Real-Time Identification of Incipient Surface Morphology Variations in Ultraprecision Machining Process

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Prahalad Rao ◽  
Satish Bukkapatnam ◽  
Omer Beyca ◽  
Zhenyu (James) Kong ◽  
Ranga Komanduri
Keyword(s):  
Surface Morphology ◽  
Real Time ◽  
Process Monitoring ◽  
Diamond Turning ◽  
Machining Process ◽  
Complex Signal ◽  
Ultraprecision Machining ◽  
Real Time Monitoring ◽  
Process Dynamics ◽  
Sensor Signals

Real-time monitoring and control of surface morphology variations in their incipient stages are vital for assuring nanometric range finish in the ultraprecision machining (UPM) process. A real-time monitoring approach, based on predicting and updating the process states from sensor signals (using advanced neural networks (NNs) and Bayesian analysis) is reported for detecting the incipient surface variations in UPM. An ultraprecision diamond turning machine is instrumented with three miniature accelerometers, a three-axis piezoelectric dynamometer, and an acoustic emission (AE) sensor for process monitoring. The machine tool is used for face-turning aluminum 6061 discs to a surface finish (Ra) in the range of 15–25 nm. While the sensor signals (especially the vibration signal in the feed direction) are sensitive to surface variations, the extraneous noise from the environment, machine elements, and sensing system prevents direct use of raw signal patterns for early detection of surface variations. Also, nonlinear and time-varying nature of the process dynamics does not lend conventional statistical process monitoring techniques suitable for characterizing UPM-machined surfaces. Consequently, instead of just monitoring the raw sensor signal patterns, the nonlinear process dynamics wherefrom the signal evolves are more effectively captured using a recurrent predictor neural network (RPNN). The parameters of the RPNN (weights and biases) serve as the surrogates of the process states, which are updated in real-time, based on measured sensor signals using a Bayesian particle filter (PF) technique. We show that the PF-updated RPNN can effectively capture the complex signal evolution patterns. We use a mean-shift statistic, estimated from the PF-estimated surrogate states, to detect surface variation-induced changes in the process dynamics. Experimental investigations show that variations in surface characteristics can be detected within 15 ms of their inception using the present approach, as opposed to 30 ms or higher with the conventional statistical change detection methods tested.

Method for Evaluating Machining Toolholder-Spindle Unbalance Due to Chips at the Toolholder Interface

2010 ◽  
Author(s):  
John Agapiou
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Diagnostic System ◽  
Machining Process ◽  
Monitoring Method ◽  
Spindle System ◽  
On Line

Machining process monitoring method is developed for detecting and diagnosis of the presence of chips at the toolholder-spindle interface. Although toolholders can be simply balanced before they are placed in the spindle, there can be some balancing problems remaining when one or more loose machining chips are attached at the toolholder-spindle interface(s) during a tool change. A method is developed by considering the natural and geometric unbalances of the toolholder-spindle system combined with an analysis of the toolholder tilt due to the presence of loose machining chips around the spindle. The method can be integrated on-line as a real-time expert diagnostic system for toolholder tilt due to the presence of loose machining chips at the spindle nose. The expert diagnostic system makes intelligent decisions on toolholder unbalance and concerns with chips at the interface that result in unwanted tilting and vibrations. The tool unbalance algorithm was able to monitor the toolholder tilting according to the results of this study.

scholarly journals Real-Time Monitoring and Diagnosis Platform for a Machining Process

2008 ◽  
Vol 41 (2) ◽  
pp. 10674-10679
Author(s):  
E. Portillo ◽  
M. Marcos ◽  
I. Cabanes ◽  
D. Orive ◽  
J.A. Sánchez
Keyword(s):  
Real Time ◽  
Machining Process ◽  
Real Time Monitoring ◽  
Monitoring And Diagnosis

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.

