Aero-engine Casing Research Articles

A new cause-mechanism independence estimation based cross-domain learning method for machining deformation prediction

Monitoring data-based machining deformation prediction is fundamental for accurate deformation control and product quality guarantee. For problems where involved unobservable variables like residual stress that can lead to data distribution bias, causal cross-domain learning methods have prominent advantages over other pure data-driven methods by shifting cause distributions and mechanisms. However, existing causal methods are based on the hypothesis that cause and mechanism are independent, which ignores the corresponding changes of mechanism across domains and can limit accuracies. This paper proposes a new causal cross-domain learning method based on cause-mechanism independence estimation, where the hypothesis is broken by taking the dependence of cause and mechanism into consideration. A cause-mechanism independence estimator is established by introducing the structural integral of mechanism derivative multiplies cause distribution, and the estimation value can measure the cross-domain changes of mechanism. As a result, the proposed method based predicting model can make efficient distribution shifts according to the estimation. The machining of aero-engine casings is taken as a case study, and experimental results show that the proposed method could predict the deformation well with limited target domain data. Besides, the proposed method can be readily extended to other cross-domain regression problems involved with unobservable variables.

Ground test modeling of cylindrical aero-engine casing under high-temperature and high-pressure aerodynamic load

The aero-engine casing is subjected to high-temperature and high-pressure aerodynamic loads during operation. It is essential to perform the ground strength tests for the casing. To optimize the ground strength test and to improve the accuracy of temperature and pressure loading during the test, a mathematical model of high-temperature and high-pressure aerodynamic loading system for simulating the ground strength test of typical aero-engine casing is established to study the actual temperature and pressure loads on casing during ground test. The temperature and pressure results calculated by the mathematical model under typical loads matched well with the experimental measurements. The verification results show that under the test conditions, the thermal inertia of the heater, the radiation heating of the gas in the test chamber by the heater, and the heat transfer of gas mixing in the pressure process can be ignored. The sensitivity analysis of the parameters affecting the accuracy of pressure and temperature loading is carried out. We show that the pressure can be controlled quantitatively by adjusting the inlet and exhaust pipe parameters, and accurate temperature control can be achieved by designing reasonable heater power and selecting appropriate heating control parameters.

Achieving low-porosity AlSi10Mg cladding layers for the additive repair of aluminum alloy parts by directed energy deposition

Directed Energy Deposition (DED) has broad application prospects for repairing damaged aluminum alloy parts such as piston aero-engine casing. However, due to the low laser absorptivity and the complex thermal cycle process, it is challenging to obtain aluminum alloy cladding layers with high-quality morphology and low-porosity by DED method. In this work, a series of individual stages of DED process are studied during single-tracks overlapping and layers stacking. Correspondingly, the within layer scanning strategy, inter-layer stacking strategy, and process parameters are examined carefully. Using the proposed DED processing flow that combines substrate preheating with intermittent process, the deposited layers without balling and collapse are successfully achieved. Furtherly, the bi-directional scanning strategy for single-tracks overlapping within layer is proposed to compensate the thickness nonuniformity caused by heat accumulation. The inter defects identified as “lack of fusion” and “gas pores” are observed in the cladding layers with uniform surface. By analyzing the pore formation mechanism and considering the energy distribution and re-melting, the multi-layer offset stacking strategy is proposed to inhibit the porosity to 0.25 %. Combined with the optimized process parameters, the porosity is reduced up to 0.09 %, ensuring the density of the DED deposition coating. This work provides new pathway and direct guidance for fabricating high-quality AlSi10Mg cladding layers during the DED repairing of valuable aluminum alloy parts.

