Optimization Of Machining Process Research Articles

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.

Aero-Engine Blade Cryogenic Cooling Milling Deformation Simulation and Process Parameter Optimization.

For the machining of aero-engine blades, factors such as machining residual stress, milling force, and heat deformation can result in poor blade profile accuracy. To address this issue, simulations of blade milling were completed using DEFORM11.0 and ABAQUS2020 software to analyze blade deformation under heat-force fields. Process parameters such as spindle speed, feed per tooth, depth of cut, and jet temperature are used to design both a single-factor control and BBD test scheme to study the influence of jet temperature and multiple changes in process parameters on blade deformation. The multiple quadratic regression method was applied to establish a mathematical model correlating blade deformation with process parameters, and a preferred set of process parameters was obtained through the particle swarm algorithm. Results from the single-factor test indicated that blade deformation rates were reduced by more than 31.36% in low-temperature milling (-190 °C to -10 °C) compared with dry milling (10 °C to 20 °C). However, the margin of the blade profile exceeded the permissible range (±50 µm); therefore, the particle swarm optimization algorithm was used to optimize machining process parameters, resulting in a maximum deformation of 0.0396 mm when the blade temperature was -160 °C~-180 °C, meeting the allowable blade profile deformation error.

Optimizing Processing Parameters and Surface Quality of TC18 via Ultrasonic-Assisted Milling (UAM): An Experimental Study.

This study conducted longitudinal ultrasonic-assisted milling (UAM) tests and optimized a combination of milling technological parameters to achieve high-quality machining of TC18 titanium alloy. The motion paths of the cutter under the coupled superposition states of longitudinal ultrasonic vibration and end milling were analyzed. Based on the orthogonal test, the cutting forces, cutting temperatures, residual stresses, and surface topographical patterns of TC18 specimens under different UAM conditions (cutting speeds, feeds per tooth, cutting depths, and ultrasonic vibration amplitudes) were examined. The differences between ordinary milling and UAM in terms of machining performance were compared. Using UAM, numerous characteristics (including variable cutting thickness in the cutting area, variable cutting front angles of the tool, and the lifting of the cuttings by the tool) were optimized, reducing the average cutting force in all directions, lowering the cutting temperature, increasing the surface residual compressive stress, and significantly improving the surface morphology. Finally, fish scale bionic microtextures with clear, uniform, and regular patterns were formed on the machined surface. High-frequency vibration can improve material removal convenience, thus reducing surface roughness. The introduction of longitudinal ultrasonic vibration to the end milling process can overcome the limitations of traditional processing. The optimal combination of UAM parameters for titanium alloy machining was determined through the end milling orthogonal test with compound ultrasonic vibration, which significantly improved the surface quality of TC18 workpieces. This study provides insightful reference data for subsequent machining process optimization.

The Optimization of Micro-Edm Machining Process when Using Carbon Coated Micro Electrode as a Tool

1. ABLYAZ T., MURATOV K., PREETKANWAL S.B., SARABJEET S.S., 2019, Experimental Investigation of Wear Resistance of Copper Coated Electrode-Tool During Electrical Discharge Machining. IOP Conf. Ser.: Mater. Sci. Eng., 510, 012001, https://doi.org/10.1088/1757-8.... CrossRef Google Scholar

Multi-objective optimization for improving machining benefit based on WOA-BBPN and a Deep Double Q-Network

To scientifically and reasonably set and adjust the machining parameters, and improve the machining benefit of the batch machining process including machining quality, performance and efficiency, this paper proposes a low cost and high accurate multi-objective optimization framework by integrating intelligent technologies from three perspectives of physical experiment design, objective function construction and multi-objective optimization. In this framework, multilayer design (MLD) which is a computer experiment design method with low redundancy, good uniformity and filling is applied to the machining physics experiment design to reduce the experiment cost. Whale optimization algorithm (WOA) optimized backpropagation neural network (BBPN) is utilized to construct the optimization objective functions to improve the accuracy of functions. And Deep Double Q-Network (DDQN) is employed to achieve multi-objective optimization of machining quality, efficiency, and performance. The effectiveness of MLD, WOA-BBPN and DDQN in multi-objective optimization of machining process are verified by comparisons of common modeling and multi-objective optimization algorithms based on the milling data. These techniques provide a good guidance for the selection of machining parameters to improve the machining benefit and demonstrates some advantages. The research of this paper also offers a good example of applying intelligent technology in machining.

