Volumetric Error-Based Condition and Health Monitoring System for Machine-Tools

Résumé Des défaillances ou détériorations imprévues ou non détectées des machines-outils entraînent des pertes de production et de qualité, d'où la nécessité d'une maintenance prescriptive et normative utilisant la surveillance de l'état des machines-outils. Cette recherche présente la méthodologie et les solutions développées pour surveiller l’état de précision des machines-outils à cinq axes en analysant les erreurs volumétriques de la machine-outil. L’erreur volumétrique est définie comme un vecteur d'erreur cartésien représentant l'écart de la position réelle de l'outil par rapport à sa position attendue par rapport au repère de la pièce et projeté dans le repère de base. La méthode SAMBA (Scale and Master Ball Artefact) a été utilisée pour mesurer les erreurs volumétriques de la machine-outil expérimentale à cinq axes. Les erreurs volumétriques acquises contenant les états normaux et défectueux de la machine-outil constituent la base de données pour cette recherche. De plus, des pseudo-fautes et les fautes graduelles et soudaines simulées ont également été utilisées. Les caractéristiques du vecteur d'erreurs volumétriques extraites par des mesures de similarité de vecteur sont utilisées comme entrée pour le graphique de contrôle basé sur les moyennes mobiles pondérées exponentiellement, où le changement anormal du vecteur unique d'erreurs volumétriques peut être détecté. Pour surveiller de manière exhaustive l’état de précision de la machine-outil, une matrice de mesures de similarité vectorielle combinée contenant toutes les caractéristiques d’erreurs volumétriques acquises a été proposée et traitée par le graphique de contrôle de la moyenne mobile pondérée exponentiellement. Pour les mêmes défauts, les deux traitements de données ci-dessus peuvent tous détecter automatiquement le temps exact d’apparition du défaut. Sur la base d'une logique de surveillance complète des erreurs volumétriques, une analyse fractale des coordonnées d'erreur volumétrique a également été explorée. Les résultats des tests révèlent qu’il s’agit d’un outil efficace pour représenter la fonctionnalité des erreurs volumétriques. Pour comprendre le processus de changement de l'état de la machine-outil, les erreurs volumétriques historiques acquises ont été traitées par analyse en composantes principales et par K-moyennes. D'une part, les méthodes proposées séparent les états normaux et défectueux de la machine-outil (près de 100%), d'autre part, les machines-outils désignées fournissent les références pour la reconnaissance de l'état d’autre machines-outils lors du traitement de nouvelles données d'erreurs volumétriques. En résumé, le travail de recherche effectué dans cette thèse a contribué à la mise au point d’une solution efficace de surveillance de l’état de la précision des machines-outils à l’aide des erreurs volumétriques des machines-outils, basées sur des méthodes d’extraction de caractéristiques, de reconnaissance des modifications et de classification des états. Le système développé peut reconnaître les points de changement exacts des défauts réels du codeur d'axe C, des pseudo-défauts EXX et EYX. De plus, il atteint une précision proche de 100% dans la classification de l'état défectueux et normal de la machine-outil. ---------- Abstract Unexpected or undetected machine tool failures or deterioration results in production and quality losses, hence proactive and prescriptive maintenance using machine tool condition monitoring is sought. This research presents the methodology and solutions developed to monitor the accuracy state of five-axis machine tools by analyzing the machine tool volumetric errors which are defined as the Cartesian error vector of the deviation of the actual tool position compared to its expected position relative to the workpiece frame and projected into the foundation frame. The scale and master ball artefact (SAMBA) method has been used for the measurement of volumetric errors of the experimental five-axis machine tool. The acquired volumetric errors containing machine tool normal and faulty states provide the database for this research. In addition, pseudo-faults and the simulated gradual and sudden faults have also been used. Volumetric error vector features extracted by vector similarity measures are used as the input for the exponential weight moving average control chart where the abnormal change of the single volumetric error vector can be detected. To comprehensively monitor the machine tool accuracy state, a combined vector similarity measure array containing all acquired volumetric errors features has been proposed and processed by the exponential weight moving average control chart. Towards the same faults, the above two data processing can all automatically detect the exact fault occurrence time. Based on the logic of comprehensive monitoring of volumetric errors, fractal analysis of volumetric error coordinates has also been explored. The testing results reveal that it is an effective tool for volumetric errors features representing. To understand the change process of the machine tool state, the acquired historical volumetric errors have been processed by principal component analysis and K-means. For one thing, the proposed methods separate the normal and faulty states of the machine tool (Nearly 100%), for another thing, the designated machine tools provide the references for machine tools state recognition when processing new volumetric errors data. In summary, this research contributed to the development of an efficient solution for machine tool accuracy state monitoring using machine tools volumetric errors based on feature extraction, change recognition and state classification methods. The developed system can recognize the exact change points of real C-axis encoder faults, pseudo-faults EXX and EYX. In addition, it achieves close to 100% accuracy in machine tool faulty and normal state classification

