Machining-induced Residual Stress Research Articles

Research on distortion in boring process of large-size main bearing holes in marine diesel engine body

Machining-induced residual stress (MIRS) in thin-walled components affects their machining accuracy, especially for large-size thin-walled components. This study focuses on the bodies of marine diesel engines, exploring the distribution of MIRS and distortion caused by the gravity-coupled machining residual stress during the boring process of the main bearing hole. The research obtained the distribution of MIRS and the machining distortion based on the finite element method and the mapping method. It examined the influence rules of various parameters, such as the cutting speed, feed, and depth of cut, on MIRS and machining distortion. The study shows that cutting speed, feed, and depth of cut are vital factors affecting MIRS and machining distortion. For the machining distortion of large-size and thin-walled components, their own weight is an essential factor that cannot be ignored. By optimizing the wall thickness, the distortion range can be effectively controlled, supporting the lightweight design of the structure.

Machining-induced residual stress and deformation during Mg–Li alloy thin-walled rib-web parts milling

The deformation of thin-walled parts based on the lightweight Mg–Li alloy materials caused by machining-induced stress is an important factor hindering the further development of lightweight in the aerospace field. Prediction of the machining-induced residual stress is the key to understand the deformation and derive low-stress machining scheme. In this study, the equivalent cutting deformation loads were applied to substitute the strongly coupled thermal-mechanical behavior between the tool and part to realize the prediction of the machining-induced stress and deformation of thin-walled rib-web part under different milling conditions. The local regular and complex rib-web parts share a same deformation mode, both behave as in-plane bending deformation of the machined surface. The low-stress milling scheme of up milling combined with cryogenic cooling obtained based on the local regular four rid-web thin-walled part was applicable to the prediction of the complex Mg-Li alloy disk rib-web part. Compared with the initial scheme, the maximum stress and deformation displacement based on the low-stress scheme were reduced from 80 to 36 MPa and from 0.04060 to 0.00814 mm, respectively. Finally, the effectiveness of low-stress milling scheme were verified based on the milling and measurement experiments of the complex thin-walled disk rib-web part. These results indicate the effectiveness of the modeling method for thin-walled rib-web parts and prove the results obtained based on the local model can be extended and applied to large complex parts with the same structural type.

In-Process Machining Distortion Prediction Method Based on Bulk Residual Stresses Estimation from Reduced Layer Removal

Manufacturing structural monolithic components for the aerospace market often involves machining distortion, which entails high costs and material and energy waste in industry. Despite the development of distortion calculation and avoidance tools, this issue remains unsolved due to the difficulties in accurately and economically measuring the residual stresses of the machining blanks. In the last years, the on-machine layer removal method has shown its potential for industrial implementation, offering the possibility to obtain final components from blanks with measured residual stresses. However, this measuring method requires too long an implementation time to be used in-process as part of the manufacturing chains. In this sense, the objective of this paper is to provide a machining distortion prediction method based on bulk residual stress estimation and hybrid modelling. The bulk residual stresses estimation is performed using reduced layer removal measurements. Considering bulk residual stress data and machining-induced residual stress data, as well as geometry and material data, real-part distortion calculations can be performed. For this, a hybrid model based on the combination of an analytical formulation and finite element modelling is employed, which enables us to perform fast and accurate calculations. With the developments here presented, the machining distortion can be predicted, and its uncertainty range can be calculated, in a simple and fast way. The accuracy and practicality of these developments are evaluated by comparison with the experimental results, showing the capability of the proposed solution in providing distortion predictions with errors lower than 10% in comparison with the experimental results.

Prediction of five-axis machining-induced residual stress based on cutting parameter identification

The performance of the machined surface is significantly affected by the machining-induced residual stress (Rs), which should be well predicted for better regulation. However, the real-time factors, such as positioning error, and installation error, will make the actual cutting parameters (ACP) deviated from the designed cutting parameters (DCP), and decrease the Rs prediction accuracy. Thus, this paper proposes a novel cutting parameter identification method to improve the prediction accuracy of five-axis machining-induced residual stress. Firstly, the cutting parameter (the cutting width is used in this paper) is identified inversely by the real-time cutting force, which provides input parameters for the accurate Rs prediction. Then, the mechanical stress and the thermal stress are recalculated by the identified cutting parameters to improve the prediction accuracy. Finally, the loading conditions are determined by considering the effects of cutter postures, and the Rs prediction model is established in five-axis milling. Based on the experimental validation, the identified cutting parameters (ICP) are more closely to ACP. For example, the mean error of the identified cutting depth decreases from 0.075 mm to 0.03 mm, and the error rates of simulated temperature rise are significantly reduced by 68.8 %. The Rs prediction error rate obtained by ICP significantly decreases by 48.1 %. The proposed method improves the Rs prediction precision by inversely identifying the cutting parameter with the real-time cutting force. It benefits real-time control of Rs for the better surface quality of machined parts.

