Machined Workpiece Research Articles

Investigation of Al-6061 alloy in face milling through DFA approach

The manufacturing of the components through the machining process considering the higher production rate and desired quality is always a matter of concern for the industrial field. Also, achieving this with a green machining process using near-dry cooling strategies such as minimum quantity lubrication (MQL), cryogenic cooling etc. is a challenging task. In this work, an attempt has been made to implement the MQL for the machining of the aluminium alloy Al-6061 workpiece. The outputs such as temperature and surface roughness have been focused under the controlled machining environment. As there are two output responses, a multi-response optimization technique system desirability functional analysis (DFA) has been exploited. The experimental array was created using the Taguchi approach. A total of eighteen try-outs were executed and the best setting achieved in terms of the input factor levels is CFR = 150 ml/hr, Speed = 1000 rpm, 175 ml/hr, feed of tool = 60 mm/min and cutting depth = 0.2 mm. The values of temperature and surface roughness are noted at optimal settings as 60 °C and 0.32 µm respectively with an enlargement of 2.67 % in the multi-response factor.

Machining performance and optimization of process parameters of Incoloy alloy A-286 using EDM process

The major concern faced by various manufacturing Industry is to obtain the optimum machining variables maximum rate of material removed, low rate of tool wear and good value of surface integrity for a superalloys which is desirable characteristics from machining performance. The problem identified with the machining of the Incoloy alloy A286 which are considered very hard for getting machined which can be attained by Electrical Discharge Machining (EDM). The presented paper shows impact in various input parameters that is current, pulse on time and pulse off time and the output parameters that is material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) based on the experiment performed that are thoroughly studied also during machining of Incoloy alloy A-286 workpiece while machining it with the copper as tool electrode material. Response Surface Methodology based Box Bekhen Design technique was used for Machining variables to get optimized. The parameters for optimization were later calculated.

Development of a simple substitute model to describe the normal force of fluids in narrow gaps

AbstractFluids in narrow gaps are employed frequently in many applications. The motivation for their use is diverse and ranges from hydrodynamic lubrication in plain bearings to the transport of hard particles into the working gap for the purpose of machining workpiece surfaces in lapping processes. Depending on the focus of the analysis, it may be useful to investigate the entire pressure field or to calculate only individual quantities. For example, in sophisticated simulations it may be of interest to know the resulting force of a fluid as a function of the external system state in order to describe its damping characteristics. Especially for the simulation of flows in narrow gaps, the Reynolds equation is a convenient choice, which, in contrast to the more general Navier‐Stokes equations, can lead to considerable savings in computational time because no three‐dimensional discretization is required, but only a two‐dimensional discretization. However, if not a highly detailed pressure field is of interest, but only simple relations such as the resulting force as a function of distance and velocity, and if this relation to be evaluated many times for different parameter combinations over a wide range of values, the use of a robust substitute model is a good choice. This article deals with the creation of such a substitute model based on the Reynolds equation taking cavitation into account.

Intelligent integrated framework towards high-accuracy machining

The thermal error is the main factor hindering the realization of the high-accuracy machining. The intelligent integrated framework towards the high-accuracy machining, which is expected to realize improve the machining accuracy, is scarcely reported so far. The self-perception is difficult due to the limited bandwidth of the industrial Internet, and the thermal error model with a strong robustness and high prediction accuracy has not been established. To address the above challenges, in this study, an intelligent integrated framework is designed towards the high-accuracy machining. The intelligent gateway is designed to conduct the data transfer and edge computing. Then the data perception and transfer are conducted with the ZigBee wireless sensor network and cluster-type network structure. Then the data computation is carried out to realize the preliminary training and fine training of the thermal error model. The Bi-directional Long Short-Term Memory Network (BILSTM) is combined with a new Residual Network (RESNET) to propose the BILSTM-RESNET (BLRNET) model, and then the Convolutional Neural Network (CNN)-BLRNET model is proposed, and the spatiotemporal features in the thermal error data are extracted. Finally, the CNN-BLRNET model is embedded into the intelligent integrated framework, and the thermal error control service is provided by the intelligent integrated framework. With the implementation of the intelligent integrated framework, the measured contour error is reduced by 41.2%, and the roundness error of the machined workpiece is reduced by 67.5%.

