NAMRC 49 Fast-Tracked Research Papers to Journal of Manufacturing Systems and Journal of Manufacturing Processes

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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

Preface

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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

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

Thermal properties of cubic KTa 1 x Nb x O 3 crystals

Cubic potassium tantalite niobate KTa1−xNbxO3 KTN crystals of large size, good quality, and varying Nb concentration have been grown by the Czochralski method and their thermal properties have been systematically studied. The melting point, molar enthalpy of fusion, and molar entropy of fusion of the crystals were determined to be: 1536.9 K, 12 068.521 J mol−1, and 7.85 J K−1 mol−1 for KTa0.67Nb0.33O3; and 1520.61 K, 15 352.511 J mol−1, and 10.098 J K−1 mol−1 for KTa0.67Nb0.33O3, respectively. Based on the data, the Jackson factor was calculated to be 0.994f and 1.214f for KTa0.67Nb0.33O3 and KTa0.63Nb0.37O3, respectively. The thermal expansion coefficients over the temperature range of 298.15−773.15 K are: =4.0268 10−6 /K, 6.4428 10−6 /K, 6.5853 10−6 /K for KTaO3, KTa0.67Nb0.33O3, and KTa0.63Nb0.37O3, respectively. The density follows an almost linear decrease when the temperature increases=from 298.15 to 773.15 K. The measured specific heats at 303.15 K are: 0.375 J g−1 K−1 for KTaO3; 0.421 J g−1 K−1 for KTa0.67Nb0.33O3, and 0.430 J g−1 K−1 for KTa0.63Nb0.37O3 The thermal diffusion coefficients of the crystals were measured over the temperature range from 303.15−563.15 K. The calculated thermal conductivity values of KTaO3, KTa0.67Nb0.33O3, and KTa0.63Nb0.37O3 at 303.15 K are 8.551, 5.592, and 4.489 W m−1 K−1, respectively. The variation of these thermal properties versus Nb concentration is qualitatively analyzed. These results show that crystalline KTN is a promising material for optical applications