Properties Of Workpiece Research Articles

Synergistic densification mechanism of irregular Ti powder during CIP: 3D MPFEM simulation with real-shape particles

Achieving high green density is essential for ensuring the mechanical properties of powder metallurgy workpieces. However, the densification behavior of irregular ductile powders remains unclear due to oversimplification in current numerical simulations. To address this, a three-dimensional multi-particle finite element method model incorporating realistic powder morphology, size distribution, and stacking structure is constructed. It is found that a pivotal motion-deformation synergistic stage exists between the conventional particle rearrangement and elastic–plastic deformation stages. In this stage, plastic deformation is driven by the spheroidization of coarse powders, whereas the resulting vacancies in the stacking structure facilitate slight particle motion. Thereafter, plastic deformation is dominated by the flattening of fine particles, and the pore-filling capacity decreases due to the reduction in non-contact surface area. This synergistic and complementary interaction between the spheroidization of coarse powders and the flattening of fine powders enhances mechanical interlocking and promotes micropore closure. As a result, the micropores exhibit a tendency of downsizing and homogenization, substantially boosting the potential for achieving full densification during sintering. Based on these findings, a method for determining the optimal forming pressure is proposed, considering the manufacturing costs of powder compacts and the characteristics of the micropores in both green and sintered bodies.

Mechanical properties of workpieces based on 3D printing

Mechanical properties of workpieces based on 3D printing

Nitriding layer depth detection based on mixing frequency nonlinear ultrasonic parameters

Nitriding treatment can improve the surface properties of workpieces, thus increasing the service life of the workpiece. The depth of nitriding layer is not only one of the important indexes for evaluating the nitriding effect, but also an important factor affecting the end-use performance of the workpiece. While the existing hardness and metallographic methods cannot meet the needs for non-destructive testing of nitriding layer depth in shaft parts. Therefore, a method using non-linear ultrasonic testing technology is proposed for non-destructive evaluation of nitriding layer depth. In this study, 1045 steel shaft specimens with different nitriding layer depths were prepared by a liquid salt bath nitriding method. The total depth of the nitriding layer was measured using a microhardness tester, and metallographic microscopy was applied to observe microstructure changes before and after nitriding treatment. With the proposed non-destructive method, the longitudinal critically refracted (LCR) wave mixing detection model was established and the ultrasonic nonlinear coefficients were used for characterizing the nitrided layer depths. Experimental results show that the LCR wave sum frequency (LCRWSF) detection model better characterizes the nitriding layer depth of 1045 steel and has higher sensitivity. As a result, the LCRWSF model is more suitable to efficiently estimate the nitrided layer depth.

Multi-response optimization of M2 steel coatings deposited by co-axial laser cladding on A2 steel tool surfaces

Laser cladding is a highly effective technique used in additive manufacturing to enhance the surface properties of workpieces. It is employed to improve wear resistance, corrosion resistance, and high-temperature resilience of materials. This study explores the utilization of laser cladding technology to repair the surface of AISI A2 tool steel by applying a coating of M2 steel. Sixteen experiments were designed using an orthogonal methodology to investigate the intricate relationship between various processing parameters, including laser power, scan speed, powder feed rate, and overlapping ratio. These parameters were examined in conjunction with key mechanical properties of the coating, such as micro-hardness, friction-wear characteristics, and shear bond strength.Additionally, analytical techniques such as Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDS), and X-ray Diffraction (XRD) were employed to gain insights into the microstructure of the coatings and elucidate the underlying failure modes. Shear testing of the coatings indicated a tendency towards a brittle fracture mode within the coating, with the dominant wear mechanism involving a combination of abrasive and oxidative wear.Finally, a TOPSIS-Grey Relational Analysis (GRA) method was utilized to identify the optimal process parameters. These optimal parameters were determined to be a laser power of 1200 W, a scan speed of 5 mm/s, a powder feed rate of 14 g/min, and an overlapping ratio of 30 %. Subsequent validation experiments carried out with these parameters demonstrated superior performance compared to the optimal group identified in the orthogonal experiment.