Prediction model of volume average diameter and analysis of atomization characteristics in electrostatic atomization minimum quantity lubrication

Minimum quantity lubrication (MQL) is a relatively efficient and clean alternative to flooding workpiece machining. Electrostatic atomization has the merits of small droplet diameter, high uniformity of droplet size, and strong coating, hence its superiority to pneumatic atomization. However, as the current research hotspot, the influence of jet parameters and electrical parameters on the average diameter of droplets is not clear. First, by observing the shape of the liquid film at the nozzle outlet, the influence law of air pressure and voltage on liquid film thickness (h) and transverse and longitudinal fluctuations are determined. Then, the mathematical model of charged droplet volume average diameter (VAD) is constructed based on three dimensions of the liquid film, namely its thickness, transverse wavelength (λh), and longitudinal wavelength (λz). The model results under different working conditions are obtained by numerical simulation. Comparisons of the model results with the experimental VAD of the droplet confirm the error of the mathematical model to be less than 10%. The droplet diameter distribution span value Rosin-Rammler distribution span (R.S) and percentage concentrations of PM10 (particle size of less than 10 µm)/PM2.5 (particle size of less than 2.5 µm) under different working conditions are further analyzed. The results show that electrostatic atomization not only reduces the diameter distribution span of atomized droplets but also significantly inhibits the formation of PM10 and PM2.5 fine-suspension droplets. When the air pressure is 0.3 MPa, and the voltage is 40 kV, the percentage concentrations of PM10 and PM2.5 can be reduced by 80.72% and 92.05%, respectively, compared with that under the pure pneumatic atomization condition at 0.3 MPa.

Stress-Induced Deformation of Thin Copper Substrate in Double-Sided Lapping

Double-sided lapping is an precision machining method capable of obtaining high-precision surface. However, during the lapping process of thin pure copper substrate, the workpiece will be warped due to the influence of residual stress, including the machining stress and initial residual stress, which will deteriorate the flatness of the workpiece and ultimately affect the performance of components. In this study, finite element method (FEM) was adopted to study the effect of residual stress-related on the deformation of pure copper substrate during double-sided lapping. Considering the initial residual stress of the workpiece, the stress caused by the lapping and their distribution characteristics, a prediction model was proposed for simulating workpiece machining deformation in lapping process by measuring the material removal rate of the upper and lower surfaces of the workpiece under the corresponding parameters. The results showed that the primary cause of the warping deformation of the workpiece in the double-sided lapping is the redistribution of initial residual stress caused by uneven material removal on the both surfaces. The finite element simulation results were in good agreement with the experimental results.

Analisis Kinerja Mesin CNC Turning SL 25 Mori Seiki Ditinjau dari Nilai Kebulatan Benda Kerja di Bengkel Mekanik Polman Babel

In production machining process is something that must be prioritized. One factor that affects the quality of production is the condition of the machine tool. The Mori Seiki SL 25 CNC Turning Machine is a machine that has existed since 1998 at the Polman Babel Mechanic Workshop and has been used for production processes and learning facilities for approximately 25 years. The use of machine tools at that time can affect the machine's geometric conditions, affecting the quality of the product produced. Roundness is one of these geometric conditions because many components with round cross-sections affect the components' function if deviations occur. Deviations in the roundness value on the machine can be known by testing the roundness value of the machining workpiece. From these tests, geometric errors in the machine and the feasibility of the machine in processing workpieces at specific size tolerances can be seen. The workpiece resulting from the machining process will be measured for its roundness value and then analyzed using the Minimum Zone Center (MZC) method. The test results on the geometric conditions of the machine are that the highest roundness deviation is at position 1 with an average of 21.7 μm which is the outermost position of the clamping area. The slightest deviation value is at position 6 with an average of 10.9 μm, and the machine can produce workpieces with a tolerance above 21 μm.

