Model Learning in a Multistage Machining Process: Online Identification of Force Coefficients and Model Use in the Manufacturing Enterprise

2013 ◽  
Author(s):  
Parikshit Mehta ◽  
Laine Mears
Keyword(s):  
Process Control ◽  
Least Square ◽  
Machining Process ◽  
Force Model ◽  
Recursive Least Square ◽  
Force Coefficient ◽  
Model Learning ◽  
Force Coefficients ◽  
Control Objective ◽  
And Control

This work presents a systems approach in machining process control. Traditional force-based machining process control has been focused on single machine-single operation. The force or power sensor is used to measure the instantaneous force/power, and control action is taken by changing the feedrate in real time to follow a given force setpoint. The application of such control has successfully been implemented to prevent chatter and to elongate tool life by minimizing tool wear. This research seeks to extend the application of control algorithms to learn about the machining system (comprised in this context of a workpiece being operated on in progressive machining), and how knowledge generated by the process can be passed on to the next process for optimization. To demonstrate this, turning of a partially hardened bar is explored. A nonlinear mechanistic force model-based control framework attempts to control the cutting force at a designated setpoint, with material properties changing over the cut. The force coefficients for the material are calculated offline using experimental data and Bayesian inference methods. Since the hardened part of the bar will shift the force coefficient values, an online estimation strategy (Bayesian Recursive Least Square estimator) is used to learn the new coefficients as well as satisfying the control objective. With the newly learned coefficients passed downstream, the subsequent operation experiences no compromise of control objective as well reduces the maximum values of force encountered. Numerical analyses presented show the adaptation and control scheme performance.

Identification of Polynomial Cutting Coefficients for a Dual-Mechanism Ball-end Milling Force Model

2019 ◽  
Vol 13 (3) ◽  
pp. 232-240
Author(s):  
Zhixin Feng ◽  
Meng Liu ◽  
Guohe Li
Keyword(s):  
Cutting Force ◽  
End Milling ◽  
Least Square ◽  
Force Model ◽  
Cutting Force Coefficients ◽  
Milling Force ◽  
Ball End Milling ◽  
Force Coefficients ◽  
Cutting Coefficients ◽  
Milling Force Model

Background: Calibration of cutting coefficients is the key content in modeling a mechanistic cutting force model. Generally, in modeling cutting force for ball end milling, the tangent, radial and binormal cutting force coefficients are each considered as a polynomial, respectively. This fact is due to the dependency between the cutting force coefficients and the cutting edge inclination angle which is variable in ball-end mills. Objective: This paper presents an approach to determine the polynomial cutting force coefficients. Methods: In this approach, the cutting force coefficients are expressed as explicit linear equations about the average slotting forces. After analysis of the least square regression method which is utilized in the cutting coefficients evaluation, the principle of cutting parameters choice in calibration experiment and the relationship between the order of polynomial and the number of experiments are presented. Besides, a lot of patents on identification of polynomial cutting coefficients for milling force model were studied. Results: Finally, a series of semi-slotting verification cutting tests were arranged, the measured force agrees well with the predicted force, which demonstrates the effectiveness of this approach. Conclusion: Based on the calibration method proposed in this paper, the cutting coefficients can be determined through (m+2) slotting experiments for m-degree shearing coefficients polynomial theoretically.

Non-Linear Complementary SO(3) Filters for Attitude Estimation and Navigation of Ground Vehicles

2014 ◽  
Vol 663 ◽  
pp. 254-258
Author(s):  
Fargham Sandhu ◽  
Hazlina Selamat ◽  
Yahaya Md Sam
Keyword(s):  
Low Cost ◽  
Attitude Estimation ◽  
Least Square ◽  
Low Grade ◽  
Ground Vehicles ◽  
Recursive Least Square ◽  
Stability And Control ◽  
Filter Approach ◽  
Navigational System ◽  
And Control

