scholarly journals Estimating dynamic cutting forces of machine tools from measured vibrations using sparse regression with nonlinear function basis

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yongzhi Qu ◽  
Gregory W. Vogl
Keyword(s):  
Linear Regression ◽  
Cutting Forces ◽  
Machine Tools ◽  
Transfer Functions ◽  
Nonlinear Function ◽  
Data Driven ◽  
Physical Parameters ◽  
Input Output ◽  
Input Estimation ◽  
Inputs And Outputs

Estimating relationships between system inputs and outputs can provide insight to system characteristics. Furthermore, with an established input-output relationship and measured output, one can estimate the corresponding input to the system. Traditionally, the relationship between input and output can be represented with transfer functions or frequency response functions. However, those functions need to be built on physical parameters, which are hard to obtain in practical systems. Also, the reverse problem of solving for the input with a known/measured output is often more difficult to solve than the forward problem. This paper aims to explore the data-driven input-output relationship between system inputs and outputs for system diagnostics, prognostics, performance prediction, and control. A data-driven relationship can provide a new way for system input estimation or output prediction. In this paper, a sparse linear regression model with nonlinear function basis is proposed for input estimation with measured outputs. The proposed method explicitly creates a nonlinear function basis for the regression relationship. A threshold-based sparse linear regression is designed to ensure sparsity. The method is tested with experimental data from a spindle testbed that simulates cutting forces within machine tools. The results show that the proposed approach can predict the input force based on the measured vibration response with high accuracy. The current model is also compared with neural networks, which is another nonlinear regression method.

scholarly journals A surrogate-assisted optimization approach for multi-response end milling of aluminum alloy AA3105

2020 ◽  
Vol 111 (9-10) ◽  
pp. 2419-2439
Author(s):  
Tamal Ghosh ◽  
Yi Wang ◽  
Kristian Martinsen ◽  
Kesheng Wang
Keyword(s):  
Surface Roughness ◽  
Aluminum Alloy ◽  
Material Removal Rate ◽  
Cutting Forces ◽  
Material Removal ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Parameters ◽  
Data Driven ◽  
Optimal Cutting Parameters

Abstract Optimization of the end milling process is a combinatorial task due to the involvement of a large number of process variables and performance characteristics. Process-specific numerical models or mathematical functions are required for the evaluation of parametric combinations in order to improve the quality of the machined parts and machining time. This problem could be categorized as the offline data-driven optimization problem. For such problems, the surrogate or predictive models are useful, which could be employed to approximate the objective functions for the optimization algorithms. This paper presents a data-driven surrogate-assisted optimizer to model the end mill cutting of aluminum alloy on a desktop milling machine. To facilitate that, material removal rate (MRR), surface roughness (Ra), and cutting forces are considered as the functions of tool diameter, spindle speed, feed rate, and depth of cut. The principal methodology is developed using a Bayesian regularized neural network (surrogate) and a beetle antennae search algorithm (optimizer) to perform the process optimization. The relationships among the process responses are studied using Kohonen’s self-organizing map. The proposed methodology is successfully compared with three different optimization techniques and shown to outperform them with improvements of 40.98% for MRR and 10.56% for Ra. The proposed surrogate-assisted optimization method is prompt and efficient in handling the offline machining data. Finally, the validation has been done using the experimental end milling cutting carried out on aluminum alloy to measure the surface roughness, material removal rate, and cutting forces using dynamometer for the optimal cutting parameters on desktop milling center. From the estimated surface roughness value of 0.4651 μm, the optimal cutting parameters have given a maximum material removal rate of 44.027 mm3/s with less amplitude of cutting force on the workpiece. The obtained test results show that more optimal surface quality and material removal can be achieved with the optimal set of parameters.

Chaotic motion control of a vibro-impact system based on data-driven control method

2021 ◽  
pp. 107754632110433
Author(s):  
Xiao-juan Wei ◽  
Ning-zhou Li ◽  
Wang-cai Ding
Keyword(s):  
Motion Control ◽  
Data Model ◽  
Chaotic Motion ◽  
Control Method ◽  
Data Driven ◽  
Damping Coefficient ◽  
Input Output ◽  
Output Data ◽  
Controlled System ◽  
Impact System