Increasing of mechanical properties of aluminum alloy AMg5 obtained by additive growth due to its modification

The results of increasing the mechanical properties of aluminum alloy workpieces produced by 3D printing are presented by modifying the metal with dispersed zirconium particles in the process of additive growth of workpieces using electric arc surfacing wire (WAAM technology), using the AMg5 alloy as an example.

Sensitivity of process signals to deviations in material distribution and material properties of hybrid workpieces

Hybrid components, made of multiple materials, can meet the increasing demands for lightweight construction and functional integration in the automotive and aircraft industry. Hybrid semi-finished components are produced by applying a high-alloy cladding to a low-alloy base material before hot-forming and machining the workpiece. Throughout this process chain, workpiece deviations in the form of material distribution and material properties can occur that influence the component’s lifetime. This paper investigates whether such workpiece deviations can be detected within the process chain by analyzing process signals obtained from subsequent process steps. For this purpose, artificial workpiece deviations were introduced to hybrid semi-finished workpieces made of C22.8/X45CrSi9-3. Then, process signals during forming and machining were analyzed to determine their sensitivity to the artificial deviations. The results revealed that deviations in cladding size can be effectively monitored using signals from both forming and machining. Cladding position deviations can only be detected during machining, while forming signals are more responsive to detecting the introduced hardness deviations of approx. 100 HV0.1.

Frictional behavior of pure titanium thin sheet in stamping process: Experiments and modeling

Friction behavior is crucial for metal deformation and failure analysis. Tests on contact between specimens and rigid mold (S-S) and rubber (S-R) were conducted. Results show that S-S friction coefficient decreases with normal pressure, while S-R static friction coefficient increases and kinetic remains low. Friction coefficient is influenced by real contact area changes due to softer surface deformation. S-S friction depends on specimen strength, and S-R friction on rubber properties. A proposed friction model based on contact area variation accurately predicts friction changes with normal pressure and workpiece properties. Integrated with finite element simulation, the model improves efficiency and accuracy compared to classical Coulomb friction model.

Recent Trends and Developments in the Electrical Discharge Machining Industry: A Review

Electrical discharge machining (EDM) is a highly precise technology that not only facilitates the machining of components into desired shapes but also enables the alteration of the physical and chemical properties of workpieces. The complexity of the process is due to a number of regulating factors such as the material of the workpiece and tools, dielectric medium, and other process parameters. Based on the material type, electrode shape, and process configuration, various shapes and degrees of accuracy can be generated. The study of erosion is based on research into processing techniques, which are the primary tools for using EDM. Empirical knowledge with subsequent optimization of technological parameters is one of the ways to obtain the required surface quality of the workpiece with defect minimization, as well as mathematical and numerical modeling of the EDM process. This article critically examines all key aspects of EDM, reflecting both the early foundations of electrical erosion and the current state of the industry, noting the current trends towards the transition of EDM to the 5.0 industry zone in terms of safety and minimizing the impact of the process on the environment.

MD simulation study on the microstructural evolution of single-crystal Fe with pre-existing defects by Cr ion implantation

In this study, we present the molecular dynamics simulation of metal ion implantation, investigating the strengthening mechanism from a microstructural perspective. A single crystal Fe with pre-existing defects is constructed based on actual dislocation density and the effects of Cr ion implantation are investigated. Additionally, nanoindentation simulations are conducted to analyze the mechanical properties of workpieces with and without ion implantation. Effects of implantation energy and dose on the microstructure and hardness of workpieces are analyzed. The results indicate that (i) ion implantation introduces FeCr bonds with higher bond energy while reducing the original FeFe bond energy, (ii) increasing implantation energy and dose corresponds to a greater number of point defects and a higher amorphous structures proportion, (iii) the microstructure transformed by ion implantation promotes dislocation nucleation during nanoindentation, (iv) ion implantation enhances workpiece hardness due to the dual effects of interstitials impeding dislocation motion and the heightened mutual obstruction attributed to a higher dislocation density. Notably, the hardness initially enhances with increasing implantation energy and dose, followed by a slight decrease. The computational approach in this paper can serve as a valuable tool for studying the microstructural evolution of metal ion implantation by molecular dynamics simulations.

