scholarly journals Dynamic Modeling and Parameter Identification for a Gantry-type Automated Fiber Placement Machine

2021 ◽  
Author(s):  
Liang Cheng ◽  
Jianbo Wu ◽  
Yingjie Guo ◽  
Jiangxiong Li ◽  
Yinglin Ke
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Critical Role ◽  
Error Rates ◽  
Identification Algorithm ◽  
Fiber Placement ◽  
Inertial Parameters ◽  
Automated Fiber Placement ◽  
Model Linearization

Abstract Dynamic models play a critical role in the design of model-based controllers, and therefore have a significant effect on the dynamic characteristics of motion equipment. We mainly focus on the dynamic modeling and parameter identification for a gantry-type automated fiber placement (AFP) machine in this paper. First, a dynamic modeling process combining prismatic axes and revolute axes is conducted by the Newton-Euler method, in which the effects of friction and hydraulic balance system are also considered. Then, as the convenience for parameter identification and the application in linearity control, the methods of dynamic model linearization and determination of minimum inertial parameters based on the multi-body system (MBS) theory are proposed, and a dynamic model in the form of linearized minimum inertial parameters is consequently established. To identify the parameters in the model, key issues regarding excitation trajectory, filtering, and identification algorithm are discussed in detail. Finally, corresponding experiments are performed on the AFP machine, and experimental results show that there is a good agreement between the prediction of the model and the measurement in actuality. Data analysis shows that except for Z-axis, the relative error rates of the others are not greater than 5%, which proves the effectiveness of the established dynamic model and the identified parameters.

scholarly journals A Graph Theory-Based Method for Dynamic Modeling and Parameter Identification of 6-DOF Industrial Robots

2021 ◽  
Vol 11 (22) ◽  
pp. 10988
Author(s):  
Jun Cheng ◽  
Shusheng Bi ◽  
Chang Yuan ◽  
Lin Chen ◽  
Yueri Cai ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Theoretical Basis ◽  
Dynamic Parameter ◽  
Dynamic Parameters ◽  
Precision Manufacturing ◽  
Robot System ◽  
Precise Control ◽  
Dynamic Parameter Identification

At present, the absolute positioning accuracy and control accuracy of industrial serial robots need to be improved to meet the accuracy requirements of precision manufacturing and precise control. An accurate dynamic model is an important theoretical basis for solving this problem, and precise dynamic parameters are the prerequisite for precise control. The research of dynamics and parameter identification can greatly promote the application of robots in the field of precision manufacturing and automation. In this paper, we study the dynamical modeling and dynamic parameter identification of an industrial robot system with six rotational DOF (6R robot system) and propose a new method for identifying dynamic parameters. Our aim is to provide an accurate mathematical description of the dynamics of the 6R robot and to accurately identify its dynamic parameters. First, we establish an unconstrained dynamic model for the 6R robot system and rewrite it to obtain the dynamic parameter identification model. Second, we establish the constraint equations of the 6R robot system. Finally, we establish the dynamic model of the constrained 6R robot system. Through the ADAMS simulation experiment, we verify the correctness and accuracy of the dynamic model. The experiments prove that the result of parameter identification has extremely high accuracy and the dynamic model can accurately describe the 6R robot system mathematically. The dynamic modeling method proposed in this paper can be used as the theoretical basis for the study of 6R robot system dynamics and the study of dynamics-based control theory.

Identification of Vehicle Inertia Parameters: From Test Bench Design to Movement Trajectory Optimization

2018 ◽  
Author(s):  
Di Yao ◽  
Kay Büttner ◽  
Günther Prokop
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Test Bench ◽  
Movement Trajectory ◽  
Inertial Parameters ◽  
Inertia Parameters ◽  
Observation Matrix ◽  
Moving Platform ◽  
Systematic Solution ◽  
Optimal Movement