Reaction mechanisms and mechanical properties of SiCf/SiC composite and GH536 superalloy joints using CoFeNiCrMn high-entropy alloy filler

To meet the service conditions and strength requirements of turbine stator blades and the inner and outer rings of aero engine casings, CoFeNiCrMn high-entropy alloy filler was used to braze SiCf/SiC/GH536 joints. This study investigated the effects of holding time on the joints' microstructure and mechanical properties. Key phases identified in the welded joints include MoNiSi, FCC, and Cr23C6 near the composites, with brittle Ni3Si and Fe2Si intermetallic compounds forming due to filler diffusion. Optimal brazing parameters were found to be 1220 °C for 30 min with a shear strength of 64.28 MPa. The study also highlighted that increased holding time at the same temperature enhances diffusion at the joint, increasing brittle intermetallic compounds, initially improving shear strength, which then declines. Microstructural and fracture morphology analyses revealed that insufficient insulation time leads to poor welding and stress concentration at pores, causing cracks. Excessive insulation time results in joint fractures due to the brittleness of Ni3Si and Fe2Si intermetallic. Thus, joint shear strength correlates with welding quality and intermetallic compound distribution.

Research on rapid stabilization of aero-engine casings subjected to shock loads using nonlinear energy sink

In this study, we study the application of nonlinear energy sinks (NES) for targeted vibration energy transfer and absorption in aero-engine casings subjected to shock loads. A simplified single-degree-of-freedom model with an attached NES is characterized to understand the influence of control parameters such as damping and nonlinear stiffness on the NES. Using the complexification-averaging (CX-A) technique, we analyze the main dynamic characteristics of the vibration system, revealing nonlinear normal modes (NNM) and the energy localization phenomenon that enables targeted energy transfer. Then we establish a thin-walled casing model with an NES and calculate its vibration energy transfer under shock load. The results of this study are as follows: (1) NNMs are related to initial energy, with energy localization leading to targeted vibration energy transfer and dissipation; (2) NES operates optimally within a specific energy domain, exceeding this domain reduces its effectiveness, which is primarily influenced by the NES’s nonlinear stiffness; (3) Optimizing the NES’s nonlinear stiffness on the thin-walled casing achieves targeted shock energy transfer and dissipation, significantly reducing the casing’s vibration amplitude (from 0.008 m to 0.003 m, a 62.5% reduction) and stabilization time (from 0.007s to 0.002s, a 71% reduction). The study’s results are instructive for achieving rapid stabilization of aero-engine casings under shock excitation, providing insights into the mechanism of NES and the impact of damping and nonlinear stiffness on its performance. This research guides the optimized design of NES for thin-walled casings to effectively dissipate shock-induced vibrations.

Accuracy analysis and improvement methods for revolving parts in counter-rotating electrochemical machining with continuous radial feeding

Counter-rotating electrochemical machining (CRECM) stands out as an efficient and cost-effective method for machining aero-engine casings. To maintain stability in the CRECM process equilibrium state, continuous feeding of the cathode tool is essential. However, this continuous feeding approach with single direction of rotation may result in poor roundness and reduced machining accuracy. This paper aims to analyze the sources and rules of roundness error and asymmetry in CRECM. Based on simulation results, a method involving periodic reverse rotation of electrodes with continuous feeding is proposed for CRECM. This approach aims to mitigate the cumulative machining error, thereby significantly improving machining accuracy. Experimental results validate the efficacy of the proposed method at a rotation speed of 0.2 rpm. The roundness error is markedly reduced from 0.19 mm to 0.04 mm, resulting in a more symmetric convex structure. Additionally, the sidewall taper angle is reduced from 14.5 degrees to 6.4 degrees. This underscores the effectiveness of the proposed method in enhancing machining accuracy.