Characterization of pineapple leaf fiber, areca fiber and egg shell powder reinforced phenolic resin composites and finding optimal parameters for sustainable machining

In the aim of the present investigation is to fabricate pineapple leaf and areca fiber and egg shell powder reinforced phenolic resin composites through hand layup technique and study their mechanical properties such as tensile, compression, flexural, impact and hardness properties. The mechanical properties finding revealed that 70:30 ratio reinforced composite showed efficient properties among all the combinations of fibers. So machining study has been carried out on this composite with three process variables such as spindle speed, feed rate and pecking and analyzed the effect of drilling duration through Taguchi’s technique. The finding revealed that a spindle speed of 1000 rpm, feed rate of 0.15 mm/min and pecking of 4.5 mm is the optimal machining process parameter for getting minimum drilling duration. Moreover, Analysis of Variance (ANOVA) confirmed that, spindle speed is the process parameter having a significant effect on the drilling duration.

Optimization of the Wire Electric Discharge Machining Process of Nitinol-60 Shape Memory Alloy Using Taguchi-Pareto Design of Experiments, Grey-Wolf Analysis, and Desirability Function Analysis

The nitinol-60 shape memory alloy has been rated as the most widely utilized material in real-life industrial applications, including biomedical appliances, coupling and sealing elements, and activators, among others. However, less is known about its optimization characteristics while taking advantage to choose the best parameter in a surface integrity analysis using the wire EDM process. In this research, the authors proposed a robust Taguchi-Pareto (TP)-grey wolf optimization (GWO)-desirability function analysis (DFA) scheme that hybridizes the TP method, GWO approach, and DFA method. The point of coupling of the TP method to the GWO is the introduction of the discriminated signal-to-noise ratios contained in the selected 80-20 Pareto rule of the TP method into the objective function of the GWO, which was converted from multiple responses to a single response accommodated by the GWO. The comparative results of five outputs of the wire EDM process before and after optimization reveals the following understanding. For the CR, a gain of 398% was observed whereas for the outputs named Rz, Rt, SCD, and RLT, losses of 0.0996, 0.0875, 0.0821, and 0.0332 were recorded. This discrimination of signal-to-noise ratio based on the 80-20 rule makes the research different from previous studies, restricting the data fed into the GWO scheme to the most essential to accomplishing the TP-GWO-DFA scheme proposed. The use of the TP-GWO-DFA method is efficient given the limited volume of data required to optimize the wire EDM process parameters of nitinol.

Study on optimization of surface processing technology of silicon nitride bearing ring

Based on the difficult machining characteristics of silicon nitride materials, this manuscript focuses on optimizing the precision machining process of silicon nitride bearing components and improving the machining efficiency and quality of silicon nitride bearing components. Firstly, the mechanism of crack formation and propagation in hard and brittle materials under the action of abrasive particles is discussed in this paper, and based on this, a kinematic model of single abrasive grain cutting on hard and brittle materials is established. The effect of workpiece linear velocity, grinding wheel linear velocity, grinding wheel oscillation velocity and feed velocity on inner surface roughness of Si3N4 ring was discussed through grinding test. The experimental results show that the surface roughness can reach about Ra0.20 ∼Ra0.33 μm through a large number of grinding experiments by adjusting the combination of process parameters μm. On this basis, use the optimized process parameters calculated by the surface quality prediction model constructed in this manuscript to conduct grinding test again, and the surface roughness value reaches about Ra0.19 ∼Ra0.23 μm. The purpose of optimizing the process parameters is realized. Finally, the surface roughness can be further reduced and maintained at Ra0.05 ∼Ra0.06 μm by further superfinishing the surface after the process optimization with an oilstone. The research work of this manuscript realized the optimization of precision machining process of silicon nitride bearing ring and the rapid optimization of machining process through the established prediction model, which improved the precision machining efficiency of ceramic bearing components. Through the study of this manuscript, a process route of controllable processing of inner surface quality of Si3N4 ring is formed, from preliminary selection of process parameters by optimization model to precision grinding and then to ultra-finishing of whetstone, which provides theoretical reference for efficient and precise manufacturing of practical bearing components.

