D. Mishra, A. Pal, N. Nemick, A. Saha, V. Prasad, H. Zhang
A simulated, non-pressurized hydrothermal system consisting of a fluid-superposed porous layer is fabricated and used for visualization and measurement of the temperature field using liquid crystal thermography. The system is used for various boundary conditions with pure glycerine as the working fluid and the porous layer is made of 3mm diameter glass beads. Experimental data is recorded using a color CCD camera and flow visualization is obtained through a long exposure video photography. A calibration is performed to relate the temperature with scattered colors at an orthogonal angle to the incoming white light sheet. Quantitative temperature data is obtained through this calibration and compared with the numerical predictions. For numerical studies the system is modeled as a composite layer of fluid and porous charge using the Darcy-Brinkman-Forchheimer flow model. A two-dimensional curvilinear algorithm using finite volume technique with a non-staggered grid is used to simulate the temperature field and transport phenomena for various Rayleigh–Darcy number combinations of varying aspect ratio. The results, for the first time, make an attempt towards understanding the transport process in hydrothermal system through both numerical simulation and experimental validation.
{"title":"Experimental and Numerical Study of Transport Phenomena in a Simulated Hydrothermal Crystal Growth System of Fluid-Saturated Porous Layer","authors":"D. Mishra, A. Pal, N. Nemick, A. Saha, V. Prasad, H. Zhang","doi":"10.1115/imece2000-1482","DOIUrl":"https://doi.org/10.1115/imece2000-1482","url":null,"abstract":"\u0000 A simulated, non-pressurized hydrothermal system consisting of a fluid-superposed porous layer is fabricated and used for visualization and measurement of the temperature field using liquid crystal thermography. The system is used for various boundary conditions with pure glycerine as the working fluid and the porous layer is made of 3mm diameter glass beads. Experimental data is recorded using a color CCD camera and flow visualization is obtained through a long exposure video photography. A calibration is performed to relate the temperature with scattered colors at an orthogonal angle to the incoming white light sheet. Quantitative temperature data is obtained through this calibration and compared with the numerical predictions. For numerical studies the system is modeled as a composite layer of fluid and porous charge using the Darcy-Brinkman-Forchheimer flow model. A two-dimensional curvilinear algorithm using finite volume technique with a non-staggered grid is used to simulate the temperature field and transport phenomena for various Rayleigh–Darcy number combinations of varying aspect ratio. The results, for the first time, make an attempt towards understanding the transport process in hydrothermal system through both numerical simulation and experimental validation.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129775255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Femtosecond laser ablation of titanium and silicon samples has been studied via time-of-flight (TOF), emission spectroscopy and microscopy measurement. Laser pulses of around 100 fs (FWHM) at λ = 800 nm were delivered by a Ti:sapphire femtosecond laser system. A vacuum chamber with a base pressure of 10−7 torr was built for ion TOF measurement. These ion TOF spectra were utilized to determine the velocity distribution of the ejected ions. While nanosecond laser ablation typically generates ions of a few tens of eV, femtosecond laser irradiation even at moderate energy densities can produce energetic ions with energies of up to a few keV. The most probable energy of these fast ions is proportional to the laser fluence. The structure and number of peaks of the TOF spectra varies with the laser fluence. Images of plume emission were captured by an intensified CCD (ICCD) camera. The plume emission spectrum was analyzed by a spectrometer. Laser ablated craters were measured by an interferometric microscope and a scanning electron microscope (SEM). Ablation yield was expressed as a function of laser fluence, and number of shots.
{"title":"Femtosecond Laser Ablation of Titanium and Silicon","authors":"Mengqi Ye, C. Grigoropoulos","doi":"10.1115/imece2000-1474","DOIUrl":"https://doi.org/10.1115/imece2000-1474","url":null,"abstract":"\u0000 Femtosecond laser ablation of titanium and silicon samples has been studied via time-of-flight (TOF), emission spectroscopy and microscopy measurement. Laser pulses of around 100 fs (FWHM) at λ = 800 nm were delivered by a Ti:sapphire femtosecond laser system. A vacuum chamber with a base pressure of 10−7 torr was built for ion TOF measurement. These ion TOF spectra were utilized to determine the velocity distribution of the ejected ions. While nanosecond laser ablation typically generates ions of a few tens of eV, femtosecond laser irradiation even at moderate energy densities can produce energetic ions with energies of up to a few keV. The most probable energy of these fast ions is proportional to the laser fluence. The structure and number of peaks of the TOF spectra varies with the laser fluence. Images of plume emission were captured by an intensified CCD (ICCD) camera. The plume emission spectrum was analyzed by a spectrometer. Laser ablated craters were measured by an interferometric microscope and a scanning electron microscope (SEM). Ablation yield was expressed as a function of laser fluence, and number of shots.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129101729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel Neural Network Based Controller (NNBC) was developed following a comprehensive set of experiments carried out on a pilot-scale stoker test facility at CRE Group Ltd., U.K. The NNBC mimicked the actions of an expert boiler operator, by providing ‘near optimum’ settings of coal feed and air flow, as well as ‘staging’ these parameters during load following conditions, before fine tuning the combustion air under quasi-steady-state conditions. Test results from the online implementation of the NNBC have demonstrated that improved transient and steady-state combustion conditions were attained. The prototype NNBC thus provides both stoker manufacturers and users with a means of reducing pollutant emissions, as well as improving the combustion efficiency of this type of coal firing equipment.