A novel rotor dynamic balancing method based on blade tip clearance measurement without the once per revolution sensor

The existence of the aeroengine casing, limited monitoring points, and multi-fault characteristics make obtaining the rotor’s vibration transmission characteristics challenging, resulting in difficulties accurately identifying the rotor unbalance. This paper utilizes a high-frequency composite sensor to monitor the engine’s blade tip clearance (BTC) and extracts unbalanced information from BTC signals for rotor dynamic balancing, while avoiding the need for the once per revolution (OPR) sensor. First, the vibration characteristics of the rotor-blade system under multi-fault conditions are investigated. Then, based on BTC measurement, a none OPR method and an unbalance identification method are proposed, in which the radial vibration of the blade tip in the BTC signals at different speeds is extracted and operated in the time domain to obtain the rotor unbalanced vibration, the signal is reconstructed, and cross-correlation analysis is used to accurately identify the magnitude and phase of the unbalanced signal. Finally, a rotor test bench is utilized for experimental verification. The results reveal that the dynamic balancing method based on the BTC signal can more precisely identify the rotor unbalance than the traditional rotor dynamic balancing method. The application of this technique will effectively improve engine health management and fault prediction.

Laser metal deposition of AlSi10Mg for aeroengine casing repair: Microhardness, wear and corrosion behavior

Laser metal deposition (LMD) based remanufacturing leverage of the flexibility of additive manufacturing and provides an attractive and cost-effective means for repairing or remanufacturing high value engineering components. In this work, AlSi10Mg alloy was cladding on the surface of cast aluminum alloy plate for aeroengine casing, AlSi10Mg repair layer with porosity of only 0.19% was obtained when the laser power P = 1800 W, powder feeding rate f = 1.6 g/min, scanning speed v = 8 mm/s. Microhardness, wear and corrosion resistance tests were used to evaluate the repair feasibility of the AlSi10Mg repair layer. The results indicated that the microhardness of the AlSi10Mg repair layer was more than 80% of aeroengine casing, the ultimate tensile strengthen and elongation of the repair layer are 215 ± 12 MPa and 5.79 ± 0.1%, respectively, while the tensile strength of the substrate is 225 MPa. The mechanical properties of the repair layer basically meet the repair requirements of the aeroengine casing. The coefficient of friction (COF) and wear rate ω of the repair layer were comparable to the level of aeroengine casing. Moreover, the corrosion behavior test results showed that the corrosion potential of as-build AlSi10Mg repair layer was −0.90 V, which was higher than that of substrate (−0.92 V), indicating that the AlSi10Mg repair layer has better corrosion resistance than substrate, and the Al9Si phase is the important factor that effecting the corrosion performance of AlSi10Mg repair layer. This work can provide reference for the potential application of the LMD technology in the field of high-quality repair of aeroengine casing.

Experimental study and comprehensive analysis of leakage characteristics of the casing flange of an aero-engine

Leakage at the casing flange in aero-engines can reduce the engine performance and endanger the normal operation of the whole aircraft. An experimental system is designed in this paper to investigate the influence of loaded parameters and structural parameters on the sealing characteristics of the casing flange in aero-engines. The system includes an experimental table subsystem, a hydraulic subsystem, an inflation and leakage measurement subsystem and an experimental data acquisition subsystem. The results show that the gas pressure has a greater influence compared to the axial force. When the gas pressure rises from 0.2 MPa to 0.5 MPa, the leakage volume increases from about 9 L/min to about 34 L/min. Among the structural parameters, the number of bolts has the greatest influence, followed by the number of layers, and the surface roughness has the least influence, with the three influencing parameters not being mutually exclusive. The benefits of improving the structural parameters to reduce the leakage volume all increase with the rise of gas pressure. The leakage volume of experiment parts with 48 bolts arranged on the 2-layer casing flange with surface roughness 1.6 and 3.2 are taken as two sets of the contract type data and fitted by the nested Genetic Algorithm. The fitted results indicate that increase of surface roughness from 1.6 to 3.2 will result in an increase of 26 L/min in the leakage volume. The conclusions from the paper can provide guidance suggestions for the sealing design of the aero-engine casing.