The use of Inertial Navigational System (INS) has been proven to be suitable for vehicular stability and control. The same system can be used for inertial based navigation in the absence of GPS. In this paper, the problem of obtaining good attitude estimates from low cost sensors used for car navigation in the absence of GPS data is discussed. The states to be estimated are using angular velocity and linear accleration signals obtained from the sets of gyros and accelerometers of the INS. The special orthogonal group, the SO(3)-based complementary filters, have been used as the estimators as they are most suited for embedded systems to generate highly efficient algorithms for navigation. The INS has also been integrated with a set of magnetometers to assist in achieving global navigation. This integration requires kinematics equations as well as the inclusion of the gyro and accelerometer calibration and filtering. By using the quatronion representation, not only highly compact algorithms for integration can be generated, but it can also estimate and remove the effects of other biases and misalignments caused by, for instance, inaccurate installations and inherent sensors problems. The results obtained through simulation indicate better performance then Kalman filter approach as well as iterative recursive least square approach even with low grade sensors. The results are comparable with attitude estimation using roll index but with much less computations and better performance.

Modular CNC Design for Intelligent Machining, Part 2: Modular Integration of Sensor Based Milling Process Monitoring and Control Tasks

1996 ◽  
Vol 118 (4) ◽  
pp. 514-521 ◽  
Author(s):  
Y. Altintas¸ ◽  
W. K. Munasinghe
Keyword(s):  
Process Control ◽  
Real Time ◽  
Process Monitoring ◽  
Tool Path ◽  
Position Control ◽  
Milling Process ◽  
Machining Process ◽  
Monitoring And Control ◽  
Process Monitoring And Control ◽  
And Control

Modular integration of sensor based milling process monitoring and control functions to a proposed CNC system architecture is presented. Each sensor based process control algorithm resides in a dedicated processor in the AT bus with a modular software. The CNC system’s motion control module has been designed to accomodate rapid manipulation of feeds, cutting conditions and NC tool path which may be demanded by machining process control modules in real time. Modular integration of adaptive control of cutting forces, tool condition monitoring, chatter detection and suppression tasks are illustrated as examples. The process control and monitoring modules are serviced in the real-time multi-tasking environment within one millisecond time intervals without disturbing the position control system. The paper present constraints and guidelines in designing CNC systems which allow modular integration of user developed real time machining process control and monitoring applications.

Effect of Lubrication Conditions to the Cutting Force Coefficients in Machining Process

2015 ◽  
Vol 1115 ◽  
pp. 55-58
Author(s):  
Wan Mohd Azlan Nowalid ◽  
Muhammad Adib Shaharun ◽  
Ahmad Razlan Yusoff
Keyword(s):  
Cutting Force ◽  
Metal Cutting ◽  
Machining Process ◽  
Force Model ◽  
Cutting Force Model ◽  
Cutting Force Coefficients ◽  
Milling Force ◽  
Force Coefficients ◽  
Milling Forces ◽  
Lubrication Conditions

The cutting force is the main important factor contributing the machined work piece surface and in determining the acceptable cutting parameters for high productivity in metal cutting industries. The prediction of cutting force coefficients of materials were calculated from the average cutting force model contributing to the constants of cutting force coefficients. In this study, experimental investigation is conducted to determine the cutting force coefficients in the average cutting force model, by identifying cutting force coefficients with different lubrication conditions such as dry, flood and minimal lubrication conditions and cutting speeds. A series of slot milling experiments are measured the milling forces by fixing the spindle speeds and radial/axial depths of cutting and linearly varying the feed per tooth. Using linearly fitting the experimental data, the tangential and radial milling force coefficients are then computed. The achieved results showed that the changing of spindle speed and different lubrication conditions affecting the milling force coefficient.

scholarly journals Recursive least square and control for PUMA robotics

2021 ◽  
Vol 21 (2) ◽  
pp. 1238
Author(s):  
Lafta E. Jumaa Alkurawy
Keyword(s):  
Real Time ◽  
Inverse Kinematics ◽  
Algebraic Method ◽  
Pi Controller ◽  
Least Square ◽  
Recursive Least Square ◽  
End Effector ◽  
And Control ◽  
Reference Input