{"title":"The Development of a Neural Network Based System for the Optimal Control of Chain-Grate Stoker-Fired Boilers","authors":"A. Chong, S. Wilcox, J. Ward","doi":"10.1115/imece2000-1467","DOIUrl":"https://doi.org/10.1115/imece2000-1467","url":null,"abstract":"\u0000 A novel Neural Network Based Controller (NNBC) was developed following a comprehensive set of experiments carried out on a pilot-scale stoker test facility at CRE Group Ltd., U.K. The NNBC mimicked the actions of an expert boiler operator, by providing ‘near optimum’ settings of coal feed and air flow, as well as ‘staging’ these parameters during load following conditions, before fine tuning the combustion air under quasi-steady-state conditions. Test results from the online implementation of the NNBC have demonstrated that improved transient and steady-state combustion conditions were attained. The prototype NNBC thus provides both stoker manufacturers and users with a means of reducing pollutant emissions, as well as improving the combustion efficiency of this type of coal firing equipment.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123995772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A numerical analysis, using a control volume approach, is conducted to study the transient cutting tool temperatures with temperature dependent thermal properties. With temperature dependent thermal properties, the governing conduction equation is non-linear and thus, the standard analytical solutions are no longer valid. In any cutting processes, the temperature distribution is intrinsically three-dimensional and very steep temperature gradient may be generated in the vicinity of the tool-chip interface. In this region, where the maximum temperature occurs, the effect of variable thermal properties may become important. The full three-dimensional non-linear transient heat conduction equation is solved numerically to study these non-linear effects on cutting tool temperatures. The extremely small size of the heat input zone (tool-chip interface), relative to the tool insert rake surface area, requires the mesh to be dense enough in order to obtain accurate solutions. This usually requires very intensive computational efforts. Due to the size of the discretized domain, an efficient algorithm is desirable in the solution of the problem. Four different iterative schemes are explored, and an optimized numerical scheme is chosen to significantly reduce the required computing time. This numerical model can be used for process development in an industrial setting. The effect of two different heat flux input profiles, a spatially uniform plane heat flux and a spatially non-uniform plane heat flux at the tool-chip interface, on the tool temperatures are also investigated in the present study. Some recommendations are given regarding the condition when these non-linear effects can not be ignored.
{"title":"Non-Linear Numerical Analysis in Transient Cutting Tool Temperatures","authors":"T. Jen, G. Gutiérrez, S. Eapen","doi":"10.1115/imece2000-1480","DOIUrl":"https://doi.org/10.1115/imece2000-1480","url":null,"abstract":"\u0000 A numerical analysis, using a control volume approach, is conducted to study the transient cutting tool temperatures with temperature dependent thermal properties. With temperature dependent thermal properties, the governing conduction equation is non-linear and thus, the standard analytical solutions are no longer valid. In any cutting processes, the temperature distribution is intrinsically three-dimensional and very steep temperature gradient may be generated in the vicinity of the tool-chip interface. In this region, where the maximum temperature occurs, the effect of variable thermal properties may become important. The full three-dimensional non-linear transient heat conduction equation is solved numerically to study these non-linear effects on cutting tool temperatures. The extremely small size of the heat input zone (tool-chip interface), relative to the tool insert rake surface area, requires the mesh to be dense enough in order to obtain accurate solutions. This usually requires very intensive computational efforts. Due to the size of the discretized domain, an efficient algorithm is desirable in the solution of the problem. Four different iterative schemes are explored, and an optimized numerical scheme is chosen to significantly reduce the required computing time. This numerical model can be used for process development in an industrial setting. The effect of two different heat flux input profiles, a spatially uniform plane heat flux and a spatially non-uniform plane heat flux at the tool-chip interface, on the tool temperatures are also investigated in the present study. Some recommendations are given regarding the condition when these non-linear effects can not be ignored.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124135645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Silica aerogels are unique nanostructured materials that possess many distinctive qualities, including extremely low densities and thermal conductivities, very high surface-area-to-volume ratios, and large strength-to-weight ratios. Aerogels, however, are very brittle, and are not readily shaped using traditional machining operations. Ultrafast laser processing may provide an alternative for precision shaping and machining of these materials. This paper discusses investigations of ultrafast laser machining of aerogels for material removal and micromachining. The advantages of ultrafast laser processing include a minimal thermal penetration region and low processing temperatures, precision removal of material, and good-quality feature definition. In this work, an amplified femtosecond Ti:sapphire laser system is used to investigate the breakdown threshold, material removal rate, and specific issues associated with laser processing of aerogels, as well as recommendations for further investigations for these unique materials.