Feedforward compensation of contour errors in robotic machining system using compliance model

Industrial robots are receiving attention as alternatives for large machine tools owing to their high flexibility, large workspace, and cost-effectiveness. However, their low machining accuracy hinders their adoption for machining processes. The major error source is its compliance error caused by the cutting force resulting from the low stiffness of the robot manipulator. To increase the machining accuracy, an external measurement system and a compliance model were used to measure or estimate the compliance errors, and a control algorithm was subsequently applied to compensate for those errors. However, the control algorithm in terms of improving the dimensional accuracy of the workpiece has not been presented thus far. Unlike previous methods that deal with each axis compliance error separately, in this study, a contour control algorithm is proposed to effectively improve dimensional accuracy of the machined workpiece. The compliance model and a force torque sensor were used to predict the actual position of the cutting tool, and the contour errors were calculated using the interpolated reference positions and the predicted actual positions. A feedforward controller was designed to compensate for the estimate contour errors by modifying the reference positions. Experiments were conducted to evaluate the effectiveness of the proposed algorithm.

Application of coolants during tool-based machining – A review

Coolant is a substance that applied in a machining process for the efficient machining of materials. The application of coolants is based on the several factors including the types of machining process, workpiece material, cutting tool and cost. Coolant helps to dissipate the heat that can be generated during the machining operation, induce lubricating effects to decrease the friction caused by the interfaces of two surfaces, flush away chips and offer corrosion protection. With the right type of coolants used, the performance of machining applications and the attributes of workpieces can be remarkably enhanced. The objective of the study to provides a critical review on the mechanism of coolant penetration, functions, variety of coolants, cooling actions, effectiveness, applications, and the additives that alter the ability and properties of coolants. Furthermore, the critical review also addresses the new technology cryogenic machining that uses cryogenic gases as coolants instead of conventional coolants.

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.

Straightness Geometric Error Assessment for CNC Milling Machine

The straightness movement error of the machine tools axis contributes significantly to the straightness of the workpiece machining feature. This paper focuses on the assessment study of CNC machine tools’ straightness geometric error for obtaining recommendation information to improve machine geometric accuracy. A research method by determining measurement parameters according to ISO 230 procedure, no-load measurement of straightness vertical-horizontal geometric error using a laser interferometer, collecting data, data analysis. Data analysis calculates positional straightness deviation, mean positional deviation, systematic positional deviation, repeatability, and accuracy of straightness movement for each machine axis, and generating error compensation values for improving machine geometric error. The travelled distance of the X, Y, and Z-axis CNC milling machine tested is about 1100 mm, 560 mm, and 520 mm. The assessment result shows mean deviation straightness horizontal of X, Y, and Z-axis is 4.14 μm, 3.41μm, and 0.95 μm. The mean deviation straightness vertical of X, Y, and Z-axis is 3.75 μm, 2.63 μm, and 2.30 μm. Finally, the assessment outcome is generating error compensation values of each axis. It could be recommendation information for setting back error compensation parameter on CNC controller, hence the mean deviation of straightness geometric error machine tools to be less than 1 μm.

An Improved ResNet-1d with Channel Attention for Tool Wear Monitor in Smart Manufacturing.

Tool wear is a key factor in the machining process, which affects the tool life and quality of the machined work piece. Therefore, it is crucial to monitor and diagnose the tool condition. An improved CaAt-ResNet-1d model for multi-sensor tool wear diagnosis was proposed. The ResNet18 structure based on a one-dimensional convolutional neural network is adopted to make the basic model architecture. The one-dimensional convolutional neural network is more suitable for feature extraction of time series data. Add the channel attention mechanism of CaAt1 to the residual network block and the channel attention mechanism of CaAt5 automatically learns the features of different channels. The proposed method is validated on the PHM2010 dataset. Validation results show that CaAt-ResNet-1d can reach 89.27% accuracy, improving by about 7% compared to Gated-Transformer and 3% compared to Resnet18. The experimental results demonstrate the capacity and effectiveness of the proposed method for tool wear monitor.

