Mapping relationship analysis of welding assembly properties for thin-walled parts with finite element and machine learning algorithm

The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.

Field-assisted machining of difficult-to-machine materials

Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining technologies often struggle to achieve ultra-precision with DMMs resulting from poor surface quality and low processing efficiency.In recent years,field-assisted machining (FAM) technology has emerged as a new generation of machining technology based on innovative principles such as laser heating,tool vibration,magnetic magnetization,and plasma modification,providing a new solution for improving the machinability of DMMs.This technology not only addresses these limitations of traditional machining methods,but also has become a hot topic of research in the domain of ultra-precision machining of DMMs.Many new methods and principles have been introduced and investigated one after another,yet few studies have presented a comprehensive analysis and summarization.To fill this gap and understand the development trend of FAM,this study provides an important overview of FAM,covering different assisted machining methods,application effects,mechanism analysis,and equipment design.The current deficiencies and future challenges of FAM are summarized to lay the foundation for the further development of multi-field hybrid assisted and intelligent FAM technologies.

Machining Deformation Prediction of Thin-Walled Part Based on Finite Element Analysis

For the problems of machining distortion and the low accepted product during milling process of aluminum alloy thin-walled part,this paper starts from the analysis of initial stress state in material preparation process,the change process of residual stress within aluminum alloy pre-stretching plate is researched,and the distribution law of residual stress is indirectly obtained by delamination measurement methods,so the effect of internal residual stress on machining distortion is considered before finite element simulation. Considering the coupling effects of residual stress,dynamic milling force and clamping force on machining distortion,a threedimensional dynamic finite element simulation model is established,and the whole cutting process is simulated from the blank material to finished product,a novel prediction method is proposed,which can availably predict the machining distortion accurately. The machining distortion state of the thin-walled part is achieved at different processing steps,the machining distortion of the thin-walled part is detected with three coordinate measuring machine tools,show that the simulation results are in good agreement with experimental data.

Effect of Machined Surface Integrity on Fatigue Performance of Metal Workpiece:A Review

Fatigue performance is a serious concern for mechanical components subject to cyclical stresses,particularly where safety is paramount.The fatigue performance of components relies closely on their surface integrity because the fatigue cracks generally initiate from free surfaces.This paper reviewed the published data,which addressed the effects of machined surface integrity on the fatigue performance of metal workpieces.Limitations in existing studies and the future directions in anti-fatigue manufacturing field were proposed.The remarkable surface topography(e.g.,low roughness and few local defects and inclusions)and large compressive residual stress are beneficial to fatigue performance.However,the indicators that describe the effects of surface topography and residual stress accurately need further study and exploration.The effect of residual stress relaxation under cycle loadings needs to be precisely modeled precisely.The effect of work hardening on fatigue performance had two aspects.Work hardening could increase the material yield strength,thereby delaying crack nucleation.However,increased brittleness could accel-erate crack propagation.Thus,finding the effective control mechanism and method of work hardening is urgently needed to enhance the fatigue performance of machined components.The machining-induced metallurgical structure changes,such as white layer,grain refinement,dislocation,and martensitic transformation affect the fatigue performance of a workpiece significantly.However,the unified and exact conclusion needs to be investigated deeply.Finally,different surface integrity factors had complicated reciprocal effects on fatigue performance.As such,studying the comprehensive influence of surface integrity further and establishing the reliable prediction model of workpiece fatigue performance are meaningful for improving reliability of components and reducing test cost.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting materials.To further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 K.The average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction processes.The results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element features.Furthermore,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 K.This study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

Conformal Human–Machine Integration Using Highly Bending‑Insensitive,Unpixelated,and Waterproof Epidermal Electronics Toward Metaverse

Efficient and flexible interactions require precisely converting human intentions into computer-recognizable signals,which is critical to the breakthrough development of metaverse.Interactive electronics face common dilemmas,which realize highprecision and stable touch detection but are rigid,bulky,and thick or achieve high flexibility to wear but lose precision.Here,we construct highly bending-insensitive,unpixelated,and waterproof epidermal interfaces(BUW epidermal interfaces)and demonstrate their interactive applications of conformal human–machine integration.The BUW epidermal interface based on the addressable electrical contact structure exhibits high-precision and stable touch detection,high flexibility,rapid response time,excellent stability,and versatile“cut-and-paste”character.Regardless of whether being flat or bent,the BUW epidermal interface can be conformally attached to the human skin for real-time,comfortable,and unrestrained interactions.This research provides promising insight into the functional composite and structural design strategies for developing epidermal electronics,which offers a new technology route and may further broaden human–machine interactions toward metaverse.

