Deformation Error Research Articles

Improved prediction of litchi impact characteristics with an energy dissipation model

A model of elastic impact (EI) and a model that considers energy dissipation (CED model) were developed to describe the deformation behavior during litchi impact. In the CED model, dissipation energy is due to damping effect and the change of elastic modulus after impact. Drop tests of fruit onto a fixed plate were conducted with a pendulum device for three litchi cultivars (‘Nuomci’, ‘Guiwei’, and ‘Lvhebao’) to test the accuracy of the two models. The results show that the CED model better simulated the change of fruit velocity during impact and better predicted the occurrence time of maximum impact force and maximum deformation compared to the EI model. Both models accurately predicted the maximum impact force of ‘Guiwei’ litchi with all errors less than 5 %, but had errors up to 24.8 % for the prediction of the maximum impact force of ‘Nuomici’ and ‘Lvhebao’ litchis at high drop heights (drop height ≥ 400 mm). The CED model accurately predicted maximum deformation (errors less than 8 %) but the EI model over-predicted maximum deformation with errors range from 12.2 % to 39.2 % at all drop heights for the three tested cultivars. Both models predicted the contact time with low-level errors (<10 %). With a reliable theoretical model, the impact characteristics of litchis can be calculated and then applied to assess the fruit damage degree.

Optimization of Generatively Encoded Multi-Material Lattice Structures for Desired Deformation Behavior

Natural systems achieve favorable mechanical properties through coupling significantly different elastic moduli within a single tissue. However, when it comes to man-made materials and structures, there are a lack of methods which enable production of artifacts inspired by these phenomena. In this study, a method for design automation based on alternate deposition of soft and stiff struts within a multi-material 3D lattice structure with desired deformation behavior is proposed. These structures, once external forces are applied, conform to the geometry given in advance. For that purpose, a population-based algorithm was proposed and integrated with a multi-material physics simulator. To reduce the amount of data processed during optimization, a generative encoding method based on discrete cosine transform (DCT) was proposed. This enabled a compressed topological description and promoted symmetry in material distribution. The simulation results showed different three-dimensional lattice structures designed with proposed algorithm to meet a set of desired deformation behaviors. The relation between residual deformation error, targeted deformation geometry, and material distribution is discussed.

A novel gaussian distribution and tukey weight (gdatw) algorithms: deformation accuracy for augmented reality (ar) in facelift surgery

In facelift surgeries, tracking the exact position of the patient’s facial nerves, blood vessels and other tissues can help the surgeons make the right decisions during surgery. Using Augmented Reality technologies that display the mapping of soft tissues beneath the skin, surgeons will be able to detect and locate the regions of cutting correctly prior to making the first cut. The study of Augmented Reality-assisted surgeries has not been found in facelift surgeries involving facial soft tissues, thus this journal can provide with invaluable first steps into the more concentrated studies involving this area. The current available systems in the oral and maxillofacial areas has shown limitation in supporting the elastic nature of facial soft tissues, which shape shifts and changes due to patient’s movement, or movement caused by surgeon during surgery. This paper aims to increase overlay accuracy by reducing elastic deformation error for Augmented Reality in facelift surgeries. The proposed system consists of a Gaussian Distribution and Tukey Weight (GDaTW) algorithm to reduce the deformation error after the geometric error algorithm had been performed. The test results confirm that the new algorithm improves the video accuracy by ~0.10 mm by reducing the overlay error caused by elastic deformation with the display framerate of 5–10 frames per second compared to 10–13 frames per second in existing system. The improvement proposed in this system increases the overlay accuracy by reducing elastic deformation error. The correct display of soft tissues in the mandibular region using Augmented Reality aims to aid plastic surgeons perform facelift surgeries with confidence, to avoid making the wrong cuts of vital areas occluded by the patient’s skin.

