,000 rpm, feed speed 200 mm/min, cutting width 50 μm and cutting depth 40 μm. The dimensional accuracy and surface topography of the magnesium alloy stent were measured and characterized, and results showed that the dimensional accuracy was within 0.01 mm, and the machined stent achieved better surface quality without obvious processing defects such as pits, grooves and pores while comparing with the stent under original processing parameters. In order to evaluate the radial support performance of the stent, the radial compression and expansion experiments and simulations were conducted, respectively. Results showed that the maximum diameter error in the experiment and simulation was 13.35 %, and the radial recoiling error was 16.79 %. This work provides a potential stent manufacture technique in the development and clinical application of bioresorbable magnesium alloy stent.

Performance assessment of some selected vegetable oils as lubricants in turning of AISI 1045 steel using a Taguchi-based grey relational analysis approach

ABSTRACT This study is focused on the performance evaluation of three cutting lubricants, which are neem, palm kernel and mineral oil in the turning of AISI 1045 with MQL. Optimization of machining parameters with multi-response signals using Taguchi-based grey relational analysis was done. For experimental design, tests were developed using Taguchi's L9 orthogonal array and the signal-to-noise ratio was obtained using the smaller-the-better approach to achieve the optimal combination. Performance indicators including surface roughness and cutting temperature were measured throughout the machining process. Neem oil performed best among the oils for surface roughness while mineral oil outperformed them all for cutting temperature, according to the experimental results. The optimum spindle speed, feed rate and depth of cut for neem, palm kernel and mineral oils are 870 rev/min, 0.25 mm/rev and 1.25 mm; 870 rev/min, 0.10 mm/rev and 0.75 mm; and 415 rev/min, 0.10 mm/rev and 1.00 mm, respectively.

The machining parameter optimisation during the machining of the Cr-Mn austenitic stainless steel by the topsis method

The Cr-Mn stainless steel (austenitic) was dry turned in a lathe. A carbide tool (Indoloy) was used for the machining. The speed (v), feed (f), and depth of cut (DOC, d) were the input parameters. The experimental work was done following the Taguchi L 27 design plan. The chip reduction coefficients (CRC) and the von Mises stresses (VMS) were determined for all the experimental conditions. The TOPSIS (Technique for Order of Preference by Similarity to the Ideal Solution) method was used to optimise the process parameters. The optimal conditions were attainable at high speed (v = 100 m/min.), moderate feed (f = 0.1 mm/rev.), and moderate DOC (d = 1 mm). At optimized machining conditions, the VMS was 3429.5 MPa and the CRC was 1.6. The VMS was higher for the lower speed, and the VMS was lower for the higher speed. The higher VMS was attributed to the formation of strain-induced martensite (SIM). The lower VMS was due to the dynamic recrystallization (DRX) at the higher temperature for the higher speed. For the higher speed, the DRX and the dynamic precipitation (DP) occurred at the chip surface (under).

Effect of Copper Percentage in Copper Tungsten Electrode on Ti6Al4V by Electrical Discharge Machining

The aim of this study was to identify the optimal parameters for electrical discharge machining of Ti6Al4V titanium alloy using copper tungsten electrodes with tungsten compositions of 70% and 80%, respectively. The experiment involved varying current, pulse on time, and duty factor were determined using the Taguchi experimental method L-18 orthogonal arrays. The level of importance of each machining parameter on material removal rate (MRR) was determined using analysis of variance (ANOVA), and the optimal machining parameter combination was obtained using the analysis of signal-to-noise (S/N) ratio. The results showed that a Cu30/W70% electrode performed the best in terms of MRR, electrode wear, and surface roughness. The optimal machining conditions for achieving maximum MRR were found to be a current of 30 A, a pulse on time of 50 μs, and a duty factor of 40%.

A comprehensive review of machine learning techniques in computer numerical controlled machines

Machine learning (ML) is significant advancements in computer science and data processing systems that may be utilized to improve almost all technology-enabled services, goods, and industrial applications. Machine learning is a branch of computer science and artificial intelligence. It emphasizes the use of data and algorithms to mimic the learning process of machines and improve system accuracy. To extend the life of the cutting tools used in machining processes, machine learning algorithms may be used to forecast cutting forces and cutting tool wear. In order to improve productivity during the component production processes, optimized machining parameters for CNC machining operations may be obtained by applying cutting-edge machine learning algorithms. Furthermore, the appearance of Advanced machine learning algorithms can forecast and enhance machinable components to raise the caliber of machinable parts. Machine learning is applied to prediction approaches of energy consumption of CNC machine tools in order to analyses and minimize power usage during CNC machining processes. The use of machine learning and artificial intelligence systems in CNC machine tools is examined in this paper, and future research projects are also suggested in order to offer an overview of the most recent studies on these topics. As a result, the research filed can be moved forward by reviewing and analyzing recent achievements in published papers to offer innovative concepts and approaches in applications of machine learning in CNC machine tools.