<p>The solution of inverse kinematics system based on recursive least square (RLS) theorem is improved this paper. The task in joints of robotics is inverse kinematics for PUMA robotics. The design the manipulator of robotics is not simple if due to model of algebraic method. I suggested a method of RLS method to get predicts the positions of robot and it is comfortable the applications in real-time.<strong> </strong>The RLS is used to find the solution of the inverse kinematics for the joints 6-dof of the robotics. This technique is important to compute the joints of each arm space with Cartesian axes in the end-effector. The identification will be in each joint for PUMA by RLS and applied PI controller on each joint to get the response follows the reference input by tuning the values of coefficients of PI.</p>

An Implicit Quadratic H∞ Self-Tuning Controller

1991 ◽  
Vol 113 (4) ◽  
pp. 729-735 ◽  
Author(s):  
R. A. Hashim ◽  
M. J. Grimble
Keyword(s):  
Diophantine Equations ◽  
Simple Procedure ◽  
Optimal Solution ◽  
Least Square ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Control Signals ◽  
Control Scheme ◽  
Self Tuning ◽  
And Control

An implicit H∞ self-tuning control scheme is presented. Costing of the system error and control signals is achieved using a dynamic cost function. The H∞ optimal solution to this problem is obtained using a recursive least square identification algorithm. The simple procedure for calculating the controller, without solving any diophantine equations, make this method particularly suitable for self-tuning control applications.

Vehicle State Estimation for Rollover Avoidance

2008 ◽  
Author(s):  
Jihua Huang
Keyword(s):  
State Estimation ◽  
Least Square ◽  
Lateral Acceleration ◽  
Extreme Condition ◽  
Recursive Least Square ◽  
Nonlinear Region ◽  
Main Challenge ◽  
Roll Velocity ◽  
Vehicle State Estimation ◽  
And Control

To enhance vehicle/road safety, rollover warning and control systems have received considerable research interest in recent years, especially for vehicles with high center of gravity (CG). Accurate and reliable estimates of the relevant vehicle states facilitate the design of such systems. This paper investigates the state estimation for rollover avoidance, in which the relevant states include vehicle roll velocity and roll angle, as well as sideslip velocity and yaw velocity. The main challenge of the design comes from the fact that, under near-rollover situations, vehicle dynamics is complex and nonlinear. Not only vehicle suspension and tires are in their nonlinear region, but also vehicle yaw, sideslip and roll motions are highly coupled. In addition, the estimation needs to deal with sensor biases and sensor nonlinearity under this extreme condition. To address those issues, this paper proposes a vehicle state estimation design that consists of three parts: a sensor pre-filter, an Extended Kalman filter (EKF), and a sideslip velocity estimator. The sensor pre-processor removes sensor biases by utilizing the Recursive Least Square technique with a varying forgetting factor. The EKF is designed based on a linear yaw/sideslip/roll model, and its feedback gains are further scheduled based on vehicle lateral acceleration in order to reduce the effects of increased model inaccuracy as vehicle roll motion becomes more severe. The sideslip velocity estimator adjusts the sideslip velocity estimated by the EKF to extend the estimation to the nonlinear region. Both simulation and vehicle fishhook testing have been used to verify the effectiveness of the design.

Primitive Study on Model-Based Process Identification by Utilizing Force Estimation Techniques

2016 ◽  
Author(s):  
Norikazu Suzuki ◽  
Ryosuke Ikeda ◽  
Eiji Shamoto
Keyword(s):  
Transfer Function ◽  
Cutting Force ◽  
Least Square Method ◽  
Least Square ◽  
Numerical Control ◽  
Cutting Process ◽  
Force Model ◽  
Force Estimation ◽  
Force Coefficient ◽  
Cutting Force Coefficient

This study presents a new method to identify parameters representing cutting process and transfer function of flexible mechanical structures mounted on a traveling stage by utilizing only internal information of computerized numerical control (CNC) system. Disturbance force input to CNC is estimated by disturbance observer and cutting force is estimated based on cutting force model. Analyzing influence of the estimated cutting force on the disturbance force, parameters used in the assumed models of cutting process and structural dynamics are identified in quasi-real-time. Least square method (LSM) is utilized for the parameter identification. Face turning experiment using an ultra-precision machine tool was conducted to verify feasibility of the proposed method. Experimental results clarified that the cutting force coefficient and the modal parameters representing the dynamic characteristics of the force transfer function can be identified accurately by the proposed method.

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