1,851 research outputs found
Stochastic Tool Wear Prediction for Sustainable Manufacturing
AbstractTo provide scientific support for decision-making in critical applications such as maintenance scheduling and inventory management, tool wear monitoring and service life prediction are of significance to achieving sustainable manufacturing. Past research typically assumed time-invariant machining settings in modeling wear progression, hence is limited in accurately tracking varying wear rates. This paper presents a stochastic joint-state-and-parameter model with machining setting as a parameter that affects the state evolution or tool wear propagation. The model is embedded in a particle filter for recursive wear state prediction. Effectiveness of this method is verified through experimental data measured on a CNC milling machine
Macro, mini, micro and nano (M(sup 3)N) technologies for the future
Microelectromechanical systems (MEMS), micro systems technologies (MST), and micromanufacturing are relatively recent phrases or acronyms that have become synonymous with the design, development, and manufacture of 'micro' devices and systems. Micromanufacturing encompasses MEMS or MST and, in addition, includes all of the processes involved in the production of micro things. Integration of mechanical and electrical components, including built-in computers, can be formed into systems which must be connected to the macroworld. Macro, mini, micro, and nano technologies are all a part of MEMS or micromanufacturing. At this point in the development of the technology, it is becoming apparent that mini systems, with micro components, could very well be the economic drivers of the technology for the foreseeable future. Initial research in the fabrication of microdevices using IC processing technology took place over thirty years ago. Anisotropic etching of silicon was used to produce piezoresistive diaphragms. Since the early 60's, there has been gradual progress in MEMS until the early 1980's when worldwide interest in the technology really started to develop. During this time high aspect ratio micromachining using x rays was started in Germany. In 1987 the concept of a 'silicon micromechanics foundry' was proposed. Since then the interest in the U.S., Germany, and Japan has increased to the point where hundreds of millions of dollars of research monies are being funneled into the technology (at least in Germany and Japan) and the technology has been classified as critical or as a technology or national importance by the U.S. government
WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
Convolutional neural network (CNN), with ability of feature learning and
nonlinear mapping, has demonstrated its effectiveness in prognostics and health
management (PHM). However, explanation on the physical meaning of a CNN
architecture has rarely been studied. In this paper, a novel wavelet driven
deep neural network termed as WaveletKernelNet (WKN) is presented, where a
continuous wavelet convolutional (CWConv) layer is designed to replace the
first convolutional layer of the standard CNN. This enables the first CWConv
layer to discover more meaningful filters. Furthermore, only the scale
parameter and translation parameter are directly learned from raw data at this
CWConv layer. This provides a very effective way to obtain a customized filter
bank, specifically tuned for extracting defect-related impact component
embedded in the vibration signal. In addition, three experimental verification
using data from laboratory environment are carried out to verify effectiveness
of the proposed method for mechanical fault diagnosis. The results show the
importance of the designed CWConv layer and the output of CWConv layer is
interpretable. Besides, it is found that WKN has fewer parameters, higher fault
classification accuracy and faster convergence speed than standard CNN
Towards a Digital Twin Framework in Additive Manufacturing: Machine Learning and Bayesian Optimization for Time Series Process Optimization
Laser-directed-energy deposition (DED) offers advantages in additive
manufacturing (AM) for creating intricate geometries and material grading. Yet,
challenges like material inconsistency and part variability remain, mainly due
to its layer-wise fabrication. A key issue is heat accumulation during DED,
which affects the material microstructure and properties. While closed-loop
control methods for heat management are common in DED research, few integrate
real-time monitoring, physics-based modeling, and control in a unified
framework. Our work presents a digital twin (DT) framework for real-time
predictive control of DED process parameters to meet specific design
objectives. We develop a surrogate model using Long Short-Term Memory
(LSTM)-based machine learning with Bayesian Inference to predict temperatures
in DED parts. This model predicts future temperature states in real time. We
also introduce Bayesian Optimization (BO) for Time Series Process Optimization
(BOTSPO), based on traditional BO but featuring a unique time series process
profile generator with reduced dimensions. BOTSPO dynamically optimizes
processes, identifying optimal laser power profiles to attain desired
mechanical properties. The established process trajectory guides online
optimizations, aiming to enhance performance. This paper outlines the digital
twin framework's components, promoting its integration into a comprehensive
system for AM.Comment: 12 Pages, 10 Figures, 1 Table, NAMRC Conferenc
Threshold Energy Switching and Its Application to Wireless Sensing
ABSTRACT A solid state threshold energy switching device based on a relaxation oscillator is discussed in the context of a self energizing wireless pressure sensor. The study is an integral part of the design of a wireless pressure sensor for in-situ injection molding machine cavity pressure measurement and real time process control. The pressure information is measured using a piezoelectric stack and converted to a train of ultrasonic pulses, using the oscillator based threshold switching device, to a receiver outside of the mold. In this paper the threshold switching device is developed, simulated using a circuit simulation program, and validated experimentally. Its properties are discussed with reference to pressure measurement and acoustic signal transmission. INTRODUCTION It has been shown that direct cavity pressure and temperature measurement, more than any indirect method, is related to final part qualit
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