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

Utilizing Machine Learning with Unique Pentaplet Data Structure to Enhance Data Integrity

Data protection in databases is critical for any organization,as unauthorized access or manipulation can have severe negative consequences.Intrusion detection systems are essential for keeping databases secure.Advancements in technology will lead to significant changes in the medical field,improving healthcare services through real-time information sharing.However,reliability and consistency still need to be solved.Safeguards against cyber-attacks are necessary due to the risk of unauthorized access to sensitive information and potential data corruption.Dis-ruptions to data items can propagate throughout the database,making it crucial to reverse fraudulent transactions without delay,especially in the healthcare industry,where real-time data access is vital.This research presents a role-based access control architecture for an anomaly detection technique.Additionally,the Structured Query Language(SQL)queries are stored in a new data structure called Pentaplet.These pentaplets allow us to maintain the correlation between SQL statements within the same transaction by employing the transaction-log entry information,thereby increasing detection accuracy,particularly for individuals within the company exhibiting unusual behavior.To identify anomalous queries,this system employs a supervised machine learning technique called Support Vector Machine(SVM).According to experimental findings,the proposed model performed well in terms of detection accuracy,achieving 99.92%through SVM with One Hot Encoding and Principal Component Analysis(PCA).

Structural Reliability Analysis Based on Support Vector Machine and Dual Neural Network Direct Integration Method

Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.

Fully Integrated Machine Control for Ultra Precision Machining

To meet the demands for highly advanced components with ultra precise contour accuracy and optical surface quality arising in the fields of photonics and optics, automotive, medical applications and biotechnology, consumer electronics and renewable energy, more advanced production machines and processes have to be developed. As the complexity of machine tools rises steadily, the automation of manufacture increases rapidly, processes become more integrated and cycle times have to be reduced significantly, challenges of engineering efficient machine tools with respect to these demands expand every day. Especially the manufacture of freeform geometries with non-continuous and asymmetric surfaces requires advanced diamond machining strategies involving highly dynamic axes movements with a high bandwidth and position accuracy. Ultra precision lathes additionally equipped with Slow Tool and Fast Tool systems can be regarded as state-of-the-art machines achieving the objectives of high quality optical components. The mechanical design of such ultra precision machine tools as well as the mechanical integration of additional highly dynamic axes are very well understood today. In contrast to that, neither advanced control strategies for ultra precision machining nor the control integration of additional Fast Tool systems have been sufficiently developed yet. Considering a complex machine setup as a mechatronic system, it becomes obvious that enhancements to further increase the achievable form accuracy and surface quality and at the same time decrease cycle times and error sensitivity can only be accomplished by innovative, integrated control systems. At the Fraunhofer Institute for Production Technology IPT a novel, fully integrated control approach has been developed to overcome the drawbacks of state-of-the-art machine controls for ultra precision processes. Current control systems are often realized as decentralized solutions consisting of various computational hardware components for setpoint generation, machine control, HMI （human machine interface）, Slow Tool control and Fast Tool control. While implementing such a distributed control strategy, many disadvantages arise in terms of complex communication interfaces, discontinuous safety structures, synchronization of cycle times and the machining accuracy as a whole. The novel control approach has been developed as a fully integrated machine control including standard CNC （computer numerical control） and PLC （programmable logic controller） functionality, advanced setpoint generation methods, an extended HMI as well as an FPGA （field programmable gate array）-based controller for a voice coil driven Slow Tool and a piezo driven Fast Tool axis. As the new control system has been implemented as a fully integrated platform using digital communication via EtherCAT, a continuous safety strategy could be realized, the error sensitivity and EMC susceptibility could be significantly decreased and the overall process accuracy from setpoint generation over path interpolation to axes movements could be enhanced. The novel control at the same time offers additional possibilities of automation, process integration, online data acquisition and evaluation as well as error compensation methods.