Non-invasive milling force monitoring through spindle vibration with LSTM and DNN in CNC machine tools

Milling force reflects the complex mechanical interaction between machine tool and workpiece, it is of great significance to acquire milling force accurately to monitor and optimize machining process. In this paper, a convenient method to monitor milling force non-invasively is proposed, which can predict milling force based on spindle vibration in real-time. Long and short-term memory network (LSTM) and deep neural network (DNN) are used to build the mapping between milling force and spindle vibration in data-driven manner. Temporal features of training data are extracted through LSTM, while DNN is used to reinforced key features. Accuracy of the proposed method is demonstrated by experiments. Comparing with existing data-driven prediction methods, prediction accuracy is improved at least by 16.7% using LSTM-DNN. This paper provides an easy implemented and accurate approach to acquire milling force of machine tool and has great value for machining process monitoring.

Investigation on the effect of machining parameters on 42CrMo4 DPS steels

Machining 42CrMo4 martensite—ferrite dual phase steel is challenging due to its high hardness and it is essential to determine the favorable requirements for the optimum machining condition. The ability to alter the martensite quantity in dual phase structure of steel leading to variation in bulk hardness is the motivation for machinability investigation. In heavy duty machining, the determination of tool life and surface roughness at various conditions of machining plays an important role in the manufacturing industry. In the present work, machinability tests are carried out on 42CrMo4 martensite—ferrite dual phase steel to assess the tool life and surface roughness. Speed, feed, and depth of cut are varied in different levels. The tests are carried out as per the full factorial method. A microstructure study is performed to correlate the various mechanical properties with phase morphology. The main objective of this study is to obtain the optimized machining process parameters for the turning operation of 42CrMo4 martensite—ferrite dual phase steel. All cutting tests are carried out under dry conditions using a carbide insert. Microstructure and mechanical property analysis shows an increase in the martensite quantity with the increase in the dual phase processing temperature. From the ANOVA results, it is found that the depth of cut is the major contributing factor to the variation of tool life and surface roughness within the range of values considered for the study. Microstructure analysis revealed the distribution of ferrite and martensite phases evenly. The optimum combination of machining parameters is calculated for obtaining a superior combination of tool life and surface roughness. The 42CrMo4 DPS steel may be used for structural applications with wide variation in the property range.

Micro-machining and process optimization of ultrasonic assisted rotary µ-electrochemical discharge machining using TOPSIS method

Glass is a substance that is gaining numerous applications in many industries, such as biomedical, nuclear, etc., due to its hard, transparent, and inert nature. The main challenge is to machine glass due to its brittle nature. Electrochemical Discharge Machining (ECDM) is a novel process that is being used to machine such hard and electrically non-conductive materials. Over the years, researchers tried to incorporate many other processes into the ECDM process in order to increase the process capabilities. In this research, Ultrasonic assisted Rotary µ- ECDM process was used to drill holes in glass workpiece. In this process, the rotary motion was provided to the workpiece rather than to the tool to observe the results of this process. Experiments were performed using Taguchi’s L9 orthogonal array taking amplitude of vibrations, rotational speed, and tool feed rate as the input parameters and overcut and circularity as the response characteristics. In addition to this, multi-response process optimization was carried out using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method to optimize the process.