Coaxiality prediction for aeroengines precision assembly based on geometric distribution error model and point cloud deep learning

Assembly accuracy of aeroengines influences operation performance and service life. The coaxiality of the aeroengine is the main index of assembly accuracy and is also a core index to represent assembly quality. However, direct measurement of coaxiality is a difficult technical problem due to the sealed structure of the aeroengine casing system. A coaxiality prediction method is proposed to obtain coaxiality and assist assembly by geometric distribution error modeling and point cloud deep learning. The prediction process consists of three steps. In the beginning, the geometric distribution error model is established to construct the accurate dense point cloud of aeroengine part surfaces by the non-uniform rational B-splines (NURBS) method based on the coordinate measuring machine collecting information. Then, the mapping between the dense point cloud and coaxiality is established to obtain an assembly dataset by the virtual assembly. Finally, the dataset is fed to a new point cloud deep learning backbone, Self-channel cross attention point network, and realizes end-to-end coaxiality prediction based on the aeroengine surface point cloud. The geometric distribution error model is tested on the aeroengine simulated parts with 0.001 mm accuracy. The prediction method is verified on the aeroengine simulated parts and compared with other point cloud deep learning baselines. The method proposed in this paper realizes 93.17% prediction accuracy with 0.01 mm coaxiality precision which is a high performance and meets the requirements of industrial measurement. This paper provides an effective coaxiality prediction model for the aeroengine casing system, to improve the accuracy and efficiency of the aeroengine assembly.

Evolution of the Interelectrode Gap during Co-Rotating Electrochemical Machining

A new co-rotating electrochemical machining method is presented to machine the complex structure inside annular parts such as flame tubes and aero-engine casings. Due to the unique shape and motion of electrodes, it is difficult to accurately compute the electric field intensity in the machining area. In this paper, the complex electric field model is simplified by conformal transformation, and the analytical solution of electric field intensity is exactly calculated. A material removal model is built on the basis of the electric field model, and the dynamic simulation of the material removal process is realized. The effects of the cathode radius, applied voltage, feed rate and initial interelectrode gap on the interelectrode gap (IEG) and material removal rate (MRR) are analyzed. The simulation results indicate that the MRR is always slightly less than the feed rate in a quasi-equilibrium state, resulting in a slow reduction in IEG. In addition, the final machining state is not affected by the initial IEG, and the MRR in a quasi-equilibrium state is determined by the feed rate. Several comparative experiments were carried out using the optimized processing parameters, in which the MRR and IEG were measured. The convex structures were successfully machined inside the annular workpiece with optimum machining parameters. The experimental results are in good agreement with the theoretical results, indicating that the established model can effectively predict the evolution process of MRR and IEG.

A New Fourier Q Operator Network Based Reinforcement Learning Method for Continuous Action Space Decision-making in Manufacturing

The problems of continuous action space decision-making are widespread in industrial manufacturing. However, when dealing with these problems, existing reinforcement learning (RL) methods relies on a large number of training samples, which is always unacceptable given the limited availability or expensive nature of data, such as low-volume manufacturing. This paper proposes a new Fourier Q operator network (FQON) based RL method. The input of FQON is the expected state function and its output the Q-value function, and both functions take the action in RL as independent variables. The infinite-dimensional mapping between the function domains is established by a set of parameters that can be used with different discretization, which fixes the mapping complexity regardless of the action space resolution. By taking the advantages of the fast calculation using on Fourier kernel operator, the mapping complexity is highly reduced, and it enables that FQON can realize the decision-making in continuous action space using a small amount of training samples. Taking machining deformation control of an aero-engine casing as a case study, experimental results showed that FQON based RL method can control the deformation well with limited training samples.