This work presents a new systematic solution to identify the vehicle inertia parameters which are essential inputs for vehicle simulation and vehicle safety research. In conceptual design phase of this work, a virtual three Degree-of-Freedom (DoF) test bench/ parallel manipulator (PM) whose moving platform is used to clamp vehicle under test is developed. In order to realize the kinematic characteristics of the proposed PM, the kinematic analysis consists of inverse kinematic and singularity architecture is carried out. Aiming at obtaining all ten vehicle inertia parameters (i.e., mass, center of gravity and inertia tensor), the observation matrix for parameter identification is derived from the dynamic model of PM. To get the dynamic model, the Euler’s equation and Lagrange approach are applied to implement the dynamic analysis for PM’s moving platform and actuators, respectively. It is beneficial to reduce the complexity of dynamic model and load of numerical computation. In the following section, to minimize the sensitivity of parameter identification to measurement noise, an optimization process of searching for the optimal movement trajectory of PM is proposed. For this purpose, the parameterized finite-Fourier-series are used to definite the general movement trajectory of PM firstly. Subsequently, the parameters of general trajectory are optimized by employing a nonlinear iterative algorithm. Objective of this algorithm is to obtain the minimal condition number of observation matrix and meanwhile to ensure the PM still works in the achievable working space during the test. The results show that the vehicle inertial parameters can be effectively identified by executing the single optimal movement trajectory on the PM. It is expected that the proposed systematic solution could be an important approach to improve the identification efficiency and identification accuracy of vehicle inertial parameters.

Kinematic modeling and parameter identification for a heavy gantry-type automated fiber placement machine considering gravity deformation

2020 ◽  
pp. 095440622094572
Author(s):  
Jianbo Wu ◽  
Liang Cheng ◽  
Yunbo Bi ◽  
Jiangxiong Li ◽  
Yinglin Ke
Keyword(s):  
Parameter Identification ◽  
Kinematic Model ◽  
Measurement Data ◽  
Joint Interface ◽  
Deformation Model ◽  
Kinematic Modeling ◽  
Fiber Placement ◽  
Position Errors ◽  
Automated Fiber Placement ◽  
Placement Machine

Automated fiber placement machine is the key equipment for low-cost and automated manufacturing of high-performance carbon composite materials. In order to meet the required position accuracy of fiber placement, this paper focuses on the kinematic modeling and parameter identification of the automated fiber placement machine. A kinematic model taking account of geometric deviations is established firstly. Since joint interfaces are the main origin of gravity deformation in a machine tool, an elastic beam deformation model is introduced to represent the joint interface, and then the former kinematic model is modified by analytical expressions of the gravity deformation for each joint interface. Based on the measurement data and the Levenberg-Marquardt optimization method, the parameter identification of the kinematic model is realized, and main issues such as measurement data selection, objective function definition are discussed. Finally, a kinematic calibration experiment is performed, and the experimental results verify the feasibility and validity of the modeling method. The position errors in Z direction of the automated fiber placement machine are effectively reduced by over 80%, which suggests that the proposed method reduces the effect of the gravity deformation and improves the accuracy of the automated fiber placement machine.

Automated Fiber Placement of Composite Wind Tunnel Blades: Process Planning and Manufacturing

2019 ◽  
Author(s):  
Ramy Harik ◽  
Joshua Halbritter ◽  
Dawn Jegley ◽  
Ray Grenoble ◽  
Brian Mason
Keyword(s):  
Wind Tunnel ◽  
Process Planning ◽  
Fiber Placement ◽  
Automated Fiber Placement

scholarly journals Review: Filament Winding and Automated Fiber Placement with In Situ Consolidation for Fiber Reinforced Thermoplastic Polymer Composites

Polymers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1951
Author(s):  
Yi Di Boon ◽  
Sunil Chandrakant Joshi ◽  
Somen Kumar Bhudolia
Keyword(s):  
Processing Parameters ◽  
Filament Winding ◽  
Reference Points ◽  
Thermoplastic Composites ◽  
Manufacturing Processes ◽  
Fiber Reinforced ◽  
Consolidation Process ◽  
Fiber Placement ◽  
Automated Fiber Placement