Study on the effect of air and argon assisted short electric arc machining on Ti6Al4V titanium alloy

The use of an argon–water medium rather than air–water medium is proposed to increase the material removal rate (MRR) and decrease surface oxide production for short electric arc machining. To provide a more stable discharge state during high-temperature processing, this approach uses argon to reduce the production of highly resistant refractory materials (e.g. titanium oxide). For short-arc machining of Ti6Al4V titanium alloy, the effects of the voltage, milling depth, electrode speed and gas flow rate on the MRR, relative electrode loss rate and surface roughness were compared for air–water and argon–water mixtures. It was discovered that the milling depth and voltage had the greatest impact on the MRR, voltage and electrode rotational speed had the greatest impact on the relative tool electrode wear rate (REWR), and voltage had the greatest impact on the surface roughness. In contrast to the air–water mixture, the MRR with the argon–water working medium was as high as 9391 mm3/min, with a 13% improvement in MRR, as well as a smooth and flat surface of the machined workpiece. Nevertheless, the REWR increased to 2.46% and the surface roughness was at least 432 μm with the argon–water medium, increasing by 59 μm compared with the air–water medium. Moreover, the addition of argon reduced the types of surface compound on the workpiece and decreased the residual stress defects, including the recast layer, microcracks and holes.

Robot machining of thin-walled workpieces with automatically reconfigurable fixturing through feature analysis

Machining of large scale thin-walled workpieces pose high requirements for fixturing because of large stiffness changes due to material removal. Automatically reconfigurable fixtures that allow in-process reconfiguration are a promising approach. In this paper, an efficient approach based on feature analysis is proposed for fixture reconfiguration planning. For this, material removal is simulated. At discrete points in time, feature analysis is performed and an approximated local stiffness map is calculated with weight functions derived from the features’ parameterizations. Viable fixture configurations are then identified through root finding and validated through simulation in Ansys and experimentally with a fixture jig. In both validations, workpiece deformation for drilling operations could be reduced to an acceptable level enabling the implementation's future use in industrial application.

Maximizing MRR of Inconel 625 machining through process parameter optimization of EDM

Past decade witnessed a growing trend of electrical discharge machining (EDM) process, laying a strong consideration on it. EDM is suitable for material subtraction from hard to machine materials by electric spark between work material and tool used as electrodes. Besides the reduction of material from workpiece in machining, the process leads to the removal of a certain amount of tool material as well. There is always a quest for achieving economy in machining through increased material removal rate (MRR) using optimum machining process parameters. In this paper an experimental exploration is presented for optimizing process parameters (factors) like time of pulse on (Ton), input current (Ip) and time of pulse off (Toff) to maximize MRR for subtracting material from Inconel 625 work part using Taguchi experimental design (DoE) and its analysis.

On-Machine Measurement System of Tool Topography Using Edge Reversal Principle

Worn tools would wreak havoc on the surface integrity of workpieces in precision machining. This paper develops a new system for on-machine measurements of tool topography with micron accuracy based on the edge reversal principle. An array of experiments has been carried out to verify the system performance amenable to measuring various tools. An initial database of tools has been established as well for rapidly calculating tool wear and recording the topography characteristics of the different stages of tool wear. The on-machine measurement system is capable of achieving an expanded measurement uncertainty of 0.6 μm for tools with corner radii from 200 μm to 800 μm. The work provides equipment and data basis for efficient on-machine measurement of tool topography, which is beneficial to the prediction and management of tool life intelligently.