Correlation between machining-induced surface alterations and stress corrosion cracking susceptibility of au stenitic stainless steels

Correlation between machined surface alterations and stress corrosion cracking (SCC) susceptibility of austenitic stainless steels is investigated. The machining-induced residual stress, roughness, micro-hardness and dislocation density were characterized and the surface/subsurface cracks were examined after SCC tests in boiling magnesium chloride solution. The results showed SCC crack initiation and early propagation were highly related to the machining-induced alterations. By establishing a quantitative relationship between the surface characteristics and surface crack density, the comprehensive impact of the residual stress, surface roughness as well as the dislocation density on SCC susceptibility is revealed. The predicted crack density is in good agreement with the measured data. As a result, the SCC susceptibility of the machined surface is described by the surface integrity properties.

Progress in machining-induced residual stress and microstructural evolution of inhomogeneous materials and composites

Progress in machining-induced residual stress and microstructural evolution of inhomogeneous materials and composites

Determination of the Cutting-Edge Microgeometry Based on Process Forces during Peripheral Milling of Ti-6Al-4V Using Machine Learning

The residual stress state of the machined sub-surface influences the service quality indicators of a component, such as fatigue life, tribological properties, and distortion. During machining, the radius of the cutting edge changes due to tool wear. The cutting-edge rounding significantly affects the residual stress state in the part and the occurring process forces. This paper presents a tool wear prediction model based on in-process measured cutting forces. The effects of the cutting-edge geometry on the force behavior and the machining-induced residual stresses were examined experimentally. The resulting database was used to realize a Machine Learning algorithm to calculate the hidden value of tool wear. The predictions were validated by milling experiments using rounded cutting edges for different process parameters. The microgeometry of the cutting edge could be determined with a Root Mean Square Error of 8.94 μm.

Study of distortion on milled thin-wall aluminum parts influenced by initial residual stress and toolpath strategy

Monolithic aluminum alloy parts are highly required in the aeronautical industry, but they show significant geometrical distortion after the machining process. This work investigated the distortion attributed by the initial residual stress of the raw material and the machining-induced residual stress during the milling process, as well as exploring the effects of the machining toolpath strategy. Single-/multi-pocket parts were milled from 7050-T7451 aluminum blocks with different initial residual stresses, and an element deletion method was developed for the numerical study to simulate different sequences of material removal. It was revealed that the toolpath parallel to the long side of the block caused more distortion on the side surfaces of the final part. The value of distortion was positively correlated to the magnitude of the initial residual stress of raw material. The simulation results indicated that the distortion attributed by machining-induced residual stress accounted for about 15% of the final distortion. The finding promotes the design optimization of machining monolithic parts by minimizing distortion, thereby benefitting the application of large monolithic parts in industry.

The Influence of Tool Geometry Parameters on Thermo-Mechanical Loads and Residual Stresses Induced by Orthogonal Cutting of AA6061-T6: A Numerical Investigation

The residual stresses state that a mechanical part obtained after machining is a crucial factor that impacts its in-service performance. This stress state is influenced by the thermomechanical loads exerted on the parts during the machining process, which are, in turn, determined by the tool parameters, process, and machining conditions. The aim of the present research was to anticipate how the cutting tool’s edge radius, rake angle, and clearance angle would affect the forces, temperature, and residual stresses induced while orthogonally cutting aluminum AA6061-T6. To achieve this, two-dimensional DEFORM™ software was utilized to develop a finite element model. The residual stresses trend results obtained demonstrated that rake angles of 0° and 17.5–20° values with a small edge radius (5 to 10 µm) and clearance angles of 7 and 17.5° values gave higher compressive stresses. The obtained simulated results were in good agreement with the experiments. The cutting forces, the temperature, and the maximum and minimum machining-induced residual stresses were found to be influenced more by the tool edge radius and the tool rake angle. The influence of the clearance angles on the above-mentioned machining responses was the least. Residual stresses can have a significant impact on the in-service performance of machined parts. The obtained results will help engineers select or design tools that promote a desired surface integrity during machining. This task is not obvious in practice because of difficulties in measuring residual stresses and also because the machining parameters and the tool geometry parameters have different and opposite impacts on thermo-mechanical loads, productivity, and on machining induced residual stresses.

