51,446 research outputs found
Model-based observer proposal for surface roughness monitoring
ComunicaciĂłn presentada a MESIC 2019 8th Manufacturing Engineering Society International Conference (Madrid, 19-21 de Junio de 2019)In the literature, many different machining monitoring systems for surface roughness and tool condition have been proposed and validated experimentally. However, these approaches commonly require costly equipment and experimentation. In this paper, we propose an alternative monitoring system for surface roughness based on a model-based observer considering simple relationships between tool wear, power consumption and surface roughness. The system estimates the surface roughness according to simple models and updates the estimation fusing the information from quality inspection and power consumption. This monitoring strategy is aligned with the industry 4.0 practices and promotes the fusion of data at different shop-floor levels
A multi-sensor based online tool condition monitoring system for milling process
Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications
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Measuring the features sensitivity of fusion sensor using neural network in milling operation
Identification of influent factors on surface integrity in nickel-base superalloy drilling
For the critical rotating components in aeronautical industry, the metallurgical quality achieved after machining conditions could determine their mechanical behaviour in fatigue. To guarantee this quality, the tools, materials and cutting conditions are frozen during the validation process by a cutup part following by an acceptable surface integrity. Even with the fixed parameters, perturbations can occur during the process and may have a direct impact over the metallurgical quality through the apparition of anomalies, which could reduce the calculated fatigue life. The aim of this study is to define
a Process Monitoring technique able to detect the thickness affected by the machining taking into account
the flank wear effect
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
Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection
Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing an essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or workpiece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still ongoing challenge. In this paper, force signals are used for monitoring tool-wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results found prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear
Analytical and comparative study of using a CNC machine spindle motor power and infrared technology for the design of a cutting tool condition monitoring system
This paper outlines a comparative study to compare between using the power of the spindle and the infrared images of the cutting tool to design a condition monitoring system. This paper compares the two technologies for the development of a tool condition monitoring for milling processes. Wavelet analysis is used to process the power signal. Image gradient and Wavelet analyses are used to process the infrared images. The results show that the image gradient and wavelet analysis are powerful image processing techniques in detecting tool wear. The power of the motor of the spindle has shown less sensitivity to tool conditions in this case when compared to infrared thermography
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
Tool wear monitoring in turning using fused data sets of calibrated acoustic emission and vibration
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The main aim of this research is to develop an on-line tool wear condition monitoring intelligent system for single-point turning operations. This is to provide accurate and reliable information on the different states of tool wear. Calibrated acoustic emission and vibration techniques were implemented to monitor the progress of wear on carbide tool tips. Previous research has shown that acoustic emission (AE) is sensitive to tool wear. However, AE, as a monitoring technique, is still not widely adopted by industry. This is because it is as yet impossible to achieve repeatable measurements of AE. The variability is due to inconsistent coupling of the sensor with structures and the fact that the tool structure may have different geometry and material property. Calibration is therefore required so that the extent of variability becomes quantifiable, and hence accounted for or removed altogether. Proper calibration needs a well-defined and repeatable AE source. In this research, various artificial sources were reviewed in order to assess their suitability as an AE calibration source for the single-point machining process. Two artificial sources were selected for studying in detail. These are an air jet and a pulsed laser; the former produces continuous-type AE and the latter burst type AE. Since the air jet source has a power spectrum resembling closely the AE produced from single-point machining and since it is readily available in a machine shop, not to mention its relative safety compared to laser, an air-jet source is a more appealing choice. The calibration procedure involves setting up an air jet at a fixed stand-off distance from the top rake of the tool tip, applying in sequence a set of increasing pressures and measuring the corresponding AE. It was found that the root-mean-square value of the AE obtained is linearly proportional to the pressure applied. Thus, irrespective of the layout of the sensor and AE source in a tool structure, AE can be expressed in terms of the common currency of 'pressure' using the calibration curve produced for that particular layout. Tool wear stages can then be defined in terms of the 'pressure' levels. In order to improve the robustness of the monitoring system, in addition to AE, vibration information is also used. In this case, the acceleration at the tool tip in the tangential and feed directions is measured. The coherence function between these two signals is then computed. The coherence is a function of the vibration frequency and has a value ranging from 0 to 1, corresponding to no correlation and full correlation respectively between the two acceleration signals. The coherence function method is an attempt to provide a solution, which is relatively insensitive to the dynamics and the process variables except tool wear. Three features were identified to be sensitive to tool wear and they are; AErms, and the coherence function of the acceleration at natural frequency (2.5-5.5 kHz) of the tool holder and at high frequency end (18-25kHz) respectively. A belief network, based on Bayes' rule, was created providing fusion of data from AE and vibration for tool wear classification. The conditional probabilities required for the belief network to operate were established from examples. These examples were presented to the belief network as a file of cases. The file contains the three features mentioned earlier, together with cutting conditions and the tool wear states. Half of the data in this file was used for training while the other half was used for testing the network. The performance of the network gave an overall classification error rate of 1.6 % with the WD acoustic emission sensor and an error rate of 4.9 % with the R30 acoustic emission sensor.Funding was obtained from The Royal Thai Government, the Petroleum Authority of Thailand and King Mongkut's University of Technology Thonburi (KMUTT
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