Condition Monitoring Methodology for Manufacturing and Design

1998 ◽  
Author(s):  
Irem Y. Tumer ◽  
Kristin L. Wood ◽  
Ilene J. Busch-Vishniac
Keyword(s):  
Milling Process ◽  
Monitoring Methods ◽  
Design And Manufacturing ◽  
Systematic Methodology ◽  
Surface Deviations ◽  
Part Surface ◽  
Tool Vibrations ◽  
Changes Over Time ◽  
Part Production ◽  
Manufacturing Data

Abstract Part production requires constant monitoring to assure the effective manufacturing of high-quality components. The choice of monitoring methods can become a crucial factor in the decisions made during and prior to manufacturing. In an ideal world, designers and manufacturers will work together to interpret manufacturing and part data to assure the elimination of faults in manufacturing. However, manufacturing still lacks mathematically robust means of interpreting the manufacturing data so that a channel of communication can be established between design and manufacturing. To address part production concerns, we present a systematic methodology to interpret manufacturing data based on signals from manufacturing (e.g., tool vibrations, part surface deviations). These signals are assumed to contain a fingerprint of the manufacturing condition. The method presented in this paper is based on a mathematical transform to decompose the signals into their significant modes and monitor their changes over time. The methodology is meant to help designers and manufacturers make informed decisions about a machine and/or part condition. An example from a milling process is used to illustrate the method’s details.

Dynamic cutting force prediction for micro end milling considering tool vibrations and run-out

2018 ◽  
Vol 233 (7) ◽  
pp. 2248-2261 ◽  
Author(s):  
Xuewei Zhang ◽  
Tianbiao Yu ◽  
Wanshan Wang
Keyword(s):  
Cutting Force ◽  
Cutting Forces ◽  
End Milling ◽  
Chip Thickness ◽  
Milling Process ◽  
Force Model ◽  
Dynamic Cutting Force ◽  
Micro End Milling ◽  
Tool Vibrations ◽  
End Milling Process

An accurate prediction of cutting forces in the micro end milling, which is affected by many factors, is the basis for increasing the machining productivity and selecting optimal cutting parameters. This paper develops a dynamic cutting force model in the micro end milling taking into account tool vibrations and run-out. The influence of tool run-out is integrated with the trochoidal trajectory of tooth and the size effect of cutting edge radius into the static undeformed chip thickness. Meanwhile, the real-time tool vibrations are obtained from differential motion equations with the measured modal parameters, in which the process damping effect is superposed as feedback on the undeformed chip thickness. The proposed dynamic cutting force model has been experimentally validated in the micro end milling process of the Al6061 workpiece. The tool run-out parameters and cutting forces coefficients can be identified on the basis of the measured cutting forces. Compared with the traditional model without tool vibrations and run-out, the predicted and measured cutting forces in the micro end milling process show closer agreement when considering tool vibrations and run-out.

Research on Tool Wear Form in Micro Turn-Milling Process

2012 ◽  
Vol 184-185 ◽  
pp. 663-667 ◽  
Author(s):  
Lin Hui Zhao ◽  
Jian Cheng Zhang ◽  
Wei Su
Keyword(s):  
Tool Wear ◽  
Small Diameter ◽  
Milling Process ◽  
Milling Tool ◽  
Key Factor ◽  
Part Surface ◽  
End Mills ◽  
Series Of Experiments ◽  
Turn Milling ◽  
Wear Condition

In micro machining, turn-milling tool wear is a key factor for part surface quality. This paper carries on experiments on end mills wear in micro turn-milling machining, aiming to research the wear form and provide some reference data for developing wear standard of small diameter end mills. To measure wear condition of end mills, machine vision technique is utilized. This paper designs and sets up an online end mill wear measurement system for a micro turn-milling process center. With a series of experiments on small diameter end mills, wear conditions of different cutting positions are researched. Based on analysis of experiment data, wear characteristics and wear rule for micro turn-milling process are summarized in this paper.

scholarly journals Does the use of playback affect the estimated numbers of red–legged partridge Alectoris rufa?

2012 ◽  
Vol 35 (2) ◽  
pp. 429-435
Author(s):  
P. Tizzani ◽  
◽  
E. Negri ◽  
F. Silvano ◽  
G. Malacarne ◽  
...  
Keyword(s):  
Critical Evaluation ◽  
Alectoris Rufa ◽  
Size Estimation ◽  
Monitoring Methods ◽  
Direct Observations ◽  
Underestimation Rate ◽  
Conservation Threats ◽  
Changes Over Time ◽  
Monitoring Area