Optimization of Machine and Operational Parameters in the Development of Stubble Manager cum Crop Planter

Aims: Optimization of machine parameters (type of flail blade and number of strip-till blades per flange) and operational parameters (rotor shaft speed and forward speed) for the development of stubble manager cum crop planter for conservation agriculture.
 Study Design: Split plot (3 factors); Type of flail blade/number of strip-till blades per flange as main plot and rotor shaft speed and forward speed as sub plots.
 Place and Duration of Study: Dr. NTR College of Agricultural Engineering, Bapatla, Andhra Pradesh, India. Duration of the study was from August 2020 to July 2022 (24 moths).
 Methodology: Developed stubble manager cum crop planter as a solution for seeding of rice fallow pulse crops under combine harvested paddy field condition. This machine consists of two consists of stubble managing unit for mulching and strip tillage operations at a time and crop planting unit for placing pulse crop seed in furrow strips opened by stubble managing unit. The stubble managing unit was optimized for mulching parameters (shredding efficiency and paddy residue size reduction) and strip tillage parameters (furrow cross sectional area, furrow backfill and mass median diameter of soil clods) through field evaluation under combine harvested paddy field condition.
 Results: Interactions between machine and operational parameters were not significant on shredding efficiency. Highest paddy residue size reduction (77.33 %) up to 10 cm was observed with Y-type flail blade at rotor shaft and forward speeds 1650 rpm and 2.5 km h-1. Lowest furrow cross sectional area (20.93 cm2) and highest furrow soil backfill (23.24 %) were obtained with two numbers of strip-till blades per flange at rotor shaft and forward speed 1350 rpm and 2.0 km h-1 respectively. Interactions between machine and operational parameters were not significant on mass median diameter of soil clods.
 Conclusion: Optimized parameters for the stubble managing unit of the proposed machine were Y-type flail blade, 2-numbers of strip-till blades per flange, rotor shaft speed 1500 rpm and forward speed 2.0 km h-1.

Optimization of Process Parameters in Abrasive Water Jet Machining of Austempered Ductile Iron (ADI)

Optimization of Process Parameters in Abrasive Water Jet Machining of Austempered Ductile Iron (ADI)

Determination of the Depth of Cut via Surface Integrity

The present paper continues the authors’ research in machining process optimization, including the direction of machining parameters optimization. The paper develops an innovative method, via surface integrity, for determining the technological route and the related depths of cut, with respect to machining front faces of cast iron parts. For correctly establishing the depths of the cut, the errors that appear within surface and subsurface layers during the casting process, as well as during machining, must be gradually eliminated. These errors make it necessary to consider the concept of surface integrity. This paper presents the modality of integrating the components of surface integrity into the depth of cut. For the practical use of this method, a new software tool based on a series of mathematical models and a small database was conceived. A case study illustrates how the method is applied and the software tool used to solve a specific application in the case of a belt pulley.

INVESTIGATIONS ON MECHANICAL, WEAR AND MACHINING BEHAVIOR OF TITANIUM-REINFORCED AlMg0.5Si COMPOSITES

AlMg0.5Si (AA6063) as a base matrix composite with different weight percentages of Titanium (Ti) has been synthesized by the stir casting method. Objective of this research is to investigate the effect of Ti addition on AA6063 to produce aluminium matrix composites (AMCs). AA6063, AA6063/4%-Ti, AA6063/8%-Ti, and AA6063/12%-Ti are different volume fractions. The Scanning electron microscope (SEM) analysis is used to examine the modified grain refinement and worn surface on fabricated AMCs. The evenly distributed structure was attained on the processed AA6063/Ti combinations. The mechanical properties of processed composites were investigated with the following experimental tests such as tensile strength, impact strength, micro hardness, compressive, and flexural strength. The entire mechanical properties of fabricated AMCs were enhanced with increasing Ti reinforcement particles. The CNC drilling of AA6063/Ti composites was machined by spindle speed, feed and depth of cut. The gray relational analysis (GRA) was used to carry out the optimal machining parameters with their responses such as material removal rate and surface roughness. From the analysis of ANOVA, spindle speed parameter achieved most influencing parameters.