Real time Monitoring of Process Dynamics in Anammox Reactors

2013 ◽  
Vol 2013 (4) ◽  
pp. 47-63 ◽  
Author(s):  
Bilge Alpaslan Kocamemi ◽  
Duygu Dityapak
Keyword(s):  
Real Time ◽  
Real Time Monitoring ◽  
Process Dynamics

scholarly journals Process Monitoring Dataset from the Additive Manufacturing Metrology Testbed (AMMT): “Three-Dimensional Scan Strategies”

2019 ◽  
Vol 124 ◽  
Author(s):  
Brandon Lane ◽  
Ho Yeung
Keyword(s):  
Additive Manufacturing ◽  
Real Time ◽  
Process Monitoring ◽  
Three Dimensional ◽  
Monitoring Data ◽  
Real Time Monitoring ◽  
3D Scan ◽  
Input Command

This document provides details on the files available in the dataset "20180708-HY-3D Scan Strategies" pertaining to a 3D additive manufacturing build performed on the Additive Manufacturing Metrology Testbed (AMMT)by Ho Yeung on July 8, 2018. The files include the input command files and in-situ process monitoring data, and metadata. This data is the first of future planned "AMMT Process Monitoring Reference Datasets," as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project.

Research on Real-Time Process Monitoring Technology and System for High-Speed Turbopump

2009 ◽  
Vol 413-414 ◽  
pp. 463-469
Author(s):  
L.R. Xia ◽  
Niao Qing Hu ◽  
G.J. Qin
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Process Monitoring ◽  
Digital Signal Processor ◽  
High Speed ◽  
Time Process ◽  
Real Time Monitoring ◽  
Monitoring Technology ◽  
The Real ◽  
Multiple Domain

Turbopump is a high-fault-rate component in Liquid Rocket Engine (LRE). The research on real-time process monitoring technology and system for turbopump is vital to increase the reliability and safety of LRE. In this paper, three real-time process monitoring algorithms for turbopump were studied firstly, on the basis of monitoring parameters selection and multiple domain feature extraction. Then, the real-time monitoring system realized by means of PXI (PCI eXtensions for Instrumentation) controller was introduced. And, aiming at the shortage of the previous system, the real-time process monitoring system based on DSP (Digital Signal Processor) for turbopump was studied to realize higher computing speed, which could conduce to extract multiple domain features and run manifold real-time monitoring algorithms. The new system could also satisfy the small-sized requirement in engine flying state.

A Proposed Ultraprecision Machining Process Monitoring Method Using Causal Network Model of Air Spindle System

2011 ◽  
Vol 5 (3) ◽  
pp. 362-368 ◽  
Author(s):  
Hiroshi Sawano ◽  
◽  
Ryosuke Kobayashi ◽  
Hayato Yoshioka ◽  
Hidenori Shinno
Keyword(s):  
Network Model ◽  
Process Monitoring ◽  
Machining Process ◽  
Causal Network ◽  
Ultraprecision Machining ◽  
Monitoring Method ◽  
Spindle System ◽  
Control Functions ◽  
Air Spindle ◽  
Cutting Point

Future ultraprecision machining systems require inprocess monitoring and intelligent machining control functions. This paper presents a newly developed machining process monitoring method. The method proposed aims at monitoring the ultraprecision machining process using a causal network model of an air spindle system. The results of actual machining experiments confirm that the proposed method can estimate the dynamic and thermal behaviors at the cutting point during machining. In consequence, the process monitoring method proposed can systematically predict the tool wear, the contact condition between the tool and the workpiece, the abnormal machining conditions, and so on.

The Design of CNC Lathe Cutting Force Signal On-Line Monitoring

2012 ◽  
Vol 155-156 ◽  
pp. 526-530
Author(s):  
Jian Bo Zhao
Keyword(s):  
Signal Processing ◽  
Cutting Force ◽  
Real Time ◽  
Monitoring System ◽  
Machining Process ◽  
Signal Acquisition ◽  
Real Time Monitoring ◽  
Cnc Lathe ◽  
Force Signal ◽  
On Line

The main task is to study the CNC lathe cutting force signal on-line monitoring system design, it mainly includes the cutting force signal acquisition, real-time monitoring and subsequent processing and analysis. Cutting force signal acquisition is the use of Kistler piezoelectric sensor signal, converted into voltage signal, and through the data acquisition board, to convert analog signals into digital signals, input to the computer, the computer real-time monitoring of machining process. Signal processing is to the acquisition of signal processing and analysis, to obtain the correct dynamic cutting force signal, to verify the stability and reliability of the whole monitoring system.

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