Modal control of quasi-static linear-elastic movements of precision equipment

A new modal method for controlling the deformation of a linear elastic structure under conditions of quasi-static loading is proposed. The advantage of the new method is independent control of orthogonal modes, which increases the accuracy and speed of control. To illustrate the proposed method, compensation of the deformation error in a rotary table with a high tilt accuracy is considered. Keywords: modal method, control of structure deformation, quasi-static loading, simulation of a structure, eigenforms adigorev@gmail.com

Virtual Polar Region Method Based on the Earth’s Transverse Ellipsoid Model

Experimental verification is very important for the research of inertial navigation and integrated navigation technology, but most researchers do not have the opportunity to conduct experiments directly in the polar regions. In order to solve the problem of inertial navigation verification in high latitude areas, a virtual polar region method based on transverse ellipsoid model is proposed. The method converts the reference information, initial state, and inertial sensor data into polar regions based on the transverse geographic coordinate system and can ensure that the attitude, velocity, and altitude information relative to the local-level frame remain unchanged. Therefore, the actual test data in the middle and low latitudes can be reconstructed accurately in the polar region without singularities, trajectory deformation, and principle errors. Simulation and vehicle tests show that the proposed method can achieve the same verification effect as the actual polar experiment.

The variable radial depth of cut in finishing machining of thin-walled blade based on the stable-state deformation field

Due to the low stiffness of the thin-walled part, the larger deformation error occurs in the milling process, and the machining precision and quality cannot be satisfied. Previous studies of the tool path generation methods for the deformation error control mainly focused on the deformation calculation of the cutting block and lacked how to control the machining deformation of the complex free-form surface of the thin-walled blade by the generated tool path. This paper firstly establishes the elastic deformation error model of the thin-walled part milling process depending on the stiffness matrix of the workpiece before the cutting process. Furthermore, the cutting force model in ball-end milling and the stable-state deformation field are studied to calculate the cutting contact points sequence with less machining deformation error for the tool path planning process. The radial depth of cut of the generated tool path is optimized with different strategies to calculate the suitable cutting contact points for deformation control of the thin-walled part milling process by applying the relationship between the cutting parameters and elastic deformation. Finally, thin-walled blades are machined with four different cutting paths. The measured results demonstrate that the optimized tool paths are effective for deformation error control of the thin-walled blades milling process considering the machining precision and efficiency.

A Novel Error Compensation Method for Multistage Machining Processes Based on Differential Motion Vector Sets of Multiple Contour Points

Abstract Modeling the variation propagation based on the stream of variation (SoV) methodology for multistage machining processes (MMPs) has been investigated intensively in the past two decades; however, little research is conducted on the variation reduction and the existing work fails to be applied to irregular features caused by the machining-induced variation varying with the positions of the contour points on the machined surface. This paper proposes a novel error compensation method for MMPs by modifying the tool path to reduce variation for general features. The method based on differential motion vector (DMV) sets of multiple contour points is presented to represent the deviation of the irregular feature. Then, the conventional stream of variation (SoV) model is further extended to more accurately describe variation propagation for irregular features considering the actual datum-induced variations and the varying machining-induced variations, especially the deformation errors for the low stiffness workpiece. Based on the extended SoV model and error equivalence mechanism, the datum error and fixture error are transformed to the equivalent tool path error. Then, the original tool path is modified through shifting the machine zero points of machine tools with no need for changing the original G code and workpiece setup. A real cutting experiment validates the effectiveness of the proposed error compensation method for MMPs with an average precision improvement of over 60%. The application of the extended SoV model significantly contributes to compensating more complex error sources for MMPs, such as the clamp force, the internal residual stress, etc.

Real-time monitoring of depth of cut in the multi-axis milling process with ball-end cutter

Machining accuracy is one of the most critical issues in multi-axis CNC milling. However, various factors have an adverse effect on the production of precise surfaces such as imprecision of tool-setting, force-induced deformation, and machining errors of the previous cut. Monitoring the depth of cut (DOC) in the milling process has become increasingly important. The paper develops a deep neural network for real-time monitoring of the DOC based on the measured cutting force signal. The network is a variant of ResNet and soft-thresholding is embedded for denoising. Raw force signals and other parameters can be handled by the network without preprocessing. The network is trained with multiple milling tests of various DOC and cutter axis inclination angles, and a comparison is made with classic deep residual networks. The results show that the proposed network DRSN64 performs better under high noise levels with high computational efficiency. An average absolute error of 0.03 mm is reached in the real-time tracking test with varying cutter axis inclination angle and DOC.