Prediction of time-varying dynamics and chatter stability analysis for surface milling of thin-walled curved CFRP workpiece

Machining curved surfaces for thin-walled components is always challenging. Due to the strong anisotropic features of carbon fiber reinforced plastic (CFRP), new challenges, such as prediction accuracy loss, have emerged for avoiding the chatter of machining thin-walled curved CFRP workpieces. This paper studied the influence of the anisotropic properties of CFRP workpieces and time-varying dynamics on surface milling stability. An improved damping and time-varying dynamics prediction model was proposed integrated with FEA and numerical calculation considering the change of cutting position, the materials removed, and the anisotropic features of CFRP. Then, the 3D stability lobe diagram (SLD) was achieved by considering the dynamic change at each cutter point on the machining path and dynamic cutting forces based on the instantaneous fiber cutting angle. Based on the obtained 3D SLD, CFRP milling chatter could be accurately avoided, and machining efficiency could be improved. A case study of milling a thin-walled toroidal surface was conducted for validation. In the machining process of CFRP, unlike homogeneous materials such as metals, the natural frequency of CFRP fluctuates nonlinearly with the change curve of material removal within a specific range. Under a higher material removal rate, the surface roughness of the machined surface has been reduced by 74%, Ra below 2.35 µm, based on the selected process parameters of the novel-established SLD, compared to the surface roughness, Ra close to 9.21 µm, obtained by the previous method without considering the time-varying dynamics.

Multisensor data fusion and machine learning to classify wood products and predict workpiece characteristics during milling

The wood industry demands advanced methods for material classification and workpiece characteristic modelling to enhance process monitoring and adaptive process control. This paper presents a sensor fusion approach that integrates data from acoustic emissions, airborne sound, and power consumption during the milling of solid wood and wood-based composites. The aims are to achieve accurate material classification and to model workpiece characteristics such as surface roughness or density. A design matrix was generated by extracting relevant features from the multimodal signals to serve as an input for the classification and regression algorithms. The tested classification approaches to differentiate between workpiece type demonstrated high precision with an average validation accuracy of 92.16 %. Regression models for predicting the surface roughness showed R2 values between 0.79 and 0.97. The density could be predicted with R2 values between 0.84 and 0.98. As a conclusion, workpiece types could be classified and important workpiece properties during machining, such as surface roughness and density, could be well described by using information from multiple sensors during machining.

Effect of Vibration Behavior in Low-Frequency Vibration Cutting on Surface Properties of Workpiece

The objective of this study was to determine the effect of vibration behavior on workpiece surface properties in low-frequency vibration cutting. The effects of the parameters that determine vibration behavior on surface roughness were quantitatively evaluated through a comparison with other cutting conditions. Furthermore, by clarifying how the surface properties of the workpiece, such as roughness, roundness, and cross-sectional curves, change depending on the vibration behavior, a search for optimal conditions for low-frequency vibration cutting was conducted. The best surface properties were obtained under the condition of spindle rotation per vibration E=4.5. By using a value close to the minimum possible spindle rotation R=0.5 when the workpiece is retracted, it is expected to be effective in suppressing the variation in surface roughness at each phase angle; this variation is characteristic of low-frequency vibration cutting. Workpieces machined under low-frequency vibration conditions such as (E=2.5, R=1.0) and (E=3.5, R=1.0) were found to form characteristic surface patterns on the workpiece surface owing to a phenomenon in which the depth of the cut to the workpiece changes.