{"title":"Laser Processing of Silica Aerogels Using Ultrashort Pulses","authors":"J. Sun, J. Longtin, P. Norris","doi":"10.1115/imece2000-1477","DOIUrl":"https://doi.org/10.1115/imece2000-1477","url":null,"abstract":"\u0000 Silica aerogels are unique nanostructured materials that possess many distinctive qualities, including extremely low densities and thermal conductivities, very high surface-area-to-volume ratios, and large strength-to-weight ratios. Aerogels, however, are very brittle, and are not readily shaped using traditional machining operations. Ultrafast laser processing may provide an alternative for precision shaping and machining of these materials. This paper discusses investigations of ultrafast laser machining of aerogels for material removal and micromachining. The advantages of ultrafast laser processing include a minimal thermal penetration region and low processing temperatures, precision removal of material, and good-quality feature definition. In this work, an amplified femtosecond Ti:sapphire laser system is used to investigate the breakdown threshold, material removal rate, and specific issues associated with laser processing of aerogels, as well as recommendations for further investigations for these unique materials.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122155165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The vacuum assisted resin transfer molding (VARTM) of sandwich panels may be facilitated by using high permeability layers over the skins or adding grooves in the surfaces of the core. The present paper investigates the advantages and disadvantages of both methods in terms of manufacturing cost and time through simulations and experimental observations. Before comparison, each method is optimized through simulations. The panel geometry and the injection pressure are held constant. The design parameters are the number of high permeability layers, and the number and size of grooves. The optimized processes are finally compared with each other in terms of the aforementioned cost and time. Meanwhile, the sensitivities of several important parameters in the cost model to the optimal result are studied.
{"title":"Optimization of Vacuum Assisted Resin Transfer Molding for Sandwich Panels","authors":"Jean Dai, D. Pellaton, H. Hahn","doi":"10.1115/imece2000-1492","DOIUrl":"https://doi.org/10.1115/imece2000-1492","url":null,"abstract":"\u0000 The vacuum assisted resin transfer molding (VARTM) of sandwich panels may be facilitated by using high permeability layers over the skins or adding grooves in the surfaces of the core. The present paper investigates the advantages and disadvantages of both methods in terms of manufacturing cost and time through simulations and experimental observations. Before comparison, each method is optimized through simulations. The panel geometry and the injection pressure are held constant. The design parameters are the number of high permeability layers, and the number and size of grooves. The optimized processes are finally compared with each other in terms of the aforementioned cost and time. Meanwhile, the sensitivities of several important parameters in the cost model to the optimal result are studied.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124769428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An artificial neural network (ANN) is a massively parallel, dynamic system of processing elements, neurons, which are connected in complicated patterns to allow for a variety of interactions among the inputs to produce the desired output. It has the ability to learn directly from example data rather than by following the programmed rules based on a knowledge base. There is virtually no limit to what an ANN can predict or decipher, so long as it has been trained properly through examples which encompass the entire range of desired predictions. This paper provides an overview of such strategies needed to build accurate ANN models. Following a general introduction to artificial neural networks, the paper will describe different techniques to build and train ANN models. Step-by-step procedures will be described to demonstrate the mechanics of building neural network models, with particular emphasis on feedforward neural networks using back-propagation learning algorithm. The network structure and pre-processing of data are two significant aspects of ANN model building. The former has a significant influence on the predictive capability of the network [1]. Several studies have addressed the issue of optimal network structure. Kim and May [2] use statistical experimental design to determine an optimal network for a specific application. Bhat and McAvoy [3] propose