In-process tool wear prediction system based on machine learning techniques and force analysis

This paper presents an in-process tool wear prediction system, which uses a force sensor to monitor the progression of the tool flank wear and machine learning (ML), more specifically, a Convolutional Neural Network (CNN) as a method to predict tool wear. The proposed methodology is experimentally illustrated using milling as a test process. The experiments are conducted using dry machining with a non-coated ball endmill and a stainless steel workpiece. The measurement of the flank wear is carried on in-situ utilising a digital microscope. The ML model predictions are based on an experience database which contains all the data of the precedent experiments. The proposed in-process tool wear prediction system will be reinforced later by an adaptive control (AC) system that will communicate continuously with the ML model to seek the best adjustment of feed rate and spindle speed that allows the optimization of the flank wear and extend the tool life. The AC model decisions are based on the prediction delivered by the ML model and on the information feedback provided from the force sensor, which captures the change in the cutting forces as a function of the progression of the flank wear. In this work, only the ML model component for the estimation of tool wear based on CNNs is demonstrated. The proposed methodology has shown an estimated accuracy of 90%. Additional experiments will be performed to confirm the repetitiveness of the results and also extend the measurement range to improve accuracy of the measurement system

Displacements analysis of self-excited vibrations in turning

The actual research deals with determining by a new protocol the necessary parameters considering a three-dimensional model to simulate in a realistic way the turning process on machine tool. This paper is dedicated to the experimental displacements analysis of the block tool / block workpiece with self-excited vibrations. In connexion with turning process, the self-excited vibrations domain is obtained starting from spectra of two accelerometers. The existence of a displacements plane attached to the tool edge point is revealed. This plane proves to be inclined compared to the machines tool axes. We establish that the tool tip point describes an ellipse. This ellipse is very small and can be considered as a small straight line segment for the stable cutting process (without vibrations). In unstable mode (with vibrations) the ellipse of displacements is really more visible. A difference in phase occurs between the tool tip displacements on the radial direction and on the cutting one. The feed motion direction and the cutting one are almost in phase. The values of the long and small ellipse axes (and their ratio) shows that these sizes are increasing with the feed rate value. The axis that goes through the stiffness center and the tool tip represents the maximum stiffness direction. The maximum (resp. minimum) stiffness axis of the tool is perpendicular to the large (resp. small) ellipse displacements axis. FFT analysis of the accelerometers signals allows to reach several important parameters and establish coherent correlations between tool tip displacements and the static - elastic characteristics of the machine tool components tested

New method to characterize a machining system: application in turning

Many studies simulates the machining process by using a single degree of freedom spring-mass sytem to model the tool stiffness, or the workpiece stiffness, or the unit tool-workpiece stiffness in modelings 2D. Others impose the tool action, or use more or less complex modelings of the efforts applied by the tool taking account the tool geometry. Thus, all these models remain two-dimensional or sometimes partially three-dimensional. This paper aims at developing an experimental method allowing to determine accurately the real three-dimensional behaviour of a machining system (machine tool, cutting tool, tool-holder and associated system of force metrology six-component dynamometer). In the work-space model of machining, a new experimental procedure is implemented to determine the machining system elastic behaviour. An experimental study of machining system is presented. We propose a machining system static characterization. A decomposition in two distinct blocks of the system "Workpiece-Tool-Machine" is realized. The block Tool and the block Workpiece are studied and characterized separately by matrix stiffness and displacement (three translations and three rotations). The Castigliano's theory allows us to calculate the total stiffness matrix and the total displacement matrix. A stiffness center point and a plan of tool tip static displacement are presented in agreement with the turning machining dynamic model and especially during the self induced vibration. These results are necessary to have a good three-dimensional machining system dynamic characterization

Lady Gwendolen, David, Rosemary, Philip Game Junior and their dog on the deck of the ship Zealandia arriving in Sydney, 7 February 1933 [picture].

Title devised from accompanying information where available.; Part of the: Fairfax archive of glass plate negatives.; Fairfax number: 3625.; Governor of New South Wales Sir Philip Game and his family returning from a five week holiday in Hobart; Also available online at: http://nla.gov.au/nla.pic-vn6262045; Acquired from Fairfax Media, 2012

The new district and county atlas of Queensland, 1878 [cartographic material] : together with map of Queensland, indicating roads, distances, relative position of districts, &c., &c.

Maps of Queensland showing county and district boundaries. Relief shown by hachures.; Cover title: F. E. Hiscocks & Co.'s new district atlas of Queensland.; Includes list of stations and names of occupiers in Queensland 1878.; Also available online http://nla.gov.au/nla.map-raa49. [No. 1] City of Brisbane. Scale [ca. 1:9,504] -- No. 2 Map of the district of Moreton, Queensland. Scale [ca. 1:506,880] -- No. 2 Map of the Darling Downs district, Queensland. Scale [ca. 1:760,320] -- No. 4 Map of the Wide Bay & Burnett districts, Queensland. Scale [ca. 1:380,160] -- [No. 5] Map of the Port Curtis district, Queensland. Scale [ca. 1:760,320] -- No. 6 Map of the Leichhardt district, Queensland. Scale [ca. 1:1,140,480] -- No. 7 Map of the Maranoa district, Queensland. Scale [ca. 1:1,013,760] -- No. 8 Map of the Warrego district, Queensland. Scale [ca. 1:1,267,200] -- No. 9 Map of the Mitchell district, Queensland. Scale [ca. 1:1,140,480] -- No. 10 Map of the South Kennedy district, Queensland. Scale [ca. 1:1,013,760] -- No. 11 Map of the North Kennedy district, Queensland. Scale [ca. 1:1,140,480] -- [No. 12] Map of the Gregory south district, Queensland. Scale [ca. 1:1,267,200] -- No. 13 Map of the Gregory north district, Queensland. Scale [ca. 1:1,267,200] -- No. 14 Map of the Burke district, Queensland. Scale [ca. 1:1,670,000] -- No. 15 Map of the Cook district, Queensland. Scale [ca. 1:1,584,000] -- [No. 16] Map of the colony of Queensland, Australia. Scale [ca. 1:5,068,800].F. E. Hiscocks & Co.'s new district atlas of QueenslandAtlas of Queensland,187

Use of artificial neural networks to analyse tunnelling-induced ground movements obtained from geotechnical centrifuge testing

In geomechanics, centrifuge modelling and digital image analysis enable the acquisition of large amounts of high-quality data related to ground movements. In this paper, modern intelligent methods based on a feedforward artificial neural network (ANN) architecture are applied to study tunnelling-induced ground displacements. Soil displacement data obtained from a geotechnical centrifuge test are used to investigate the capabilities of ANNs in this context. Because this work represents a feasibility study, the centrifuge dataset is limited to a single test. The trial-and-error process is used to identify three architectures of varying complexity that achieve a good level of performance. Predictions are evaluated both statistically (R2) and qualitatively (analysing the shape of vertical and horizontal displacement profiles). Results show the applicability of modern intelligent analysis methods for analysing centrifuge datasets and highlight certain strengths and deficiencies of feedforward ANN architectures compared to empirical methods