Nonlinear Systematic Distortions Compensation in Satellite Images Based on an Equivalent Geometric Sensor Model Recovered From RPCs

The rational polynomial function is widely accepted as the preferred sensor model for high-resolution satellite imagery (HRSI). However, satellite images and their associated rational polynomial coefficients (RPCs) often suffer from nonlinear systematic errors which were caused by attitude oscillation, sensor deformation, and many other imperfect calibration errors, thus affecting the geo-referencing accuracy. Instead of modeling the biases by polynomials in the image space or refining RPCs directly, this study proposes an approach of going back to the physical model and correcting the local distortions in a self-calibration block adjustment. The algorithm of an equivalent geometric sensor model (EGSM) recovery from RPCs is described in detail. As an equivalent form of the physical sensor models, EGSM reflects the complete viewing geometry of push-broom HRSI. The interior and exterior orientation parameters of EGSM can be stably recovered from RPCs without using any metadata. An approach of RPCs refinement by self-calibration block adjustment based on EGSM is introduced. This approach can effectively compensate for the nonlinear systematic errors caused by platforms and sensors similar to the approach of a rigorous sensor model. The performance of EGSM-based block adjustment is compared with the RFM-based bias compensation method. Experiments using ZY-3 images show the EGSM-based approach can effectively eliminate the nonlinear distortions in satellite images caused by sensor deformation and attitude vibrations. Furthermore, experiments using images from various satellites show that the original RFM can be well fitted with the EGSM and the residuals are smaller than 0.1 pixels for all test images.

Method for Compensating Thermal Deformation Error in Transfer Station of Variable Curvature Workpiece

Method for Compensating Thermal Deformation Error in Transfer Station of Variable Curvature Workpiece

Online Deformation Characterization for Electro-Thermal-Mechanical Effect of Power Module via Confocal Methodology

Motivated by the dramatic increase of renewable energy and electric vehicle, a variety of customized power modules are rapidly designed and developed. However, it is necessary to expend a long time to examine the new power modules to ensure the reliability concerns via massive experiments, such as power cycling. The deformation characterization of the bare die has a grave impact on the failure of the power module. Therefore, to observe the weak point and presume the cycling lifetime of the power module package, the online characterization of deformation under the field application is significant. Besides, the outcomes can provide confident evidence for the finite element simulation model to conduct research on electrothermal-mechanical stress. In this letter, the mathematical model is created to understand the mechanical deformation induced by electrothermal coupling. To characterize the deformation of the power module in real-time for field application, a noninvasive confocal methodology is proposed. In addition, to confirm the feasibility of the proposed routine, an experimental setup is set up by using the front-to-front converter. The experiment results show that the maximal overall deformation occurs on the middle surface of the die while the edge of the bare die suffers minimal deformation, which corresponds to the simulation results. Besides, the overall and partial deformation errors between experimental and simulation results are within 5% and 7%, respectively.

Motion Compensation and 3-D Imaging Algorithm in Sparse Flight based Airborne Array SARi

This paper adopts a criterion of Barker code to generate a high-resolution image with a few flight times, for three dimensional (3-D) imaging model of airborne array SAR. Under the condition of motion error, we utilize the Modified Uniformly Redundant Arrays (MURA) modulation and 3-D Back Projection (BP) algorithm to obtain 3-D complex image pairs under each flight. Based on interferometry and Compressed Sensing (CS) in frequency domain, the array deformation error compensation is realized. The phases of 3-D complex image formed by the echo corresponding to negative MURA modulation are taken as reference, and phase compensation is performed on each single-pass complex image to restore the image phase relationship of each flight. Coherent accumulation of each complex image is implemented to realize high-resolution 3-D imaging under sparse flight sampling. Simulation analysis and experimental data verify the feasibility of proposed method.

Study of dynamic vibration characteristics and suppression of CNC machine tool during operation

