63,243 research outputs found
Online Tool Condition Monitoring Based on Parsimonious Ensemble+
Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common methodologies for tool condition monitoring still rely on
batch approaches which cannot cope with a fast sampling rate of metal cutting
process. Furthermore they require a retraining process to be completed from
scratch when dealing with a new set of machining parameters. This paper
presents an online tool condition monitoring approach based on Parsimonious
Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly
flexible principle where both ensemble structure and base-classifier structure
can automatically grow and shrink on the fly based on the characteristics of
data streams. Moreover, the online feature selection scenario is integrated to
actively sample relevant input attributes. The paper presents advancement of a
newly developed ensemble learning algorithm, pENsemble+, where online active
learning scenario is incorporated to reduce operator labelling effort. The
ensemble merging scenario is proposed which allows reduction of ensemble
complexity while retaining its diversity. Experimental studies utilising
real-world manufacturing data streams and comparisons with well known
algorithms were carried out. Furthermore, the efficacy of pENsemble was
examined using benchmark concept drift data streams. It has been found that
pENsemble+ incurs low structural complexity and results in a significant
reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic
Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis
This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version
Reusable rocket engine turbopump health monitoring system, part 3
Degradation mechanisms and sensor identification/selection resulted in a list of degradation modes and a list of sensors that are utilized in the diagnosis of these degradation modes. The sensor list is divided into primary and secondary indicators of the corresponding degradation modes. The signal conditioning requirements are discussed, describing the methods of producing the Space Shuttle Main Engine (SSME) post-hot-fire test data to be utilized by the Health Monitoring System. Development of the diagnostic logic and algorithms is also presented. The knowledge engineering approach, as utilized, includes the knowledge acquisition effort, characterization of the expert's problem solving strategy, conceptually defining the form of the applicable knowledge base, and rule base, and identifying an appropriate inferencing mechanism for the problem domain. The resulting logic flow graphs detail the diagnosis/prognosis procedure as followed by the experts. The nature and content of required support data and databases is also presented. The distinction between deep and shallow types of knowledge is identified. Computer coding of the Health Monitoring System is shown to follow the logical inferencing of the logic flow graphs/algorithms
Eco-efficient process based on conventional machining as an alternative technology to chemical milling of aeronautical metal skin panels
El fresado quĂmico es un proceso diseñado para la reducciĂłn de peso de pieles metálicas que, a
pesar de los problemas medioambientales asociados, se utiliza en la industria aeronáutica desde los
años 50. Entre sus ventajas figuran el cumplimiento de las estrictas tolerancias de diseño de piezas
aeroespaciales y que pese a ser un proceso de mecanizado, no induce tensiones residuales. Sin
embargo, el fresado quĂmico es una tecnologĂa contaminante y costosa que tiende a ser sustituida.
Gracias a los avances realizados en el mecanizado, la tecnologĂa de fresado convencional permite
alcanzar las tolerancias requeridas siempre y cuando se consigan evitar las vibraciones y la flexiĂłn
de la pieza, ambas relacionadas con los parámetros del proceso y con los sistemas de utillaje
empleados.
Esta tesis analiza las causas de la inestabilidad del corte y la deformación de las piezas a través
de una revisiĂłn bibliográfica que cubre los modelos analĂticos, las tĂ©cnicas computacionales y las
soluciones industriales en estudio actualmente. En ella, se aprecia cĂłmo los modelos analĂticos y las
soluciones computacionales y de simulaciĂłn se centran principalmente en la predicciĂłn off-line de
vibraciones y de posibles flexiones de la pieza. Sin embargo, un enfoque más industrial ha llevado al
diseño de sistemas de fijación, utillajes, amortiguadores basados en actuadores, sistemas de rigidez
y controles adaptativos apoyados en simulaciones o en la selecciĂłn estadĂstica de parámetros.
Además se han desarrollado distintas soluciones CAM basadas en la aplicación de gemelos virtuales.