Machine tool volumetric error features extraction and classification using principal component analysis and K-means

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification

Comparison of direct and indirect methods for five-axis machine tools geometric error measurement

ABSTRACT: Geometrical error (GE) is a key criterion for machine tools performance evaluation. GE measurement methods can be classified as direct and indirect methods. As an indirect GE measurement method, the scale and master ball artefact (SAMBA) method can estimate the GE of linear and rotary axes by probing series of master balls and a scale bar artefact installed on the machine tool pallet. The purpose of this study is to research the performance of direct and indirect GE measurement methods (i.e. laser interferometer and SAMBA method) in linear positioning error measurement such as EXX and EYY. These errors of the X- and Y-axis are separately measured on a five-axis machine tool with the two methods. The results reveal that the SAMBA method yields similar results as the laser test in error shape and range. However, there are some minor differences which are discussed

Continued spread of HIV among injecting drug users in southern Sichuan Province, China

OBJECTIVE: To estimate HIV prevalence among injecting drug users (IDUs) in a drug trafficking city in southwest Sichuan Province, China. METHODS: A total of 314 IDUs was invited to participate in the cross-sectional survey in 2004 through community outreach recruitment and peer referrals. Blood sample was taken for HIV antibody testing and a structured questionnaire was administered to collect information on socio-demographics, drug using and sexual behaviors. RESULTS: HIV prevalence among IDUs was 17.8% (56/314), about one half higher than that in previous survey in 2002 (11.3%, 43/379). Yi and other minority ethnicity (Odds ratio [OR], 3.1; 95% confidence interval [CI], 1.7–5.8; P < 0.001), and total times of sharing injecting equipments 1–9 times versus none, OR, 2.7; 95% CI 1.2–6.2; P = 0.02; and ≥10 times versus none, OR, 7.5; 95% CI, 3.2–17.7; P < 0.001) were independent risk factors for HIV infection. CONCLUSION: IDUs with high prevalence rates of HIV and equipment sharing behavior in the drug trafficking city may serve a source for further spread of HIV to other areas in China. The increasing trend of HIV epidemic among IDUs underscores the urgency of scaling up interventions

Machine Tool Volumetric Error Features Extraction and Classification Using Principal Component Analysis and K-Means

Volumetric errors (VE) are related to the machine tool accuracy state. Extracting features from the complex VE data provides with a means to characterize this data. VE feature classification can reveal the machine tool accuracy states. This paper presents a study on how to use principal component analysis (PCA) to extract the features of VE and how to use the K-means method for machine tool accuracy state classification. The proposed data processing methods have been tested with the VE data acquired from a five-axis machine tool with different states of malfunction. The results indicate that the PCA and K-means are capable of extracting the VE feature information and classifying the fault states including the C axis encoder fault, uncalibrated C axis encoder fault, and pallet location fault from the machine tool normal states. This research provides a new way for VE features extraction and classification

Five-Axis Machine Tool Coordinate Metrology Evaluation Using the Ball Dome Artefact Before and After Machine Calibration

Now equipped with touch trigger probes machine tools are increasingly used to measure workpieces for various tasks such as rapid setup, compensation of final tool paths to correct part deflections and even verify conformity to finished tolerances. On five-axis machine tools, the use of data acquired for different rotary axes positions angles brings additional errors into play, thus increasing the measurement errors. The estimation of the machine geometric error sources, using such methods as the scale and master ball artefact (SAMBA) method, and their use to calibrate machine tools may enhance five-axis on-machine metrology. The paper presents the use of the ball dome artefact to validate the accuracy improvement when using a calibrated model to process the machine tool axis readings. The inter-axis errors and the scale gain errors were targeted for correction as well the measuring tool length and lateral offsets. Worst case and mean deviations between the reference artefact geometry and the on-machine tool measurement is reduced from 176 and 70 &#181;m down to 31 and 12 &#181;m for the nominal and calibrated machine stylus tip offsets respectively