Dependence of pre-treatment structure on spheroidization and turning characteristics of AISI1040 steel

During machinability, the combination of machining process parameters and the material properties of the component to be machined plays an important role. Material properties depend upon the type of phase form present and the grain size of the formed phases, which in turn depends upon the prior treatment given to alter the initial room temperature types and form. Accordingly, spheroidization treatment was carried out on medium carbon steel (AISI1040) by altering the initial room temperature structure through normalizing and hardening treatment. Machinability experiments were performed on CNC machine by varying machining process constraints. Tool wear and surface roughness of the machined component obtained by turning were analyzed and correlated. Using Minitab and full factorial design, the ANOVA study was carried out. With the help of regression analysis, residual and main effect plots combined optimization (tool wear and surface roughness) was targeted. ANOVA result shows excellent machinability for the as-bought-spheroidized condition where feed has a 67% contribution to tool wear (TW) whereas the depth of cut has a 71.91% contribution to surface roughness (SR). Also, the optimized regression values obtained for machining parameters are feed (0.39 mm/rev), depth of cut (0.6 mm), and spindle speed (780 rpm) with composite desirability of 0.8174. TW and SR experimental values for the optimized machining parameters are 0.039 mm and 2.89 μm, respectively, and the difference between the actual and optimized values is less than 5%.

Dry machining characteristics of coated inserts and multi response optimization through DEAR‐Taguchi method

AbstractThe primary intent of the proposed research work is to investigate the effectiveness of the titanium aluminium nitride/tungsten carbide‐carbide coated insert during dry turning structural stainless steel. The aim of the study is to simultaneously optimise machining variables like spindle speed, depth of cut and feed for several responses like flank wear, material removal rate and surface roughness. Titanium aluminium nitride/tungsten carbide‐carbide is coated on the surface of carbide tool by cathodic arc evaporation method. The characterization studies have been conducted to ensure the existence of coating material. Micro hardness of coated and pure inserts was tested, which confirms that titanium aluminium nitride/tungsten carbide‐carbide coated insert possesses 17.43 % augmented hardness over pure inserts. The machining was performed by adopting Taguchi experimental design. A multi‐response optimization approach was applied in this study that included ranking methodology based on data development analysis and Taguchi's design. The performance index for multiple responses was measured and mathematically analysed for their effect on processing parameters. The combination of parameter such as spindle speed: 2000 min−1; cutting depth: 0.45 mm and feed rate: 0.16 mm rev−1 was experimental as optimal machining parameters.

Optimization of Machining Characteristics in Turning Operation of LM2 -Al2O3 Metal Matrix Composite

The optimization of machining parameters in the turning operation of metal matrix composite (MMC) is a crucial aspect in the manufacturing process. In this research work, Taguchi Method of optimization is used to optimize the cutting parameters, which include cutting speed, feed rate, and depth of cut. The experiments are carried out according to Taguchi L9 algorithm. The optimization is based on minimizing the surface roughness. The results show that the cutting speed has the most significant effect on the responses, followed by feed rate and depth of cut. It is observed a good agreement between the predicted and actual results, indicating the effectiveness of the Taguchi method in optimizing the cutting parameters.

Machinability Studies and Optimization of Process Parameters in Wire Electrical Discharge Machining of Aluminum Hybrid Composites by the VIKOR Method

Machinability Studies and Optimization of Process Parameters in Wire Electrical Discharge Machining of Aluminum Hybrid Composites by the VIKOR Method

Sustainable Dry Machining of Stainless Steel with Microwave-Treated Tungsten Carbide Cutting Tools.

This paper presents a research investigation conducted on the turning of stainless steel 316 material under a dry environment using microwave-treated cutting tool inserts. Plain tungsten carbide WC tool inserts were exposed to microwave treatment for enhancement of their performance characteristics. It was found that a 20-min microwave treatment resulted in the best tool hardness and metallurgical characteristics. These tool inserts have been used to machine SS 316 material following the Taguchi L9 design of experimental techniques. A total of eighteen experiments have been conducted by varying three main machining parameters, i.e., cutting speed, feed rate, and depth of cut, at three levels per parameter. It has been found that tool flank wear increased with all three parameters and surface roughness decreased. At the longest dept of cut, surface roughness increased. An abrasion wear mechanism was found on the tool flank face at a high machining speed and adhesion at low speed. Chips with a helical shape and low serrations have been investigated. Turning SS 316 at optimum machining parameters of 170 m/min cutting speed, 0.2 mm/rev feed rate, and 1 mm depth of cut, as obtained by the multiperformance optimization technique grey relational analysis, resulted in the best values of all machinability indicators: 242.21 µm tool flank wear, 3.81 µm mean roughness depth, and 34,000 mm3/min material removal rate, at a single parameter setting. In terms of research achievements, the percentage reduction in surface roughness is approximately 30% and represents an almost ten-fold improvement in the material removal rate. The combination of machining parameters of 70 m/min cutting speed, 0.1 mm/rev feed rate, and 0.5 mm depth of cut is optimum for the lowest value of tool flank wear when considered for single parameter optimization.