For the chaotic motion control of a vibro-impact system with clearance, the parameter feedback chaos control strategy based on the data-driven control method is presented in this article. The pseudo-partial-derivative is estimated on-line by using the input/output data of the controlled system so that the compact form dynamic linearization (CFDL) data model of the controlled system can be established. And then, the chaos controller is designed based on the CFDL data model of the controlled system. And the distance between two adjacent points on the Poincaré section is used as the judgment basis to guide the controller to output a small perturbation to adjust the damping coefficient of the controlled system, so the chaotic motion can be controlled to a periodic motion by dynamically and slightly adjusting the damping coefficient of the controlled system. In this method, the design of the controller is independent of the order of the controlled system and the structure of the mathematical model. Only the input/output data of the controlled system can be used to complete the design of the controller. In the simulation experiment, the effectiveness and feasibility of the proposed control method in this article are verified by simulation results.

scholarly journals DIFFUSIVE REPRESENTATIONS FOR THE ANALYSIS AND SIMULATION OF FLARED ACOUSTIC PIPES WITH VISCO-THERMAL LOSSES

2006 ◽  
Vol 16 (04) ◽  
pp. 503-536 ◽  
Author(s):  
TH. HÉLIE ◽  
D. MATIGNON
Keyword(s):  
Finite Order ◽  
Acoustic Waves ◽  
Complex Analysis ◽  
Differential Operators ◽  
Transfer Functions ◽  
Representation Formula ◽  
Input Output ◽  
Numerical Approximations ◽  
Pseudo Differential Operators ◽  
Scattering Matrices

Acoustic waves travelling in axisymmetric pipes with visco-thermal losses at the wall obey a Webster–Lokshin model. Their simulation may be achieved by concatenating scattering matrices of elementary transfer functions associated with nearly constant parameters (e.g. curvature). These functions are computed analytically and involve diffusive pseudo-differential operators, for which we have representation formula and input-output realizations, yielding direct numerical approximations of finite order. The method is based on some involved complex analysis.

Application of Mixed H2/H∞ Data Driven Control Design to Dual Stage Hard Disk Drives

2018 ◽  
Author(s):  
Omid Bagherieh ◽  
Prateek Shah ◽  
Roberto Horowitz
Keyword(s):  
Closed Loop ◽  
Transfer Functions ◽  
Hard Disk Drives ◽  
Control Design ◽  
Hard Disk ◽  
Data Driven ◽  
Disk Drives ◽  
Design Strategies ◽  
Data Driven Approach ◽  
Dual Stage

A data driven control design approach in the frequency domain is used to design track following feedback controllers for dual-stage hard disk drives using multiple data measurements. The advantage of the data driven approach over model based approach is that, in the former approach the controllers are directly designed from frequency responses of the plant, hence avoiding any model mismatch. The feedback controller is considered to have a Sensitivity Decoupling Structure. The data driven approach utilizes H∞ and H2 norms as the control objectives. The H∞ norm is used to shape the closed loop transfer functions and ensure closed loop stability. The H2 norm is used to constrain and/or minimize the variance of the relevant signals in time domain. The control objectives are posed as a locally convex optimization problem. Two design strategies for the dual-stage hard disk drive are presented.

scholarly journals Tracking—A

2021 ◽  
pp. 163-192
Author(s):  
Jean Walrand
Keyword(s):  
Kalman Filter ◽  
Linear Regression ◽  
Least Squares ◽  
Recursive Algorithm ◽  
Nonlinear Function ◽  
Random Variables ◽  
Random Variable ◽  
Linear Least Squares ◽  
Least Squares Estimate ◽  
Related Notion

AbstractThis chapter explains how to estimate an unobserved random variable or vector from available observations. This problem arises in many examples, as illustrated in Sect. 9.1. The basic problem is defined in Sect. 9.2. One commonly used approach is the linear least squares estimate explained in Sect. 9.3. A related notion is the linear regression covered in Sect. 9.4. Section 9.5 comments on the problem of overfitting. Sections 9.6 and 9.7 explain the minimum means squares estimate that may be a nonlinear function of the observations and the remarkable fact that it is linear for jointly Gaussian random variables. Section 9.8 is devoted to the Kalman filter, which is a recursive algorithm for calculating the linear least squares estimate of the state of a system given previous observations.

scholarly journals Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison

2020 ◽  
Vol 197 ◽  
pp. 10003
Author(s):  
Simone Ghettini ◽  
Alessandro Sorce ◽  
Roberto Sacile
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Ambient Temperature ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Empirical Model ◽  
Power Plants ◽  
Data Driven ◽  
Semi Empirical