En la revisión bibliográfica se han encontrado pocos documentos relativos a pieles y suelos
delgados por lo que se ha estudiado experimentalmente el efecto de los parámetros de corte en su
mecanizado. Este conjunto de experimentos ha demostrado que, pese a usar un sistema que
aseguraba la rigidez de la pieza, las pieles se comportaban de forma diferente a un sĂłlido rĂgido en
términos de fuerzas de mecanizado cuando se utilizaban velocidades de corte cercanas a la alta
velocidad. También se ha verificado que todas las muestras mecanizadas entraban dentro de
tolerancia en cuanto a la rugosidad de la pieza. Paralelamente, se ha comprobado que la correcta
selección de parámetros de mecanizado puede reducir las fuerzas de corte y las tolerancias del
proceso hasta un 20% y un 40%, respectivamente. Estos datos pueden tener aplicaciĂłn industrial en
la simplificaciĂłn de los sistemas de amarre o en el incremento de la eficiencia del proceso.
Este proceso también puede mejorarse incrementando la vida de la herramienta al utilizar
fluidos de corte. Una correcta lubricaciĂłn puede reducir la temperatura del proceso y las tensiones
residuales inducidas a la pieza. Con este objetivo, se han desarrollado diferentes lubricantes, basados
en el uso de lĂquidos iĂłnicos (IL) y se han comparado con el comportamiento tribolĂłgico del par de
contacto en seco y con una taladrina comercial. Los resultados obtenidos utilizando 1 wt% de los
lĂquidos iĂłnicos en un tribĂłmetro tipo pin-on-disk demuestran que el IL no halogenado reduce
significativamente el desgaste y la fricciĂłn entre el aluminio, material a mecanizar, y el carburo de
tungsteno, material de la herramienta, eliminando casi toda la adhesiĂłn del aluminio sobre el pin, lo
que puede incrementar considerablemente la vida de la herramienta.Chemical milling is a process designed to reduce the weight of metals skin panels. This process
has been used since 1950s in the aerospace industry despite its environmental concern. Among its
advantages, chemical milling does not induce residual stress and parts meet the required tolerances.
However, this process is a pollutant and costly technology. Thanks to the last advances in
conventional milling, machining processes can achieve similar quality results meanwhile vibration
and part deflection are avoided. Both problems are usually related to the cutting parameters and the
workholding.
This thesis analyses the causes of the cutting instability and part deformation through a literature
review that covers analytical models, computational techniques and industrial solutions. Analytics
and computational solutions are mainly focused on chatter and deflection prediction and industrial
approaches are focused on the design of workholdings, fixtures, damping actuators, stiffening
devices, adaptive control systems based on simulations and the statistical parameters selection, and
CAM solutions combined with the use of virtual twins applications.
In this literature review, few research works about thin-plates and thin-floors is found so the
effect of the cutting parameters is also studied experimentally. These experiments confirm that even
using rigid workholdings, the behavior of the part is different to a rigid body at high speed machining.
On the one hand, roughness values meet the required tolerances under every set of the tested
parameters. On the other hand, a proper parameter selection reduces the cutting forces and process
tolerances by up to 20% and 40%, respectively. This fact can be industrially used to simplify
workholding and increase the machine efficiency.
Another way to improve the process efficiency is to increase tool life by using cutting fluids.
Their use can also decrease the temperature of the process and the induced stresses. For this purpose,
different water-based lubricants containing three types of Ionic Liquids (IL) are compared to dry and
commercial cutting fluid conditions by studying their tribological behavior. Pin on disk tests prove
that just 1wt% of one of the halogen-free ILs significantly reduces wear and friction between both
materials, aluminum and tungsten carbide. In fact, no wear scar is noticed on the ball when one of
the ILs is used, which, therefore, could considerably increase tool life
Recommended from our members
The effect of tool fixturing quality on the design of condition monitoring systems for detecting tool conditions
Condition monitoring systems of machining processes are essential technology for improving productivity and automation. Tool wear monitoring of cutting tools is one of the important applications in this area. In this paper, the effect of collet fixturing quality on the design of condition monitoring systems to detect tool wear is discussed. The paper investigates the difference in the system's behaviour and the changes in the condition monitoring system when the cutting tool is not rigidly fastened to the collet. A group of sensors, namely acoustic emission, force, strain, vibration and sound, are used to design the condition monitoring system. Automated Sensor and Signal Processing Selection (ASPS) approach1 is implemented to address the effect of the tool holding device (collet) on the monitoring system and the most sensitive sensors and signal processing method to detect tool wear. The results prove that the change in the fixturing quality could have significant effect on the design of the condition monitoring system and the behaviour of the system
Maintenance Strategies to Reduce Downtime Due to Machine Positional Errors
Manufacturing strives to reduce waste and increase
Overall Equipment Effectiveness (OEE). When managing machine tool maintenance a manufacturer must apply an appropriate decision technique in order to reveal hidden costs associated with production losses, reduce equipment downtime
competently and similarly identify the machines’ performance.