Fiber reinforced thermoplastic composites are gaining popularity in many industries due to their short consolidation cycles, among other advantages over thermoset-based composites. Computer aided manufacturing processes, such as filament winding and automated fiber placement, have been used conventionally for thermoset-based composites. The automated processes can be adapted to include in situ consolidation for the fabrication of thermoplastic-based composites. In this paper, a detailed literature review on the factors affecting the in situ consolidation process is presented. The models used to study the various aspects of the in situ consolidation process are discussed. The processing parameters that gave good consolidation results in past studies are compiled and highlighted. The parameters can be used as reference points for future studies to further improve the automated manufacturing processes.

Effect of gap on strengths of automated fiber placement manufactured laminates

2021 ◽  
Vol 263 ◽  
pp. 113677
Author(s):  
Hiroshi Suemasu ◽  
Yuichiro Aoki ◽  
Sunao Sugimoto ◽  
Toshiya Nakamura
Keyword(s):  
Fiber Placement ◽  
Automated Fiber Placement

scholarly journals Parameter Estimation of a Countershaft Brake for Heavy-Duty Vehicles with Automated Mechanical Transmission

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1036
Author(s):  
Yunxia Li ◽  
Lei Li
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Dynamic Model ◽  
Control Flow ◽  
Mechanical Transmission ◽  
Equivalent Resistance ◽  
Heavy Duty ◽  
Characteristic Parameters ◽  
Resistance Torque ◽  
Heavy Duty Vehicles

A countershaft brake is used as a transmission brake (TB) to realize synchronous shifting by reducing the automated mechanical transmission (AMT) input shaft’s speed rapidly. This process is performed to reduce shifting time and improve shifting quality for heavy-duty vehicles equipped with AMT without synchronizer. To improve controlled synchronous shifting, the AMT input shaft’s equivalent resistance torque and the TB’s characteristic parameters are studied. An AMT dynamic model under neutral gear position is analyzed during the synchronous control interval. A dynamic model of the countershaft brake is discussed, and its control flow is given. The parameter identification method of the AMT input shaft’s equivalent resistance torque is given on the basis of the least squares algorithm. The parameter identification of the TB’s characteristic parameters is proposed on the basis of the recursive least squares method (RLSM). Experimental results show that the recursive estimations of the TB’s characteristic parameters under different duty cycles of the TB solenoid valve, including brake torque estimation, estimation accuracy, and braking intensity estimation, can be effectively estimated. The research provides some reliable evidence to further study the synchronous shifting control schedule for heavy-duty vehicles with AMT.

scholarly journals Experimental Investigation on the Influence of Fiber Path Curvature on the Mechanical Properties of Composites

Materials ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2602
Author(s):  
Huaqiao Wang ◽  
Jihong Chen ◽  
Zhichao Fan ◽  
Jun Xiao ◽  
Xianfeng Wang
Keyword(s):  
Tensile Strength ◽  
Path Planning ◽  
Composite Laminates ◽  
Bending Strength ◽  
High Strength ◽  
Bending Test ◽  
Fiber Placement ◽  
Automated Fiber Placement ◽  
Fiber Path ◽  
Path Curvature

Automated fiber placement (AFP) has been widely used as an advanced manufacturing technology for large and complex composite parts and the trajectory planning of the laying path is the primary task of AFP technology. Proposed in this paper is an experimental study on the effect of several different path planning placements on the mechanical behavior of laminated materials. The prepreg selected for the experiment was high-strength toughened epoxy resin T300 carbon fiber prepreg UH3033-150. The composite laminates with variable angles were prepared by an eight-tow seven-axis linkage laying machine. After the curing process, the composite laminates were conducted by tensile and bending test separately. The test results show that there exists an optimal planning path among these for which the tensile strength of the laminated specimens decreases slightly by only 3.889%, while the bending strength increases greatly by 16.68%. It can be found that for the specific planning path placement, the bending strength of the composite laminates is significantly improved regardless of the little difference in tensile strength, which shows the importance of path planning and this may be used as a guideline for future AFP process.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

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