Numerical and experimental determination of contact heat transfer during orthogonal cutting

During the cutting process, a large part of the mechanical work is converted into heat, which is dissipated to the environment via the chip, the tool and the workpiece in dry machining. The heat flux partition in the cutting zone depends on the contact heat transfer between the workpiece and the tool. In most cutting simulations to date, the contact heat transfer is assumed to be thermal resistance-free contact, which leads to an overestimation of the heat flux into the tool. In this work, a pressure and temperature dependent contact heat transfer model was derived and implemented in the numerical cutting simulation. The model parameters for dry machining of AISI 1045 with carbide tools were calibrated experimentally. The heat distribution determined with the model provided assured accuracy for orthogonal machining with low uncut chip thickness and thus established a basis for the investigation of the process parameters on the tool temperature.

Online Chatter Identification for Thin-Walled Parts Machining Based on Improved Multisensor Signal Fusion and Multiscale Entropy

Thin-walled workpieces frequently appear to chatter during milling, causing the creation of vibration patterns on the surface of the workpiece, which affects the machining quality and workpiece performance. Identification of online chatter is important for thin-walled parts. The vibration signal associated with chatter changes dynamically throughout the milling process due to changes in tool position, removal of workpiece material, and transmission-related vibration signal attenuation characteristics, making reliable chatter identification difficult. This research proposes a unique online chatter identification approach for the machining of thin-walled parts based on improved multi-sensor signal fusion and multi-scale entropy. In order to acquire the fused vibration signals, the vibration signals produced during the machining of thin-walled parts are first acquired by sensors at various test sites and adaptively given weight coefficients by the improved Hausdorff distance and distance factor. Second, using the multi-scale sample entropy (MSSE) and multi-scale weighted permutation entropy (MSWPE), the eigenvalues of the fused vibration signals are obtained. Finally, the trained Logistic regression (LR) classification model is used to determine the vibration status of thin-walled parts. The analysis’s results demonstrated that the technique can properly identify the vibration state of thin-walled parts, and that the fusion signal can reflect the vibration state at the tool contact site more accurately.

Modeling of microstructural workpiece rim zone modifications during hard machining

Surface integrity determines, among other factors such as tool material and wear, the chemical composition and microstructure of the workpiece material as well as selected cutting parameters, the performance of machined parts. The aggressive nature of the cutting process influences the surface integrity and causes altered microstructure on the workpiece surface. Most popular example is the White Layer formation, which reduces the fatigue resistance of components. To study the influence of cutting parameters on White Layer thickness for a steel of grade AISI 4140, in this paper, a physics-based dislocation density-based model is implemented to predict the White Layer formation under the assumption of dynamic recrystallization. As a material model, a modified coupled damage mechanics model based on Johnson–Cook is implemented, which considers in comparison to the original one, damage induced softening using damage initiation and fracture indicators as well as strengthening mechanisms through grain refinement and dislocation damping at high strain rates. Dry orthogonal cutting is modeled via Coupled-Eulerian–Lagrangian method in Abaqus/Explicit. The developed model is validated based on experimental data regarding grain size and White Layer thickness. The model overestimated the grain size on the machined workpiece rim zone by a factor of 2–4, while the White Layer thickness was predicted with an error of max. 0.36 μm.

An overview of material removal processes and its importance

Good surface finishing, quality, and maximizing metal removal are characteristics feature of machining operation. The process variables affect the product’s surface roughness and rate of metal removal. Proper management of the process variables makes it easier to lower machining costs and enhance product quality. In order to get the best product, extensive research has been done previously to optimize the procedure specifications in any machining process. This paper studies how important the material removal process is, its benefits, and its relevancy in the manufacturing industries. The most recent turning process research uses the Response Surface Methodology (RMS). Process variables including feed, cutting speed, and depth of cut work well. In most circumstances, a technical need for mechanical products, the surface profile, and the roughness of a machined workpiece are two of the most significant product quality features. The necessary surface quality must be obtained for a part to function as intended. Finding a simple solution and a completely reliable approach is crucial due to the surface quality mechanism’s process dependence and the various uncontrollable elements that affect relevant phenomena.