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.

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.

Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations

1993 ◽  
Vol 115 (3) ◽  
pp. 268-277 ◽  
Author(s):  
K. Ramamurthi ◽  
C. L. Hough
Keyword(s):  
Real Time ◽  
Metal Cutting ◽  
Failure Modes ◽  
Cutting Tools ◽  
Diagnostic System ◽  
Training Time ◽  
Process Diagnosis ◽  
Modes Of Failure ◽  
On Line ◽  
Cutting Processes

Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.

scholarly journals In-Process Monitoring of Lack of Fusion in Ultra-Thin Sheets Edge Welding Using Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2411 ◽  
Author(s):  
Yuxiang Hong ◽  
Baohua Chang ◽  
Guodong Peng ◽  
Zhang Yuan ◽  
Xiangchun Hou ◽  
...  
Keyword(s):  
Real Time ◽  
Weld Pool ◽  
Process Monitoring ◽  
Welding Process ◽  
Thin Sheets ◽  
Real Time Control ◽  
Image Processing Algorithm ◽  
Monitoring Method ◽  
Lack Of Fusion ◽  
Time Control

Lack of fusion can often occur during ultra-thin sheets edge welding process, severely destroying joint quality and leading to seal failure. This paper presents a vision-based weld pool monitoring method for detecting a lack of fusion during micro plasma arc welding (MPAW) of ultra-thin sheets edge welds. A passive micro-vision sensor is developed to acquire clear images of the mesoscale weld pool under MPAW conditions, continuously and stably. Then, an image processing algorithm has been proposed to extract the characteristics of weld pool geometry from the acquired images in real time. The relations between the presence of a lack of fusion in edge weld and dynamic changes in weld pool characteristic parameters are investigated. The experimental results indicate that the abrupt changes of extracted weld pool centroid position along the weld length are highly correlated with the occurrences of lack of fusion. By using such weld pool characteristic information, the lack of fusion in MPAW of ultra-thin sheets edge welds can be detected in real time. The proposed in-process monitoring method makes the early warning possible. It also can provide feedback for real-time control and can serve as a basis for intelligent defect identification.

scholarly journals Real-time monitoring of the reaction between aniline and acetonylacetone using extractive electorspray ionization tandem mass spectrometry

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xinglei Zhang ◽  
Miaorong Pei ◽  
Debo Wu ◽  
Shuiping Yang ◽  
Zhanggao Le
Keyword(s):  
Mass Spectrometry ◽  
Tandem Mass Spectrometry ◽  
Real Time ◽  
Sample Pretreatment ◽  
Direct Analysis ◽  
Molecular Ions ◽  
Tandem Mass ◽  
Monitoring Method ◽  
On Line ◽  
Ionization Tandem Mass Spectrometry

AbstractIn this work an on-line monitoring method was developed to study the mechanism of acetic acid catalyzed reaction between aniline and acetonylacetone using extractive electorspray ionization-tandem mass spectrometry (EESI-MS). The signals of reactants, intermediates and various byproducts were continuously detected as a function of reaction time. The chemical assignment of each signal was done via multi-stage collision induced dissociation (CID) analysis, and the reaction mechanism between aniline and acetonylacetone was deduced based on the generated molecular ions and fragment ions. The results indicate that on-line EESI-MS is an effective technique for the real time analysis of chemical reactions. EESI avoids off-line sample pretreatment and provides “soft” ionization, which allows direct analysis of various analytes at molecular level.

scholarly journals On-line and real time cell counting and viability determination for animal cell process monitoring by in situ microscopy

2011 ◽  
Vol 5 (S8) ◽  
Author(s):  
Philipp Wiedemann ◽  
Markus Worf ◽  
Hans B Wiegemann ◽  
Florian Egner ◽  
Christian Schwiebert ◽  
...  
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Animal Cell ◽  
Cell Counting ◽  
Cell Process ◽  
On Line ◽  
Viability Determination

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.

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.

scholarly journals Development Research on Integrating CNC Machine Tool with Plasma for On-line Surface Heat Treatment

2021 ◽  
Author(s):  
Shao Hsien Chen ◽  
Bo-Ting Wang
Keyword(s):  
Heat Treatment ◽  
Mechanical Strength ◽  
Real Time ◽  
Feed Rate ◽  
Surface Hardness ◽  
Experimental Results ◽  
Machining Process ◽  
Optimum Parameters ◽  
Hardening Effect ◽  
On Line

The mechanical engineering requires heat treatment after rough machining to reach the mechanical strength, but the heat treatment can induce workpiece deformation, so that the workpiece cannot be reworked. In this study, the plasma was integrated with a lathe, and the on line heat treatment was performed to achieve the mechanical strength and hardness, so as to reduce the machining process and handling. However, for on line heat treatment, it is important to study the machine and plasma parameters of the lathe and plasma, and the research method is used eventually to optimize the process, reduce the machining cost and machining error. The variable factors in surface on line real-time heat treatment are revolution, feed rate and current, the objective function is the hardness of mechanical properties. In the screening experiment, the interaction of factors was discussed using full factorial experiment. The Central Composite Design was combined with the Lack-of-Fit test for optimization experiment, the R2 coefficient was used to determine whether the regression model is appropriate. The optimum parameters were derived from the contour diagram and response surface diagram. The experimental results show that the significant factors include revolution, feed rate and current, the optimum parameters include revolution 168rpm, feed rate 0.068mm/rev and current 86A. The experimental results of optimum parameters show that the surface hardness is increased from 306HLD to 806HLD, the surface hardening effect is enhanced by 163%, so the on line real-time heat treatment equipment has a best hardening effect.

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