A method for constructing a machining knowledge graph using an improved transformer

Process knowledge base is a core component in the intelligent process, which determines the intelligent degree of product manufacturing and directly affects the production efficiency of products. However, traditional process knowledge base is often constructed manually, which is difficult and time-consuming. In addition, in the field of machining, there is a large amount of unstructured invisible process knowledge, which is not effectively organized and managed. To make use of this knowledge and provide knowledge support for downstream production and maintenance, a process knowledge base construction framework is proposed by using Knowledge Graph (KG) technology. Firstly, the ontology rules of process knowledge are designed from the perspective of the processing method of process characteristics according to the particularity of knowledge in the machining field. The process KG schema layer is then constructed. Secondly, a neural network BERT–Improved TRANSFORMER–CRF (BITC) model is proposed for the machining knowledge extraction task, and the data layer is constructed. Then, entity linking and knowledge fusion are performed by using the word vector cosine similarity algorithm and stored in Neo4j. The process KG is then constructed. Finally, the effectiveness of the proposed method is verified by using an aero-engine casing of an enterprise as an example. Under the same dataset, the BITC model scored higher than several other classical models. The Precision, Recall, and F1-score were 85.27%, 86.40%, and 85.83 %, respectively.

A Novel Approach of Resource Allocation for Distributed Digital Twin Shop-Floor

Facing global market competition and supply chain risks, many production companies are leaning towards distributed manufacturing because of their ability to utilize a network of manufacturing resources located around the world. Deriving from information and communication technologies and artificial intelligence, the digital twin shop-floor (DTS) has received great attention from academia and industry. DTS is a virtual shop-floor that is almost identical to the physical shop-floor. Therefore, multiple physical shop-floors located in different places can easily be interconnected to realize a DT that is a distributed digital twin shop-floor (D2TS). However, some challenges still hinder effective and efficient resource allocation among D2TSs. In order to attempt to address the issues, firstly, this paper proposes an information architecture for D2TSs based on cloud–fog computing; secondly, a novel mechanism of D2TS resource allocation (D2TSRA) is designed. The proposed mechanism both makes full use of a digital twin to support dynamic allocation of geographic resources and avoids the centralized solutions of the digital twin which lead to a heavy burden on the network bandwidth; thirdly, the optimization problem in D2TSRA is solved by a BP neural network algorithm and an improved genetic algorithm; fourthly, a case study for distributed collaborative manufacturing of aero-engine casing is employed to validate the effectiveness and efficiency of the proposed method of resource allocation for D2TS; finally, the paper is summarized and the relevant research directions are prospected.

Investigation of the Vibration Transmission Characteristics of the Aero-Engine Casing System by Rotating Force Exciter

This work aims to study the dynamic characteristics of an entire aero-engine casing by experiments using a new type of rotating exciter. New vibration sensor layout rules are proposed according to experimental results and vibration transmission characteristics. The vibration response of the aero-engine casing is carried out, and the vibration response of different casing measuring points is also studied. The finite element model of the engine casing’s structure is established to obtain the natural frequency of the whole aero-engine casing, which agrees well with the experimental measurement. We find that the vibration acceleration transmission value of the radial measuring point of bearing No. 1, but not the fan sensor, is more suitable for detecting the running state of the fan rotor. In addition, the sensor in the intermediate casing can detect the vibration of the high-pressure rotor. Bolt loosening of the flange has little effect on the vibration transmission characteristics of the casing. This work aims to provide experimental data of whole aero-engine vibration characteristics with a new rotating exciter for the vibration test, which can help optimize the location of the vibration transducer for the engine and the assembly technology design for the whole engine structure.

Simultaneous measurement of external and internal surface shape and deformation based on photogrammetry and stereo-DIC

Mechanical structures with cavities such as auto rim and aero-engine casing are widely used in engineering. It is of great significance for their dynamic performance evaluation if one can measure the shape and deformation of the internal and external surfaces together. However, due to the narrow interior space and self-occlusion of complex structures, the conventional stereo-DIC unit is not applicable to impose the simultaneous measurement in the outer and inner areas. This paper is therefore concerned with a systematic solution for the issue. A reflection-assisted multi-view DIC system is configured, which consists of the stereo-DIC (SDIC) unit and the reflection-assisted stereo-DIC (RSDIC) unit. The design idea and selection essential of the RSDIC unit are stated. Subsequently, a correction model for reflection imaging is detailed. Close-range photogrammetry is specially adopted to compute the 3D global points. The extrinsic parameters of each DIC unit are estimated via the rear resection so that the individual reference frame can be transformed into a common coordinate system. Five experiments, including two tests of calibration accuracy, shape and deformation measurement accuracy, and applications in an aero-engine casing prototype, prove the precision and effectiveness of the proposed method.