a stripping algorithm, starting with a large network and then reducing the network complexity by removing unnecessary weights/nodes. This ‘complex-to-simple’ procedure requires heavy and tedious computation. Villiers and Bernard [4] conclude that although there is no significant difference between the optimal performance of one or two hidden layer networks, single layer networks do better classification on average. Marwah et al. [5] advocate a simple-to-complex methodology in which the training starts with the simplest ANN structure. The complexity of the structure is incrementally stepped-up till an acceptable learning performance is obtained. Preprocessing of data can lead to substantial improvements in the training process. Kown et al. [6] propose a data pre-processing algorithm for a highly skewed data set. Marwah et al. [5] propose two different strategies for dealing with the data. For applications with a significant amount of historical data, smart select methodology is proposed that ensures equal weighted distribution of the data over the range of the input parameters. For applications, where there is scarcity of data or where the experiments are expensive to perform, a statistical design of experiments approach is suggested. In either case, it is shown that dividing the data into training, testing and validation ensures an accurate ANN model that has excellent predictive capabilities. The paper also describes recently developed concepts of physical-neural network models and model transfer techniques. In the former, an ANN model is built on the data generated through the ‘first-pr
人工神经网络（ANN）是一个由处理元件（神经元）组成的大规模并行动态系统，这些神经元以复杂的模式连接在一起，允许输入之间进行各种交互，以产生所需的输出。它能够直接从示例数据中学习，而不是遵循基于知识库的编程规则。只要通过包含整个所需预测范围的示例进行适当的训练，ANN 的预测或破译能力几乎没有限制。本文概述了建立准确的人工神经网络模型所需的策略。在对人工神经网络进行一般性介绍后，本文将介绍建立和训练人工神经网络模型的不同技术。本文将分步介绍建立神经网络模型的机制，特别强调使用反向传播学习算法的前馈神经网络。网络结构和数据预处理是建立神经网络模型的两个重要方面。前者对网络的预测能力有重大影响 [1]。有几项研究探讨了最佳网络结构的问题。Kim 和 May [2] 使用统计实验设计来确定特定应用的最佳网络。Bhat 和 McAvoy [3] 提出了一种剥离算法，从一个大型网络开始，通过去除不必要的权重/节点来降低网络复杂度。这种 "化繁为简 "的过程需要繁重而乏味的计算。Villiers 和 Bernard [4] 认为，虽然单层或双层隐藏网络的最佳性能没有显著差异，但单层网络的平均分类效果更好。Marwah 等人[5]主张采用从简单到复杂的方法，即从最简单的 ANN 结构开始训练。然后逐步提高结构的复杂度，直到获得可接受的学习效果。对数据进行预处理可以大大改进训练过程。Kown 等人 [6] 提出了一种针对高度倾斜数据集的数据预处理算法。Marwah 等人[5]提出了两种不同的数据处理策略。对于具有大量历史数据的应用，提出了智能选择方法，确保数据在输入参数范围内的等权分布。对于数据稀缺或实验成本高昂的应用，建议采用统计实验设计方法。无论在哪种情况下，实验都表明，将数据分为训练、测试和验证，可确保建立具有出色预测能力的精确 ANN 模型。本文还介绍了最近开发的物理-神经网络模型概念和模型转移技术。在物理-神经网络模型中，ANN 模型是根据所考虑过程的 "第一原理 "分析或数值模型生成的数据建立的。结果表明，这种被称为物理神经网络模型的模型具有第一原理模型的准确性，但执行速度却要快上几个数量级。由于这种模型具有许多复杂过程物理模型通常固有的所有近似值，因此开发了模型转移技术[6]，可以经济地开发精确的过程设备模型。我们将以热基材料加工为例，说明相关基本概念的应用。
{"title":"Strategies for Building Artificial Neural Network Models","authors":"R. Mahajan","doi":"10.1115/imece2000-1464","DOIUrl":"https://doi.org/10.1115/imece2000-1464","url":null,"abstract":"\u0000 An artificial neural network (ANN) is a massively parallel, dynamic system of processing elements, neurons, which are connected in complicated patterns to allow for a variety of interactions among the inputs to produce the desired output. It has the ability to learn directly from example data rather than by following the programmed rules based on a knowledge base. There is virtually no limit to what an ANN can predict or decipher, so long as it has been trained properly through examples which encompass the entire range of desired predictions. This paper provides an overview of such strategies needed to build accurate ANN models. Following a general introduction to artificial neural networks, the paper will describe different techniques to build and train ANN models. Step-by-step procedures will be described to demonstrate the mechanics of building neural network models, with particular emphasis on feedforward neural networks using back-propagation learning algorithm.\u0000 The network structure and pre-processing of data are two significant aspects of ANN model building. The former has a significant influence on the predictive capability of the network [1]. Several studies have addressed the issue of optimal network structure. Kim and May [2] use statistical experimental design to determine an optimal network for a specific application. Bhat and McAvoy [3] propose a stripping algorithm, starting with a large network and then reducing the network complexity by removing unnecessary weights/nodes. This ‘complex-to-simple’ procedure requires heavy and tedious computation. Villiers and Bernard [4] conclude that although there is no significant difference between the optimal performance of one or two hidden layer networks, single layer networks do better classification on average. Marwah et al. [5] advocate a simple-to-complex methodology in which the training starts with the simplest