A Study of the Movement Path for NC-Electrochemical Machining of an Integral Impeller

Aiming at machining an integral impeller used by an aero-engine by NC-Electrochemical Machining （ NC-ECM） , we first describe the fitting of a distorted blade surface with a ruled surface. Then a four-axis NC-electrochemical machining process is analyzed and the movement of the impeller is studied by using an equivalent open-loop plane motion link, and every program segment＇s feed displacement of three axes is calculated by analytical solution. Through comparing two results which are obtained , respectively, from analytical solution and experimental result, the comparative effectiveness has proved that the analytical solution is useful in calculating the feed displacement. The study is helpful for the programming of the machining process.

Experimental Study on Entropy Features in Machining Vibrations of A Thin-Walled Tubular Workpiece

In machining processes,chatter vibrations are always regarded as one of the major limitations for production quality and efficiency.Accurate and timely monitoring of chatter is helpful to maintain stable machining operations.At present,most chatter monitoring methods are based on the energy level at specified chatter frequencies or frequency bands.However,the spectral features of chatter could change during machining operations due to complexity and time-varying dynamics of the physical machining process.The purpose of this paper is to investigate the time-varying chatter features in turning of thin-walled tubular workpieces from the perspective of entropy.The airborne acoustics was selected as the source of information for machining condition monitoring.First,corresponding to the distinguishing surface topographies relevant to machining conditions,the features of the sound signal emitted during turning of the thin-walled cylindrical workpieces were extracted using the spectral analysis and wavelet packet transform,respectively.It was shown that the dominant vibration frequency as well as the energy distribution could shift with the transition of the machining status.After that,two relative entropy indicators based on the spectrum and the wavelet packet energy were constructed to identify chattering events in turning of the thin-walled tubes.The experimental results demonstrate that the proposed indicators could accurately reflect the transition of machining conditions with high sensitivity and robustness in comparison with the traditional FFT-based methods.The achievement of this study lays the foundations of the online chatter monitoring and control technique for turning of the thin-walled tubular workpieces.

Integration of Machining and Measuring Processes Using On-Machine Measurement Technology

This paper presents an integration methodology for ma chining and measuring processes using OMM (On-Machine Measurement) technology b ased on CAD/CAM/CAI integration concept. OMM uses a CNC machining center as a me asuring station by changing the tools into measuring probes such as touch-type, laser and vision. Although the measurement accuracy is not good compared to tha t of the CMM (Coordinate Measuring Machine), there are distinctive advantages us ing OMM in real situation. In this paper, two topics are handled to show the eff ectiveness of the machining and measuring process integration: (1) inspection pl anning strategy for sculptured surface machining and (2) tool path compensation for profile milling process. For the first topic, as a first step, effective mea suring point locations are determined to obtain optimum results for given sampli ng numbers. Two measuring point selection methods are suggested based on the CAD /CAM/CAI integration concept: (1) by the prediction of cutting errors and (2) by considering cutter contact points to avoid the measurement errors caused by cus ps. As a next step, the TSP (Traveling Salesman Problem) algorithm is applied to minimize the probe moving distance. Appropriate simulations and experiments are performed to verify the proposed inspection planning strategy, and the results are analyzed. For the second topic, a methodology for profile milling error comp ensation is presented by using an ANN (Artificial Neural Network) model trained by the inspection database of OMM system. First, geometric and thermal errors of the machining center are compensated using a closed-loop configuration for the improvement of machining and inspection accuracy. The probing errors are also t aken into account. Then, a specimen workpiece is machined and then the machi ning surface error distribution is measured on the machine using touch-type pro be. In order to efficiently analyze the machining errors, two characteristic err or parameters (W err and D err) are defined. Subsequently, these param eters are modeled by applying the RFB (Radial Basis Function) network approach a s an ANN model. Based on the RBF network model, the tool paths are compensated i n order to effectively reduce the errors by employing an iterative algorithm. In order to validate the approaches proposed in this paper, a concrete case of the machining process is taken into account and about 90% of machining error reduction is successfully accomplished through the proposed approaches.