The rapid development of aerospace, automobile, national defense and other manufacturing industries has continuously improved the performance requirements of high speed and high positioning accuracy of the CNC machine tool feed system. As the thermal deformation error increases, the system structural rigidity decreases, the vibration increases, and accuracy of machine tool reduces. Existing research mainly focuses on the properties of mechanical and single thermal feed system, and less considers the dynamic performance under the action of thermal-mechanical coupling. For the improvement of feed system performance, it is very beneficiary. This paper takes the high-speed tool of CNC feed system as objective, combined with the actual working conditions to analyze the thermal characteristics, dynamic characteristics and vibration characteristics of the system under the effect of thermal-mechanical coupling. The thermal resistance and convective heat transfer of the key joint surface, the application of moving thermal loads, and the establishment of a simulation model to complete the steady-state temperature field, transient temperature field and thermal-structure coupling analysis. The results show that the maximum temperature rise of the feed system is 21.08 °C, The maximum thermal deformation is 17.264 um. The study found that the parameters such as flow rate and airflow temperature have a significant impact on the cooling effect, so the cooling device was further optimized. This paper proposes a control method for cutting chatter of CNC machine tools based on coupling of characteristics of thermal and mechanical. The thermal characteristic of the feed system and the thermal-mechanical coupling vibration characteristic test provides the maximum temperature error of 3.2 %, verifying the correctness of the theoretical method and analysis model. The highest temperature obtained by the test is 39.7 °C, indicating that the high-speed feed system has a large thermal effect. The relative error of the vibration amplitude of the test and simulation is 12.5 %, which verifies coupling in terms of thermal-mechanical. The accuracy of the vibration analysis method; the experiment depicts that the vibration amplitude increases by 19.7 % under the coupling effect of thermal and mechanical, indicating the effectiveness of dynamic characteristics of the high-speed feed system considering the thermal-mechanical coupling effect.

Optimization of Transformer Winding Deformation Assessment Criterion Considering Insulation Aging and Moisture Content

Frequency response analysis is widely used to diagnose transformer winding deformation faults due to its high sensitivity, strong anti-interference capability, and equipment portability, but the results of frequency response analysis can be affected by insulation aging and moisture in the transformer, leading to errors in the diagnosis of winding deformation faults. Currently, there is no effective method to prevent such errors. This paper focuses on optimizing the criterion for diagnosing winding deformations when insulation aging and moisture are present. First, the winding frequency response curves of oil-paper insulation were determined by combining insulation aging and moisture tests of the oil-paper insulation with frequency response simulations of the transformer winding. Next, the winding deformation criterion predicting the likelihood and extent of errors diagnosing transformer winding deformations due to the insulation aging and moisture content is discussed. Finally, the corresponding criterion optimization method is proposed. The corresponding results show that insulation aging and moisture can lead to errors when using the correlation coefficient R criterion to diagnose the transformer winding deformations. Moreover, the possibility of winding deformation errors caused by the change of insulation state can be reduced by introducing the corresponding auxiliary criterion through comparing the capacitance change rate based on the frequency response method and that based on the dielectric spectrum method.

Backpropagation neural network assisted concentration prediction of biconical microfiber sensors.

The response of the optical microfiber sensor has a big difference due to the slight change in fiber structure, which greatly reduces the reliability of microfiber sensors and limits its practical applications. To avoid the nonlinear influences of microfiber deformation and individual differences on sensing performance, a backpropagation neural network (BPNN) is proposed for concentration prediction based on biconical microfiber (BMF) sensors. Microfiber diameter, cone angle, and relative intensity are the key input parameters for detecting the concentration of chlorophyll-a (from ∼0.03 mg/g to ∼0.10 mg/g). Hundreds of relative intensity-concentration data pairs acquired from 32 BMF sensors are used for the network training. The prediction ability of the model is evaluated by the root-mean-square error (RMSE) and the fitness value (F). The prediction performance of BPNN is compared with the traditional linear-fitting line method. After training, BPNN could adapt to the BMF sensors with different structural parameters and predict the nonlinear response caused by the small structural changes of microfiber. The concentration prediction given by BPNN is much closer to the actual measured value than the one obtained by the linear fitting curve (RMSE 1.84×10-3 mg/g vs. 4.6×10-3 mg/g). The numbers of training data and hidden layers of the BPNN are discussed respectively. The prediction results indicate that the one-hidden-layer network trained by more training data provides the best performance (RMSE and fitness values are 1.63×10-3 mg/g and 97.91%, respectively) in our experiments. With the help of BPNN, the performance of the BMF sensor is acceptable to the geometric deformation and fabrication error of microfiber, which provides an opportunity for the practical application of sensors based on micro/nanofibers.