The Residual Stress Relief and Deformation Control of Al Alloy Thin-Walled Antenna Components by Ultrasonic Regulation

The residual stress fields of the initial billet and subsequent machining in the material bring great challenges to the precision machining and geometrical stability of aluminum alloy thin-walled components. To ensure that a certain type of large-sized aluminum alloy thin-walled antenna has a small flatness deformation during forming, this paper firstly employed the ultrasonic critical refraction longitudinal wave (LCR wave) detection method to measure the different depth ranges’ residual stress distribution of 5A06/6061/7075 aluminum alloy plate, both as blanks and after multiple milling. Additionally, the effects of inherent residual stress (IRS) and machining-induced residual stress (MIRS) on the subsequent milling deformation were analyzed. After that, combined with the self-developed ultrasonic stress relief (USR) system, the deformation control effect of a thin-walled plate after eliminating residual stress in each stage was tested. The results show that the ultrasonic stress relief treatment can quickly and efficiently eliminate the IRS and MIRS with small flatness deformation. By introducing the URS treatment in the blank, rough machining, and semi-finishing stages, the components before each subsequent machining are in a low-stress state, and the component deformation can be gradually controlled so that the final thin-walled antenna has a smaller flatness.

Parametric Characterization and Distribution Consistency Evaluation of Machining-Induced Residual Stresses

Abstract Uneven distribution of machining-induced residual stresses (MIRS) caused by dynamic cutting conditions and material internal properties has a significant impact on fatigue resistance, stress corrosion resistance, and accuracy retention of aerospace structural parts. Owing to the challenges in describing residual stress distribution property, this paper proposes a distribution consistency characterization model with high accuracy, as well as a quantitative evaluation strategy in milling titanium alloy. MIRS reduction surrogate model embedded with mechanism knowledge describes the general profile of MIRS in the explicit form. Compared with the surrogate model, the prediction accuracy of MIRS under Bayesian calibration is improved by more than 25.68%. To evaluate the MIRS distribution consistency, self-consistent indexes are innovatively proposed based on the extraction of four fundamental MIRS features in parametric form. The sensitivity analyses between three machining parameters and self-consistent indexes are carried out, guiding in selecting machining parameters in consideration of MIRS distribution consistency. Consequently, this work can provide a novel insight into determining the optimal machining parameters with comprehensive consideration of MIRS features magnitudes and distribution consistency. According to the proposed procedure, there exists a potential for future extensions to other materials and cutting processes with more complex stress distributions.

Physics-Informed Uncertainty Quantification in Modeling of Machining-Induced Residual Stress

Machining processes involve various sources of uncertainty which lead to inaccurate interpretation of results in the surface integrity of machined products. This work presents a physics-informed, data-driven modeling framework for achieving comprehensive uncertainty quantification (UQ) of the impact of process and material variability on machining-induced residual stress (RS). Uncertainty due to the variation in bulk material properties and model input parameters in machining are considered. Preliminary results showed that variations in calibration parameters have a substantial effect on modeling RS, while the variation in material properties has a smaller effect. Further research directions for UQ in machining are also outlined.

A novel surface residual stress monitoring method based on the power consumption of machine tool: A case study in 5-axis machining

The machining-induced residual stress significantly influences the service performance of machined parts and should be well monitored. However, the direct measurement method is time-consuming and only can be conducted after machining. Meanwhile, the existing prediction method rarely considers the effects of real-time factors on the machining parameters and cannot meet the requirement of online residual stress monitoring. Therefore, a novel surface residual stress monitoring method is proposed based on the easily measured online factor (power consumption of the 5-axis machine tool is used in the paper). Firstly, the power consumption of the machine tool (Pt) is used to model the mean effective cutting power (Pe¯). Secondly, the relationship between the surface residual stress in the feed direction (Rsx) and Pe¯ is established. Thirdly, Rsx is predicted based on Pt by combining these two models. Finally, a novel surface residual stress monitoring method based on Pt is proposed. The effectiveness of the proposed method is validated by various experiments, and the mean prediction error rate is only 9.5 %. From the case study, when the power consumption of machine power increases at a fixed spindle speed, Rsx changes from tensile stress to compressive stress. It provides a new method and platform for controlling the surface residual stress and greatly benefits the high-performance manufacturing of complex parts.