EXPERIMENTAL DETERMINATION OF THE OPTIMUM CUTTING TOOL FOR CNC MILLING OF 3D PRINTED PLA PARTS

The purpose of this study is to determine the most suitable cutting tool for achieving the desired diameter dimensions in parts produced using PLA material in a 3D printer. A plastic plate in the shape of a rectangular prism with dimensions of 90x40x10 mm was printed without holes with a 100% filling ratio in a 3D printer. A belt-pulley mechanism requiring bearing assembly was designed, manufactured, and tested for applicability. The study successfully identified the optimum parameters for achieving a 17 mm diameter measurement with the desired tolerance in PLA material without causing melt damage. These parameters are spindle speed of 15000 rpm, feed rate of 500 mm/min, cutting depth of 0.5 mm, minimum end mill diameter of 10 mm, and 4 cutting edges. The study also found that the morphological properties of the PLA workpiece were affected by the cutting process of different diameter milling cutters. The findings of this study can be useful for improving the accuracy and efficiency of 3D printing and CNC milling processes. This study provides important insights into the appropriate cutting tool for this process of parts produced using PLA material. The identified optimum parameters can help reduce energy-time-raw material losses and accumulation of waste PLA material, which are some of the most important problems of manufacturing with 3D printers.

Effect of Contact Pressure on Strain Distribution during Compression-Type Bulk Forming Processes.

Inhomogeneity of the material properties of workpieces developed during compression-type bulk forming processes (CBFPs) is an important issue. The effect of contact pressure on the workpiece surface on the strain inhomogeneity in the workpiece was investigated to understand and reduce the formation of strain inhomogeneity during CBFPs. Workpieces fabricated via rod caliber rolling, rod flat rolling, plate flat rolling, and rod compression were analyzed and compared. The extent of strain inhomogeneity in a workpiece differs with the forming process, because the occurrence of macroscopic shear bands (MSBs) is dependent on the workpiece shape and tool design. A flat-rolled rod exhibits the maximum strain inhomogeneity, whereas a flat-rolled plate shows the minimum strain inhomogeneity. The occurrence of MSBs was influenced by the distribution of the normal contact pressure or compression stress. The MSBs were stronger when the contact pressure was higher in the edge region of the surface. For example, the flat-rolled plate exhibited weak MSBs due to the relatively uniform or higher contact pressure on the central region. In contrast, strong MSBs appeared in the flat-rolled rod and compressed rod, because the contact pressure in the edge region of these two processes was high. Thus, the strain inhomogeneity in a workpiece fabricated via CBFPs can be reduced by controlling the contact pressure distribution on the workpiece surface.

Multi-Zone Integrated Iterative-Decoupling Control of Temperature Field of Large-Scale Vertical Quenching Furnaces Based on ESRNN

Temperature uniformity within a large vertical quenching furnace is the key factor to determine the properties of aluminum workpieces. The existing temperature control method for quenching furnaces cannot overcome the influence of multi-zone coupling issues, which lead to unstable product performance and a lack of key performance. Based on a workpiece temperature field model, a spatial-temporal dimensional extrapolation method is proposed to realize fast and accurate solving of the temperature model. In view of the over-burning and under-burning problems during the temperature rising period, a self-incentive nonparametric adaptive iterative control algorithm is presented, which realizes consistent temperature rising of multiple heating zones. Aiming at the strong coupling problem of the multi-zone heating manner during the temperature holding period, the decoupling problem of multiple control loops is converted into a multi-loop integrated control optimization problem. An eigenvector self-update recurrent neural network (ESRNN) is constructed to determine the Jacobian information and tune the control parameters of each loop controller in real time, thereby realizing the integrated intelligent decoupling control of multiple heating loops. Simulation and industrial results verify the superiority of the proposed method, which can realize high-precision and high-uniformity control of a large-scale temperature field and effectively improve the quality and performance of aluminum alloy workpieces.