The red–legged partridge Alectoris rufa lives in a situation of potential conservation risk for its long–term preservation in Italy as its habitat is increasingly threatened by the disappearance of traditional agriculture–related environments. In such a situation, it is important to use effective and appropriate monitoring methods to assess population changes over time and to identify potential conservation threats. The objective of this study was to evaluate the effectiveness of the playback method to estimate the density of calling males. We compared playback method with spontaneous calling of males at dawn and direct observations along transects. The results on raw count data of playback counts revealed a strong underestimation rate compared to the method that gave the best results: count of spontaneous calls at dawn. Our study provides a critical evaluation of a method that is widely used even though data about its effectiveness are scarce. Our data do not evaluate detection probability of the three methods. Our aim was only to evaluate which methods give the best results in term of population size estimation under the same field condition (same population density, same period, same monitoring area). The results raise some doubts about the ability of the playback method to monitor red–legged partridge populations. The implications of our results for red–legged population management are discussed.

Study on High-Grade CNC Machine Tool Wear Monitoring Methods and Experimental System

2012 ◽  
Vol 591-593 ◽  
pp. 1844-1848
Author(s):  
Min Huang ◽  
Xiu Li Liu ◽  
Le Yan
Keyword(s):  
Fault Diagnosis ◽  
Tool Wear ◽  
Machine Tools ◽  
High Speed ◽  
Experimental System ◽  
Milling Process ◽  
Test Method ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Monitoring Methods

Today, CNC machine tools are moving in the high-speed, high precision, heavy and complex processing of direction, leading to early failure in service due to machine performance, if not timely diagnosis and early warning, will result in waste increases, fluctuations in the quality, productivity decline.Therefore, to ensure reliable operation of CNC machine tools is very important.To build fault diagnosis of CNC machine tools and get test method as the goal, the tool wear experiments are carried out. Signals for cutting tool with different wear in milling process are detected，acquisited and analyzed through vibration sensors and acoustic emission sensors on the milling tools. To LabVIEW8.6 as development platform, a fault diagnosis experimental system of CNC machine tools is developed, including data acquisition module, signal analysis module, fault diagnosis module.

A Systematic Methodology for Comparing Batch Process Monitoring Methods: Part II—Assessing Detection Speed

2018 ◽  
Vol 57 (15) ◽  
pp. 5338-5350 ◽  
Author(s):  
Tiago J. Rato ◽  
Ricardo Rendall ◽  
Veronique Gomes ◽  
Pedro M. Saraiva ◽  
Marco S. Reis
Keyword(s):  
Process Monitoring ◽  
Batch Process ◽  
Monitoring Methods ◽  
Batch Process Monitoring ◽  
Systematic Methodology ◽  
Detection Speed

Influence of the Temperature and Mechanical Stresses Generated by Milling Process in Machined Part Surfaces on their Accuracy

2013 ◽  
Vol 371 ◽  
pp. 59-63 ◽  
Author(s):  
Nicolae Catalin Tampu ◽  
Maria Crina Radu ◽  
Bogdan Chirita
Keyword(s):  
Residual Stress ◽  
Plastic Strain ◽  
Surface Quality ◽  
Cutting Parameters ◽  
Milling Process ◽  
Mechanical Stresses ◽  
Surface Generation ◽  
Work Piece ◽  
Stress Distributions ◽  
Surface Deviations

Surface generation by milling process involves the occurrence of some complex effects that affect surface quality. The most important effects of the milling on the generated surface are heating, plastic strain and residual stress. The aim of the present research is to analyze the influence of the work piece temperatures and residual stress distributions generated by milling in the machined surfaces on its accuracy. Two types of materials and different cutting parameters have been used in order to determine the above mentioned connection. The obtained results show that the temperature and residual stress generated by milling influences the surface deviations from flatness.

scholarly journals Monitoring methods and spatial patterns of European canker disease in commercial orchards

2016 ◽  
Vol 69 ◽  
pp. 213-220 ◽  
Author(s):  
R.E. Campbell ◽  
S. Roy ◽  
T. Curnow ◽  
M. Walter
Keyword(s):  
Spatial Pattern ◽  
Disease Control ◽  
Spatial Patterns ◽  
Disease Incidence ◽  
Systematic Sampling ◽  
Monitoring Methods ◽  
Canker Disease ◽  
Single Row ◽  
Changes Over Time ◽  
Over Time

European canker (Neonectria ditissima) kills trees and decreases production in apple orchards To determine a level of disease control or the extent of its spread in commercial orchards efficient monitoring methods are required In this study we investigated two monitoring methods sampling a single row and systematic sampling of an orchard block The spatial pattern of disease within blocks and whether this changes over time was also investigated The accuracy of singlerow monitoring depended on the level of canker in the orchard and the patchiness of the distribution of infected trees However singlerow monitoring tracked changes over time in incidence severity and type of canker sufficiently well and was efficient The spatial patterns of disease incidence across the blocks were nonrandom but showed hotspots which did not change significantly over time

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