Total productive maintenance (TPM) is a maintenance program that involves concepts for maintaining plant and equipment effectively. OEE is a powerful metric of manufacturing performance incorporating measures of the utilisation, yield and efficiency of a given process, machine or manufacturing line. It supports TPM initiatives by accurately tracking progress towards achieving “perfect production.”
This paper presents a review of maintenance management methodologies and their application to positional error calibration decision-making. The purpose of this review is to evaluate the contribution of maintenance strategies, in particular TPM, towards improving manufacturing performance, and how they could be applied to reduce downtime due to inaccuracy of the machine. This is to find a balance between predictive
calibration, on-machine checking and lost production due to inaccuracy.
This work redefines the role of maintenance management techniques and develops a framework to support the process of implementing a predictive calibration program as a prime method to supporting the change of philosophy for machine tool calibration decision making.
Keywords—maintenance strategies, down time, OEE, TPM, decision making, predictive calibration
In-flight friction and wear mechanism
A unique mechanism developed for conducting friction and wear experiments in orbit is described. The device is capable of testing twelve material samples simultaneously. Parameters considered critical include: power, weight, volume, mounting, cleanliness, and thermal designs. The device performed flawlessly in orbit over an eighteen month period and demonstrated the usefulness of this design for future unmanned spacecraft or shuttle applications
Artificial neural networks for controlling the temperature of internally cooled turning tools
Copyright @ 2013 Scientific Research PublishingBy eliminating the need for externally applied coolant, internally cooled turning tools offer potential health, safety and cost benefits in many types of machining operation. As coolant flow is completely controlled, tool temperature mea- surement becomes a practical proposition and can be used to find and maintain the optimum machining conditions. This also requires an intelligent control system in the sense that it must be adaptable to different tool designs, work piece materials and machining conditions. In this paper, artificial neural networks (ANN) are assessed for their suitability to perform such a control function. Experimental data for both conventional tools used for dry machining and internally cooled tools is obtained and used to optimise the design of an ANN. A key finding is that both experimental scatter characteristic of turning and the range of machining conditions for which ANN control is required have a large effect on the optimum ANN design and the amount of data needed for its training. In this investigation, predictions of tool tem- perature with an optimised ANN were found to be within 5°C of measured values for operating temperatures of up to 258°C. It is therefore concluded that ANN’s are a viable option for in-process control of turning processes using inter- nally controlled tools.This study is funded by the European Commission
Condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques
Paper examining condition monitoring wind turbine gearboxes using on-line/in-line oil analysis techniques
Detailed state of the art review for the different on-line/in-line oil analysis techniques in context of wind turbine gearboxes
The main driver behind developing advanced condition monitoring (CM) systems for the wind energy industry is the delivery of improved asset management regarding the operation and maintenance of the gearbox and other wind turbine components and systems. Current gearbox CM systems mainly detect faults by identifying ferrous materials, water, and air within oil by changes in certain properties such as electrical fields. In order to detect oil degradation and identify particles, more advanced devices are required to allow a better maintenance regime to be established. Current technologies available specifically for this purpose include Fourier transform infrared (FTIR) spectroscopy and ferrography. There are also several technologies that have not yet been or have been recently applied to CM problems. After reviewing the current state of the art, it is recommended that a combination of sensors would be used that analyze different characteristics of the oil. The information individually would not be highly accurate but combined it is fully expected that greater accuracy can be obtained. The technologies that are suitable in terms of cost, size, accuracy, and development are online ferrography, selective fluorescence spectroscopy, scattering measurements, FTIR, photoacoustic spectroscopy, and solid state viscometers
- …