This paper presents a data–driven model for the estimation of the performance of an aircooled steam condenser (ACC) with the aim to develop an efficient online monitoring, summarized by the condenser pressure (or vacuum) as Key Performance Indicator. The estimation of the ACC performance model was based on different dataset from three different combined cycle power plants with a gross power of above 380 MWe each, focusing on stationary condition of the steam turbine. The datasets include both boundary (e.g. Ambient Temperature, Wind Speed) and operative parameters (e.g. steam mass flow rate, Steam turbine power, electrical load of the ACC fans) acquired from the power plants and some derived variable as the incondensable fraction, which calculation is here proposed as additional parameter. After a preliminary sensitivity analysis on data correlation, the paper focuses on the evaluation of different ACC Condenser models: Semi-Empirical model is described trough curves typically based on steam mass flow rate (or condenser load) and the ambient temperature as main parameters. Since monitoring based on ACC design curves Semi-Empirical models, provides biased poor results, with an error of about 15%, the curves parameters were estimated basing on training data set. Other two data driven models were presented, basing on a neural network modelling and multi linear regression technique and compared on the base of the reduced number of input at first and then including aldo the other process variables in the prediction of the condenser back pressure. Estimate the parameters of the Semi-Empirical model, results in a better prediction if just steam mass flow rate and ambient temperature are available, with an error of the 7%, thanks to the knowledge contained within the “curves shapes”, with respect to linear regression (8.3%) and Neural Network models (7.6%). Higher accuracy can be then obtained by considering a larger number of operative parameters and exploiting more complex data-driven model. With a higher number of features, the neural network model has proved a higher accuracy than the linear regression model. In fact, the mean percentage error of the NN model (2.6%), in all plant operating conditions, is slightly lower than the error of the linear regression model, but presents and much lower than the mean error of the Semi-Empirical model thanks to the additional data-based knowledge.

Datengetriebene Steigerung der Verfügbarkeit/Data-supported root cause analysis to increase the availability of machine tools. Data-driven availability increase

2020 ◽  
Vol 110 (07-08) ◽  
pp. 532-535
Author(s):  
Eckhart Uhlmann ◽  
Roman Dumitrescu ◽  
Julian Polte ◽  
Maurice Meyer ◽  
Deniz Simsek
Keyword(s):  
Data Analysis ◽  
Machine Tools ◽  
Critical Factor ◽  
Root Cause Analysis ◽  
Data Driven ◽  
Manufacturing Companies ◽  
Cause Analysis ◽  
Root Cause ◽  
Comprehensive Data ◽  
Causes Of Failure

Die Zuverlässigkeit von Werkzeugmaschinen ist ein kritischer Faktor für den Erfolg produzierender Unternehmen. Durch die Analyse von Daten in der Produktplanung können Maschinenhersteller Ausfallursachen eliminieren und Maschinen systematisch verbessern. Jedoch stellt eine umfassende Datenanalyse viele Unternehmen vor große Herausforderungen. Die in diesem Beitrag vorgestellte Methodik adressiert diese Problematik und unterstützt Unternehmen bei der zielgerichteten Datenanalyse.   The reliability of machine tools is a critical factor for the success of manufacturing companies. By analyzing data in product planning, machine manufacturers can eliminate causes of failure and systematically improve machines. However, comprehensive data analysis poses great challenges for many companies. The methodology presented in this paper addresses this problem and supports companies in the goal-driven data analysis.

A Design Method for Two-Degree-of-Freedom Simple Multi-Period Repetitive Control Systems

2010 ◽  
Vol 36 ◽  
pp. 243-252 ◽  
Author(s):  
Yoshinori Ando ◽  
Tatsuya Sakanushi ◽  
Kou Yamada ◽  
Iwanori Murakami ◽  
Takaaki Hagiwara ◽  
...  
Keyword(s):  
Control System ◽  
Control Systems ◽  
Transfer Functions ◽  
Design Method ◽  
Output Characteristic ◽  
Degree Of Freedom ◽  
Disturbance Attenuation ◽  
Input Output ◽  
Attenuation Characteristic ◽  
Two Degree Of Freedom

The multi-period repetitive (MPR) control system is a type of servomechanism for periodic reference inputs. Using MPR controllers, transfer functions from the reference input to the output and from the disturbance to the output of the MPR control system have infinite numbers of poles. To specify the input-output characteristic and the disturbance attenuation characteristic easily, Yamada and Takenaga proposed MPR control systems, named simple multi-period repetitive (simple MPR) control systems, where these transfer functions have finite numbers of poles. In addition, Yamada and Takenaga clarified the parameterization of all stabilizing simple MPR controllers. However, using the simple MPR repetitive controller by Yamada and Takenaga, we cannot specify the input-output characteristic and the disturbance attenuation characteristic separately. From the practical point of view, it is desirable to specify the input-output characteristic and the disturbance attenuation characteristic separately. The purpose of this paper is to propose the parameterization of all stabilizing two-degree-of-freedom (TDOF) simple MPR controllers that can specify the input-output characteristic and the disturbance attenuation characteristic separately.

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