An improved thermal model for the orthogonal cutting considering non-uniform heat partition of shear heat source

Temperature distribution and heat partition have a significant impact on tool life and machined surface integrity in metal cutting process. In this work, the concept of non-uniform heat partition of the shear heat source on the shear plane was proposed, and an improved cutting thermal model was proposed based on this concept. The improved thermal model had a continuous temperature field at the shear plane compared with the previous thermal model. Comparing the thermal model before and after improvement with the finite element model (FEM), it was found that the temperature distribution of the improved thermal model was closer to the FEM. In addition, a calculation method for the heat partition coefficient of the shear heat source considering mass transfer and the ratio of the heat flow entering workpiece, chip and tool to the total heat generated during the cutting process were proposed based on the improved thermal model. The results were compared with the regression models proposed before, and showed that the shear angle is an indispensable factor for analyzing the heat flow in the cutting process when the thermophysical properties of workpiece remain unchanged.

Overview of Machinability of Titanium Alloy (Ti6Al4V) and Selection of Machining Parameters

Abstract Machining of titanium alloy Ti6Al4V is a challenging task for the industry; however, there are some solutions to overcome these difficulties. One of those is optimizing the machining parameters. Machining of Ti6Al4V made by additive manufacturing is an emerging future and is even more difficult when comparing to standard Ti6Al4V alloy. There is lot of invention on Ti6AL4V 3D printed samples, but influence of machining post-printing is lacking. In additive manufacturing of Ti6Al4V alloy, it is necessary to make a finishing operation to improve the surface quality and to ensure precise geometry tolerances. During this process, it may affect the workpiece properties such as microhardness, microstructure, internal defect distribution, internal stresses. During printing there are lots of stresses created, heat treatment is done to normalize the parts. Machining (using milling machine) also causes internal stresses which can damage the surface and part itself. Optimisation of machining parameters and printing parameters can solve this issue. This study gives an overview of selection of machining parameters by considering all the previous relevant research.

Automated process planning in milling of hybrid components

Hybrid material composites can meet the increasing demands for high strength and low weight due to their different workpiece properties. Usually, hybrid components require post-machining after their fabrication. Due to the different material properties, new challenges arise in the machining process. It is essential to recognize the course of the material boundary in order to adapt the process planning accordingly and to enable a uniform material transition during machining. This paper presents a method for automated material recognition and automatic adaptation of the process parameters considering a uniform force level during the milling of hybrid materials. This way, the load on the milling tool in the material transition area can be reduced by up to 71%, which prevents premature tool failure. An optical laser line scanner is used to localize of material transitions within hybrid components. This enables a digital mapping of the material distribution in the discretized workpiece model. In combination with an empirical force model, it is possible to predict the cutting forces of the different materials and determine the material transition area for adapting them to specified target values.

Predictive Quality Modeling for Ultra-Short-Pulse Laser Structuring utilizing Machine Learning

Laser structuring offers precision and versatility for material processing but holds potential for optimization due to high-energy consumption and long production-times. Based on a process parameter study, we utilize Machine Learning and multi-modal data fusion of process parameters, high-frequency monitoring data and workpiece properties. We perform a benchmarking to analyze how data and algorithm characteristics impact modeling accuracy. We show that given enough data, ensembles achieve high accuracy and robustness and observe that accuracy strongly depends on initial workpiece properties. The higher the influence of laser structuring, the more superior is the inclusion of time-series features extracted from monitoring data.

Acoustic emission monitoring of immersion quenching bath attributes

This paper describes the use of acoustic emission (AE) testing for monitoring immersion quenching bath attributes. Immersion quenching is a heat treatment process used to change the mechanical properties of metallic workpieces. The quenching bath cooling attributes degrade with prolonged use, thus affecting the workpiece mechanical properties. A non-destructive monitoring method of the quenching bath is required. An AE measurement system is proposed for this purpose. Steel specimens are quenched in tap water, deionized water, 5 % NaCl-water solution and 5 % detergent-water solution. AE is measured with four piezoelectric sensors. The influence of quenching bath attributes on specimen cooling is described together with the measured AE signals. AE signal peak amplitude diagrams are evaluated. A reference AE peak amplitude signal is presented with the measurements. The quenching bath attribute assessment method is established by monitoring the amount of outlying AE signals in regard to the reference AE signal. The results confirm that AE monitoring provides a decent way of assessing quenching bath attributes and thus also the quenched specimen mechanical properties.