A Deformation Force Monitoring Method for Aero-Engine Casing Machining Based on Deep Autoregressive Network and Kalman Filter

Aero-engine casing is a kind of thin-walled rotary part for which serious deformation often occurs during its machining process. As deformation force is an important physical quantity associated with deformation, the utilization of deformation force to control the deformation has been suggested. However, due to the complex machining characteristics of an aero-engine casing, obtaining a stable and reliable deformation force can be quite difficult. To address this issue, this paper proposes a deformation force monitoring method via a pre-support force probabilistic decision model based on deep autoregressive neural network and Kalman filter, for which a set of sophisticated clamping devices with force sensors are specifically developed. In the proposed method, the pre-support force is determined by the predicted value of the deformation force and the equivalent flexibility of the part, while the measurement errors and the reality gaps are reduced by Kalman filter via fusing the predicted and measured data. Both computer simulation and physical machining experiments are carried out and their results give a positive confirmation on the effectiveness of the proposed method. The results are as follows. In the simulation experiments, when the confidence is 84.1%, the success rate of deformation force monitoring is increased by about 30% compared with the traditional approach, and the final impact of clamping deformation of the proposed method is less than 0.003 mm. In the real machining experiments, the results show that the calculation error of deformation by the proposed method based on monitoring the deformation force is less than 0.008 mm.

Developing a Ball Screw Drive System of High-Speed Machine Tool Considering Dynamics

In order to provide guidance and reference for developing a ball screw drive system of the high-speed machine tool, this article investigates three basic issues with consideration of dynamics, including mechanical characteristics, inertia matching problem, and driving torque characteristics. The axial stiffness and natural frequency of the system are first calculated to verify the correctness of mechanical parts selection, and the stability of the system is ensured by designing an appropriate gain. vvNext, the inertia matching problem between the servo motor and load is analyzed based on the two-inertia-system, and the range of the inertia ratio is determined as an important principle in developing a ball screw drive system. The speed–torque characteristics are further discussed, and a graphical method is proposed to quickly and accurately determine a suitable servo motor. Finally, a complete flow is described, and the ball screw drive systems of a high-speed machine tool for aeroengine casing manufacturing are developed as an application. The actual test results of speed, accuracy, cutting ability, and machining performance indicate that the developed ball screw drive systems have a satisfactory performance, which validate the effectiveness of the comprehensive study.

Multi-Axis Loading on Multiple Flanges of Intermediate Aero-Engine Case Based on Parallel Robotic Simulation

Applying multi-axis force to an intermediate aero-engine case to simulate its working force environment is critical to accurately evaluate its mechanical properties. This paper presents a multi-axis loading method based on parallel robots to simultaneously add complex forces on two mounting flanges of the aero-engine case. The multi-axis loading device (MLD) for each flange was developed based on a 6-UPS parallel robot. Its geometric structure was presented and kinematics was analyzed. The static mapping between the multi-axis force and linear actuating force was derived on each flange, and the influence of loading two flanges at the same time was discussed. A force control algorithm based on admittance control was designed and the multi-axis loading on two flanges of aero-engine case was validated by simulations. Results illustrate that the MLD could apply multi-axis force to each flange and using two MLDs could simultaneously load two flanges. This suggests that parallel robots could be implemented as a general system to produce complex force environments for a series of aero-engine cases with multiple mounting flanges, which could mimic their actual working loads by ground tests to improve the accuracy of the mechanical property tests.

A Novel Hybrid Deep Learning Model for Rul Prediction for Industrial Settings: Aero-Engine Case Example

A Novel Hybrid Deep Learning Model for Rul Prediction for Industrial Settings: Aero-Engine Case Example