Experimental investigation and modelling of residual stresses in face milling of Al-6061-T3 using neural network

Milling process is a common machining operation that is used in the manufacturing of complex surfaces. Machining-induced residual stresses (RS) have a great impact on the performance of machined components and the surface quality in face milling operations with parameter cutting. The properties of engineering material as well as structural components, specifically fatigue life, deformation, impact resistance, corrosion resistance, and brittle fracture, can all be significantly influenced by residual stresses. Accordingly, controlling the distribution of residual stresses is indeed important to protect the piece and avoid failure. Most of the previous works inspected the material properties, tool parameters, or cutting parameters, but few of them provided the distribution of RS in a direct and singular way. This work focuses on studying and optimizing the effect of cutting speed, feed rate, and depth of cut for 6061-T3 aluminum alloy on the RS of the surface. The optimum values of geometry parameters have been found by using the L27 orthogonal array. Analysis and simulation of RS by using an artificial neural network (ANN) were carried out to predict the RS behavior due to changing machining process parameters. Using ANN to predict the behavior of RS due to changing machining process parameters is presented as a promising method. The milling process produces more RS at high cutting speed, roughly intermediate feed rate, and deeper cut, according to the results. The best residual stress obtained from ANN is ‒135.204 N/mm2 at a cutting depth of 5 mm, feed rate of 0.25 mm/rev and cutting speed of 1,000 rpm. ANN can be considered a powerful tool for estimating residual stress

Machining-induced residual stress analysis and multi-objective optimization for milling process of Mg–Li alloy

Machining-induced residual stress in an Mg–Li alloy can lead to a significant deformation of its machined thin-walled parts. To solve this issue, an integrated method that combined the grey relational analysis (GRA), back propagation (BP) neural network, and non-dominated sorting genetic algorithm-III (NSGA-III) was proposed in this study aimed to analyze the dominant mechanism of residual stress and achieve a super-objective cooperative optimization of residual stress in the LA103Z Mg–Li alloy milling process. A typical spoon-shaped distribution of surface tensile stress and subsurface compressive stress was presented in the Mg–Li alloy owing to the coupling interaction between the cutting force and temperature. The increase in the cutting temperature owing to an increase in the cutting speed was proven to be the overarching factor that induced residual stress by the correlation analysis of cutting parameters, force–heat of cut and residual stress. The BP prediction models of the residual stress were established, and the predicted values were in good agreement with the test values, with a maximum error of 5.07 %. The NSGA-III optimization algorithm was used to search for the lowest values of in-plane tensile and compressive stresses (σtx, σty, σcx and σcy). The optimized cutting speed, feed, and cutting depth were determined to be 61 m/min, 0.068 mm/z, and 0.58 mm, respectively. The maximum predictive error of four residual stresses was experimentally verified to be 15.94 %, which proved the feasibility of the multi-objective optimization approach. The residual stress level of the optimized scheme evidently decreased compared with that of the current milling scheme, with the maximum reduction of 28.37 %. Therefore, the propsoed approach can effectively reduce the risk of warping and distortion of machined Mg–Li alloy parts.

Coupled Eulerian-Lagrangian model for residual stress prediction in orthogonal cutting of Waspaloy

Residual stress is one of the most critical factors of machined surface integrity, which directly determines corrosion resistance and fatigue life of components. Therefore, it is necessary to accurately predict the machining-induced residual stress, especially for critical aerospace parts. In this paper, a finite element model based on the Coupled Eulerian-Lagrangian method is employed to predict the residual stress in orthogonal cutting of Waspaloy material under different cutting conditions. The depth profiles of residual stress along the cutting speed direction are determined using the X-ray diffraction and electropolishing techniques. The results show tensile residual stresses at the cutting speeds of 64 m/min and 86 m/min with 100 microns uncut chip thickness. The depth at the machined surface influenced by the residual stress is identified and compared from both simulations and experiments. The simulated cutting forces and residual stress distributions from the finite element model are compared with the experimental results to validate the model. In addition, the microstructure property at the machined surface is examined to determine the depth of plastic deformation layers. White layers are identified at the higher cutting speed, and it is shown that the formation of white layers is directly related to the location of the peak tensile residual stress.