Study of the surface roughness and optimization of machining parameters during laser-assisted fast tool servo machining of glass-ceramic

Glass-ceramic is a typical hard and brittle material that is difficult to machine. In order to improve the surface quality of laser-assisted fast tool servo machining optical free-form surface of glass-ceramic, the effects of spindle speed, feed speed, piezoelectric frequency and laser power on the surface roughness were investigated. Firstly, the Taguchi method (TM) was used to establish the orthogonal experiment, and the contribution rate of each machining parameter to the surface roughness was obtained through variance and signal-to-noise ratio (S/N) analysis. The order of the influence degree of each parameter on the surface roughness is as follows: laser power > spindle speed > feed speed > piezoelectric frequency. The optimal machining parameter combinations obtained for the TM experiment are as follows: spindle speed 50 rpm, feed speed 0.01 mm rev−1, piezoelectric frequency 8 Hz, laser power 75 W. The range of surface roughness reduction obtained by comparing laser-assisted machining (LAM) with pure fast tool servo (FTS) machining is 38.75%∼58.77%. The Box-Behnken Design (BBD) in response surface methodology (RSM) was used to design experiments and a regression model for surface roughness was established through RSM. The deviation between the surface roughness predicted by the regression equation and the experimental value is less than ±6%. The influence law of various machining parameters on surface roughness was studied through three-dimensional response surface. RSM optimized the minimum surface roughness with a desirability of 99.43%. The optimal combination of machining parameters optimized through RSM is as follows: spindle speed 53.71 rpm, feed speed 0.02 mm rev−1, piezoelectric frequency 6.73 Hz, laser power 72 W. This paper is the first to combine LAM with FTS for machining optical free-form surface of glass-ceramic. This study provides a reference for laser-assisted fast tool servo machining and the research methods of surface quality.

Comparative Evaluation of Coated Carbide and CBN Inserts Performance in Dry Hard-Turning of AISI 4140 Steel Using Taguchi-Based Grey Relation Analysis

Dry hard-turning is a vital manufacturing method for machining hardened steel due to its low cost, high machining efficiency, and green environmental protection. This study aims to analyze the effect of various machining parameters on cutting forces and surface roughness by employing RSM and ANOVA. In addition, multi-objective optimization (Grey Relation Analysis: GRA) is performed to determine the optimum machining parameters. Dry hard-turning tests were carried out on AISI 4140 steel (50 HRC) using coated carbide and CBN inserts with different nose radii. The results show that the cutting force components are greatly affected by the cutting depth and cutting speed for both cutting inserts. As the level of cutting depth and cutting speed rise, the cutting forces also increase. However, the feed rate was the main factor in surface roughness. A low feed rate and high cutting speed lead to good surface quality. According to the results, CBN inserts exhibited better performance compared to carbide inserts in terms of minimum cutting forces and surface roughness. The lowest radial force (Fx = 55.59 N), tangential force (Fy = 15.09 N), cutting force (Fz = 30.49 N), and best surface quality (Ra = 0.28 µm, Rz = 1.8 µm) were obtained using a CBN tool. Finally, based on the GRA, the (V = 120 m/min, f = 0.04 mm/rev, a = 0.06 mm, r = 0.8 mm) have been chosen as optimum machining parameters to minimize all responses simultaneously in the machining of AISI 4140 steel using both carbide and CBN inserts.

Recent Trends, Developments, and Emerging Technologies towards Sustainable Intelligent Machining: A Critical Review, Perspectives and Future Directions

Intelligent manufacturing is considered among the most important elements of the modern industrial revolution, which includes digitalization, networking, and the development of the intelligent manufacturing industry. With the progressive development of modern information technology, particularly the new generation of artificial intelligence (AI) technology, many new opportunities are coming into existence for intelligent machine tool (IMT) development. Intelligent machine tools offer diverse advantages, including learning and optimizing machining processes, error compensation, energy savings, and failure prevention. The paper focuses on the machine tool market in terms of global production, the leading machine tool-producing countries, and the leading countries’ market share in machine tool production. Moreover, the usage of various artificial intelligence techniques in intelligent machining operations is also considered in this comprehensive review, including machining parameter optimization, tool condition monitoring (TCM), and chatter vibration management of intelligent machine tools. Furthermore, future challenges for the machine tool industry are also highlighted.