NEW INSIGHTS INTO PRINCIPLES FOR DESIGNING ARCHITECTURE OF ENVIRONMENT SYSTEM FOR TOOL INTEGRATION ─IN THE CASE OF PRODUCTION MACHINES DESIGN

CAD/CAM integration is broadly viewed as tool integration. From this point of view, both CAD/CAM software and hardware are considered as tools. A great advantage of this view point is that a very flexible paradigm is introduced, in which the CAD/CAM system for a particular industrial application can be customized and built by selecting a set of tools. This paper intends to shed some lights on the principles for designing the architecture of such a tool integration environment. Although such an environment is no more than a computer program sys. tem itself, its particular characteristics arising from its unique role, i.e., tool integration, make the discussion on the designing of an environment architecture nontrivial. The paper externalizes the author's experiences in implementing an tool integration environment for production machines.In particular, the discussion renders some insights into the concepts such as tool integration models, various views of an environment architecture, classification of data into tool-data and environment data, and employment of the information relativity principle in making an environment architecture flexible. The paper will take production machines design as a target application that an environment system is developed for, owing to its extreme complexity and thus make sense of coverage of other engineering applications.

基于MachineWorks的数控加工仿真系统设计研究

随着当前社会发展进程的不断加快,各项设备技术得以不断的创新和研发。数控加工仿真系统上对于数控加工代码是否正确的重要检测方法。数控加工仿真系统,还能够有效完成对具体加工过程的模拟,来预见在使用过程中具体状况出现,改进具体的加工程序及工艺。基于此本文通过针对数控加工仿真系统设计中的关键性技术,采用基于MachineWorks完成数控加工仿真系统设计。通过构建该仿真系统,研究结果表明存在了一定的灵活实用性,确保了一定的工作开发使用量。以期本次研究能够为数控加工仿真系统的设计开发,能为进一步的技术研发提供可参考理论依据。

Thin-Wall Aluminum Die-Casting Technology for Development of Notebook Computer Housing

Silicon-based aluminum casting alloys are known to be one of the most widely used alloy systems mainly due to their superior casting characteristics and unique combination of mechanical and physical properties. However,manufacturing of thin-walled aluminum die-casting components,less than 1.0 mm in thickness,is generally known to be very difficult task to achieve aluminum casting alloys with high fluidity.Therefore,in this study,the optimal die-casting conditions for producing 297 mm×210 mm×0.7 mm thin-walled aluminum component was examined experimentally by using 2 different gating systems,tangential and split type,and vent design.Furthermore,computational solidification simulation was also conducted.The results showed that split type gating system was preferable gating design than tangential type gating system at the point of view of soundness of casting and distortion generated after solidification.It was also found that proper vent design was one of the most important factors for producing thin-wall casting components because it was important for the fulfillment of the thin-wall cavity and the minimization of the casting distortion.

Boundary Element Analysis forModeⅢCrack Problems of Thin-Walled Structures from Micro-to Nano-Scales

This paper develops a new numerical framework for modeⅢcrack problems of thin-walled structures by integrating multiple advanced techniques in the boundary element literature.The details of special crack-tip elements for displacement and stress are derived.An exponential transformation technique is introduced to accurately calculate the nearly singular integral,which is the key task of the boundary element simulation of thin-walled structures.Three numerical experiments with different types of cracks are provided to verify the performance of the present numerical framework.Numerical results demonstrate that the present scheme is valid for modeⅢcrack problems of thin-walled structures with the thickness-to-length ratio in the microscale,even nanoscale,regime.

Viscous Inner and Outer Pressure Forming Method of Thin-walled Tube and Its Application

Aiming at overcoming the difficulties in integral forming of thin-walled tubes with complex shapes, a novel forming method by inner and outer pressure through viscous was proposed. In this method, by dividing large deformation of the part into inner and outer pressure forming deformations, the limit deformation of tube part can be increased by several times. Meanwhile, the principle of viscous inner and outer pressure forming was provided, and key problems during the forming process such as reduction of the wall-thickness and instability wrinkling were analyzed. Thereby, the complex curved surface super-alloy GH3044 thin-walled tube with varying diameter ratio of 1.35（the ratio between the maximum and minimum diameters of the part） can be integrally formed by this method. The experimental surface of the formed part is superior in quality and the wall-thickness distribution is uniform. The results show that the viscous inner and outer pressure forming can provide a new approach for integral forming of thin-walled tubes with complex shapes.