Simulation based compensation techniques to minimize distortion of thin-walled monolithic aluminum parts due to residual stresses

Thin-walled milled monolithic aluminum workpieces are common in the aerospace industry, because of their advantages such as favorable material properties, established design methods, good machinability, known performance and reliable inspection techniques. Machining-induced residual stresses (MIRS) and initial bulk residual stresses (IBRS) are known to be the key factor for causing distortion of those workpieces. In this study the effect of both types, the milling path strategy and the wall thickness on the distortion is analyzed. A developed 3D linear elastic finite element (FEM) model, considering both types of RS as input, and predicting the resulting part distortion, is extended by using the true milling path as well as all RS contained in the entire workpiece. The simulation outcome is validated by experiments and distortion minimization techniques are derived. The results show that the model predicts the distortion shape and level for different wall thicknesses and milling strategies with a high accuracy. The experiments indicated that the distortion of low IBRS parts with 3 mm wall-thickness could be minimized by 42% by only changing the milling path. The simulation highlighted, that for smaller wall thicknesses, the distortion minimization potential is even higher (e.g. 2 mm wall thickness: up to 69% distortion minimization). For high IBRS parts, milling the inverse form of the predicted shape on the backside of the workpiece represents an alternative, which led up to a reduction of the distortion by 77%. In general, three main categories for precontrol distortion techniques can be identified as the process parameters, the part topology and the process strategy.

Residual Stresses Induced while Machining Inconel-718 in Cryogenic Environment: Numerical Estimation

Nickel alloys are recognized to offer great strength, good corrosion resistance, and outstanding thermal stability when compared to typical materials, these features can be utilized in aerospace and turbine sectors. Nickel alloy is one of the most difficult to cut materials since it is having a high strain hardening propensity and low thermal conductivity. But due to the outstanding qualities of Inconel-718, these types of material cannot be ruled out, and hence they are being machined in a cryogenic environment. Many studies have been conducted in recent years to quantify and predict machining-induced residual stress. It has been concluded that the residual stress can be viewed as a function of machining parameters. However, during the cutting process, the combined effect of thermal and mechanical loads has a direct impact on the stress field. Despite conducting experimental studies, there is a significant requirement to build numerical models to simulate residual stress distribution on machined surfaces in order to identify appropriate machining settings. The effect of cryogenic cooling on residual stress has yet to be modeled. In a cryogenic environment, finite element modeling has been successfully used to forecast residual stresses caused by machining. Tungsten Carbide was used as a single-point cutting tool for 2D orthogonal cutting in a cryogenic environment using Abaqus CAE/6.14. In this research work, it has been observed that the geometrical characteristics of the tool and the cutting parameters affect the residual stress. The results provide the correlation between residual stress and the above parameters.

Numerical investigation of sequential cuts residual stress considering tool edge radius in machining of AISI 304 stainless steel

Residual stress affects component performance, and the existence of pre-stress changes the residual stress of machined surfaces as well, emphasizing the importance of studying the evolution of residual stress in sequential cutting. This paper reports a numerical investigation of the machining-induced residual stress profile of sequential cuts for orthogonal cutting of AISI 304, considering the effects of edge radius and cutting depth. A Coupled Eulerian-Lagrangian (CEL) model is employed for the first time to stably simulate the evolution of residual stress of multiple sequential cuts. The effectiveness of the proposed method is verified by comparing the chip formation and surface residual stress between simulated and experimental results. The cutting force and cutting temperature, as well as mechanical and thermal loads, are extracted to explain the generation and evolution of residual stress in sequential cutting. It is found that the residual stress on the machined surfaces will decrease during sequential cutting, and a stable value can be reached after approximately six sequential cuts. With the progress of sequential cutting, a larger honed tool edge radius and cutting depth will lead to a slower reduction of residual stress.

Numerical and experimental investigation on thermo-mechanically induced residual stress in high-speed milling of Ti-6Al-4V alloy

The machining-induced residual stress, which significantly influences the machined part's service performance and geometrical stability, is considered an essential indicator of surface integrity. The intermittent cutting process and the consequences of thermo-mechanical and microstructural phenomenon accompanying white layer formation, which directly influences residual stress distribution of the workpiece, make the milling process different from turning process. Therefore, a comprehensive understanding of residual stress distribution mechanism within the milled part is essential. The current study presents a numerical and experimental approach for in-depth residual stress prediction in the milled part of Ti-6Al-4V alloy. First, a simplified milling model was proposed based on J-C constitutive model and J-C failure criterion. Secondly, the proposed model was verified regarding cutting forces, cutting temperature, and chip morphological characteristics. An excellent correlation was obtained between the simulated and experimental results for given analogous milling conditions. Thirdly, the workpiece was allowed to cool down to room temperature for stress-strain relaxation. Finally, the effect of the white layer on residual stress distribution and the relationship between nano-hardness and residual stress were analyzed and discussed. It was found that residual stress was tensile when no white layer was detected on the machined surface, and the nature of residual stress becomes compressive with the formation of a white layer. This study can provide an in-depth understanding of residual stress distribution within the milled part and the fatigue life of the component's services performance.