Experimental Investigation of EN-31 Steel Using VMC

This research paper presents an experimental investigation using the Vertical Machining Centre (VMC) for machining the EN31 material. The study looks at how different input factors, including feed, speed, and cut depth, affect the output factors, like the material removal rate (MRR) and surface roughness (SR). The Taguchi method is used as the optimization technique to identify the optimal combination of input parameters that lead to maximum MRR and minimum SR. The objective of the study is to establish the relationship between input and output parameters and to offer a workable strategy for machining process optimization. The study's findings will advance our understanding of EN31 material machining with VMCs and have real-world implications for the machining sector.

Adaptive CNC machining process optimization of near-net-shaped blade based on machining error data flow control

Adaptive CNC machining process optimization of near-net-shaped blade based on machining error data flow control

Enhancement Material Removal Rate Optimization of Sinker EDM Process Parameters Using a Rectangular Graphite Electrode

This article discusses the optimization of sinker electrical discharge machining (sinker EDM) processes using SPHC material that has been hardened. The sinker EDM method is widely employed, for example, in the production of moulds, dies, and automotive and aeronautical components. There is neither contact nor a cutting force between the electrode and the work material in sinker EDM. The disadvantage of the sinker EDM is its low material removal rate. This work aims to optimize the material removal rate (MRR) using graphene electrodes in a rectangular configuration. The SPHC material was selected to determine the optimum MRR model of the sinker EDM input parameter. The Taguchi experimental design was chosen. The Taguchi technique used three input parameters and three experimental levels. Pulse current (I), spark on time (Ton), and gap voltage were among the input parameters (Vg). The graphite rectangle was chosen as an electrode material. The input parameter effect was evaluated by S/N ratio analysis. The result showed that pulse current has the most significant impact on material removal rate in the initial study, followed by spark on time and gap voltage. All input parameters are directly proportional to the MRR. For optimal material removal rate, the third level of pulse current, spark on time, and gap voltage must be maintained. In addition, the proposed Taguchi optimization model could be applied to an existing workshop floor as a simple and practical electronic tool for predicting wear and future research.

A new flower pollination algorithm with improved convergence and its application to engineering optimization

The flower pollination algorithm (FPA) is a nature-inspired optimization algorithm that mimics the pollination behaviour of flowering plants. Despite the promising performance of FPA in solving single objective optimization problems, its convergence still poses challenges in practice. This study proposes a modified FPA with additional features from chaos theory and frog leaping algorithm augmented by inertia weights. The modified FPA proposed in this study is tested against benchmark mathematical functions, mechanical engineering design optimization problems, and machining process optimization problems. Performance comparison with other state-of-the-art optimization algorithms has demonstrated its ability in terms of convergence. The modified FPA significantly reduced the number of function evaluations by 84.14%, as compared to FPA in optimizing the benchmark mathematical functions. Besides, the proposed modified FPA outperformed others in 12 out of 15 mechanical engineering optimization problems.

Generalizability analysis of tool condition monitoring ensemble machine learning models

Tool condition monitoring (TCM) is an essential research area for the optimization and automation of metal machining processes, and could help manufacturers reduce costs, production time, machine downtime, energy use, and part scrappage. However, TCM systems developed in prior studies have struggled to reach the high level of generalizability which is necessary for industrial applications. This study addresses TCM system generalizability to new machining conditions, how variations in machining and environmental conditions may be used to improve model generalizability, and ensemble machine learning techniques for TCM. Milling tool life experiments were conducted using various machining conditions, and the processes' sound, spindle power, and axial load signals were collected. Different machine learning models were evaluated for the prediction of tool wear levels, including four individual models and five ensemble models. Changes in cutting speed were found to display a large effect on model performance, while the chip load showed some effect, and the feed rate had little effect. A simulated noise data augmentation technique for model improvement is applied within TCM for the first time, and resulted in increased model generalizability and reduced overfitting. Across several performance metrics the extremely randomized trees ensemble machine learning model generally performed the best for this application, achieving a leave-one-group-out cross validation accuracy score of 92.4 %, a 10-fold cross validation score of 98.9 %, and an averaged accuracy across 11 generalizability tests of 87.3 %.