Positioning Error Compensation for Industrial Robots Based on Stiffness Modelling

Insufficient stiffness of industrial robots is a significant factor which affects its positioning accuracy. To improve the positioning accuracy, a novel positioning error compensation method based on the stiffness modelling is proposed in this paper. First, the positioning errors considering the end load and gravity of industrial robots due to stiffness are analyzed. Based on the results of analysis, it is found that the positioning errors can be described by two kinds of deformation errors at joints: the axial deformation error and the radial deformation error. Then, the axial deformation error is modelled by the differential relationship of kinematics equations. The model of radial deformation error is deduced through the recurrence method and rotation transformation between joints. Finally, these two models are transformed into a Cartesian coordinate system, and a positioning error compensation method based on these two models is presented. Simulations based on the finite element analysis are implemented to verify the positioning error compensation method. The results show that the suggested method can efficiently predict the positioning error according to the gravity and loads, so that the positioning accuracy of industrial robots can be improved with the proposed method.

Numerical and experimental investigation of fully-coupled and uncoupled finite element model for electromagnetic forming of Aluminium Alloy Al 3014

Electromagnetic forming is a high-velocity forming process in which repulsive forces between conducting coil and workpiece are generated by mutual inductance, thus, creating opposite magnetic fields and Lorentz forces on conducting bodies. In such processes, the formability can be enhanced owing to the high-speed deformation of the material. This paper presents a fully-coupled numerical model and experimental study of the deformation behaviour of aluminium alloy (AL3014) using closed die magnetic pulse forming. The current density, magnetic flux density and Lorentz force of 1mm thick sheet were estimated numerically. The effect of deforming sheet on magnetic flux and induction was considered. As a result of which the percentage error in sheet deformation reduced from 10% for the existing uncoupled model to 5% for the fully coupled proposed model.

Comprehensive modeling method for deformation errors of different types of rolling joints in motion systems and its application in machine tools

This paper proposes a novel comprehensive modeling method for deformation errors of different types of rolling joints in machine tools to improving the analysis efficiency. This method comprehensively considers the deformation of three types of rolling joints, i.e., linear guideway, ball screw and bearing, firstly from the perspective of analytical modeling. Two comprehensive models for deformation errors of all rolling joints in two types of motion systems of machine tools are established respectively with the method. The nonlinear interfacial characteristics at rolling joints and the geometric and stiffness coupling characteristics are considered. In order to study characteristics of the synthetical deformation error of rolling joints, a synthetical error model for a five-axis machine tool is developed. Several results and optimizations of the joints are carried out. The proposed method in this paper can effectively avoid the tedious finite element modeling in the traditional analysis process and improve the efficiency.

Kinematic Chain Optimization Design Based on Deformation Sensitivity Analysis of a Five-Axis Machine Tool

This paper focuses on optimization of kinematic chain to reduce force-induced error in the scheme design phase of a five-axis machine tool. After conducting process planning on the machining target, local deformation of motion units in cutting positions is considered using a geometric way. A mathematical model is then established describing the influence of local deformation error on the general machining error through homogeneous transformation matrices. Conceptual model of the machine tool is constructed to obtain deformation distribution using static FEA. Error sensitivity coefficients are then calculated and compared among all alternative kinematic chains to select the optimized scheme. The proposed method is verified through a designing example of a five-axis vertical machining center. The optimized kinematic chain could lower the magnitude of deformation sensitivities by 9.69% and increase error equalization variance by 71.25% compared to the conventional method.

Application of Deep Learning in Dentistry and Implantology

Artificial intelligence and deep learning algorithms are infiltrating various fields of medicine and dentistry. The purpose of the current study was to review literatures applying deep learning algorithms to the dentistry and implantology. Electronic literature search through MEDLINE and IEEE Xplore library database was performed at 2019 October by combining free-text terms and entry terms associated with ‘dentistry’ and ‘deep learning’. The searched literature was screened by title/abstract level and full text level. Following data were extracted from the included studies: information of author, publication year, the aim of the study, architecture of deep learning, input data, output data, and performance of the deep learning algorithm in the study. 340 studies were retrieved from the databases and 62 studies were included in the study. Deep learning algorithms were applied to tooth localization and numbering, detection of dental caries/periodontal disease/ periapical disease/oral cancerous lesion, localization of cephalometric landmarks, image quality enhancement, prediction and compensation of deformation error in additive manufacturing of prosthesis. Convolutional neural network was used for periapical radiograph, panoramic radiograph, or computed tomography in most of included studies. Deep learning algorithms are expected to help clinicians diagnose and make decisions by extracting dental data, detecting diseases and abnormal lesions, and improving image quality.