Process optimization of Titanium alloy machining with Wire Electro Discharge Machining using Taguchi’s Grey Relational Analysis

Titanium alloys have exceptional mechanical properties together with high strength to weight ratio and have widespread applications in the aerospace sector. Generally, these alloys have drawback with machinability. Wire-Electro Discharge Machining (WEDM) is in use to make complex shapes. Titanium alloy efficient machining includes choosing appropriate process parameters to optimize the performance characteristics. The present experimental investigation deals with process optimization of Wire Electro Discharge Machining (WEDM) process on Titanium grade 5 material using brass wire of 0.25 mm. Servo Voltage, Peak current, Pulse-on-time and Pulse-off-time, are considered as input parameters. Experiments are carried out utilizing Taguchi's L27 orthogonal array. Every process parameter is analyzed at 3 levels by using Grey Relational Analysis (GRA). The finest process parameters that optimize the Wire Wear Ratio (WWR) and Surface Roughness (SR) are identified. ANOVA is accomplished to estimate the significance of all input parameters on output performance characteristics. Confirmation tests are conducted. Experimental results are in acceptable concurrence with the confirmation test values.

A Comparative Study on Prediction of Cutting Force using Artificial Neural Network and Genetic Algorithm during Machining of Ti-6Al-4V

The purpose of this comparative study is to improve the predictive accuracy of the cutting force during the turning of Ti-6Al-4V on a lathe machine. By optimizing the machining process parameters such as cutting speed, feed rate, and depth of cut, the cutting force in the machining process can be improved significantly. Cutting force is one of the crucial characteristics that must be monitored during the cutting process in order to enhance tool life and the surface finish of the workpiece. This paper is based on the experimental dataset of cutting forces collected during the turning of titanium alloy under the Minimum Quantity Lubrication (MQL) condition. To predict the cutting forces, two machine learning techniques are explored. Firstly, a black-box model called an Artificial Neural Network (ANN) is proposed to predict cutting force. Using the Levenberg-Marquardt algorithm, a two-layered feedforward neural network is built in MATLAB to predict cutting force. The second model to be implemented was the Genetic Algorithm (GA), a white-box model. GA is an optimization technique which is based on Darwinian theories. It is a probabilistic method of searching, unlike most other search algorithms, which require definite inputs. Using symbolic regression in HeuristicLab, a GA model is developed to estimate cutting force. The anticipated values of cutting forces for both models were compared. Since the ANN model had fewer errors, it was ascertained that the particular model is preferable for machining process optimization.

Gene expression programming for parametric optimization of an electrochemical machining process

Gene expression programming for parametric optimization of an electrochemical machining process

Effect of vibration parameters on the material removal characteristics of high-entropy alloy in scratching

Although vibration-assisted scratching achieves higher machining efficiency than conventional scratching, its microstructural behavior is still unclear. In the present work, the deformation behavior and microstructural growth of monocrystalline CoCrFeNiCu high-entropy alloy during the vibration-assisted scratching through molecular dynamics simulation are investigated. The expected result shows that the vibration-assisted scratching effectively decreases the total force compared to the conventional scratching. The increase in the vibration amplitude and frequency leads to the reduction in the total force, which is advantageous in scratching. The shear strain concentration region in vibration-assisted scratching is larger than that of conventional scratching due to the expansion of the scratched area. Besides, the increase in vibration amplitude and frequency leads to a corresponding increase in shear strain and residual stress. The vibration-assisted scratching produces a larger high-temperature zone than the conventional scratching, and the increase of vibration frequency also results in an increase in the scratched zone temperature, which has a positive effect on easier machining. The microstructure evolution shows that Shockley partial dislocations account for the majority of the total dislocations. Moreover, the scratching with a large vibration amplitude and frequency removes more material. Therefore, insights into scratching behavior at the atomic level can aid in the optimization of the vibration-assisted machining process.