ANN structure. The complexity of the structure is incrementally stepped-up till an acceptable learning performance is obtained. Preprocessing of data can lead to substantial improvements in the training process. Kown et al. [6] propose a data pre-processing algorithm for a highly skewed data set. Marwah et al. [5] propose two different strategies for dealing with the data. For applications with a significant amount of historical data, smart select methodology is proposed that ensures equal weighted distribution of the data over the range of the input parameters. For applications, where there is scarcity of data or where the experiments are expensive to perform, a statistical design of experiments approach is suggested. In either case, it is shown that dividing the data into training, testing and validation ensures an accurate ANN model that has excellent predictive capabilities.\u0000 The paper also describes recently developed concepts of physical-neural network models and model transfer techniques. In the former, an ANN model is built on the data generated through the ‘first-pr","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"432 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130494036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, a generalized solution for the thermoelastic plane wave in a semi-infinite solid induced by pulsed laser heating is formulated in the form of Fourier series. The solution takes into account the non-Fourier effect in heat conduction and the coupling effect between temperature and strain rate, which play significant roles in ultra-short pulsed laser heating. Based on this solution, calculations are conducted to study stress waves induced by different laser parameters. It is found that with the same maximum surface temperature increase, a shorter pulsed laser induces a much stronger stress wave in a solid. The non-Fourier effect causes a higher surface temperature increase, but a weaker stress wave. The surface displacement accompanying thermal expansion shows a time delay to the laser pulse in femtosecond laser heating. On the contrary, surface displacement and heating occur simultaneously in nano- and picosecond laser heating. In femtosecond laser heating, results show that the coupling effect attenuates the stress wave and extends the duration of the stress wave. This may explain the minimal damage in ultra-short laser materials processing.
{"title":"Thermoelastic Wave Induced by Pulsed Laser Heating","authors":"Xinwei Wang, Xianfan Xu","doi":"10.1115/imece2000-1478","DOIUrl":"https://doi.org/10.1115/imece2000-1478","url":null,"abstract":"\u0000 In this work, a generalized solution for the thermoelastic plane wave in a semi-infinite solid induced by pulsed laser heating is formulated in the form of Fourier series. The solution takes into account the non-Fourier effect in heat conduction and the coupling effect between temperature and strain rate, which play significant roles in ultra-short pulsed laser heating. Based on this solution, calculations are conducted to study stress waves induced by different laser parameters. It is found that with the same maximum surface temperature increase, a shorter pulsed laser induces a much stronger stress wave in a solid. The non-Fourier effect causes a higher surface temperature increase, but a weaker stress wave. The surface displacement accompanying thermal expansion shows a time delay to the laser pulse in femtosecond laser heating. On the contrary, surface displacement and heating occur simultaneously in nano- and picosecond laser heating. In femtosecond laser heating, results show that the coupling effect attenuates the stress wave and extends the duration of the stress wave. This may explain the minimal damage in ultra-short laser materials processing.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122951687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Artificial neural networks (ANNs) are good at approximating complex and non-linear data. In addition, they have excellent predictive capabilities and can be configured to be self-adaptive. As a result of these characteristics, the potential applications of ANNs are many and in diverse fields. These range from predicting the output of a manufacturing process through differentiating between handwritten letters to predicting the winner of a horse race. In this paper, we focus on applications of artificial neural networks to thermal systems including chemical vapor deposition, thermal management and heat exchangers.
{"title":"Applications of Artificial Neural Network Analysis in Thermal Systems","authors":"R. Mahajan, K. T. Yang","doi":"10.1115/imece2000-1469","DOIUrl":"https://doi.org/10.1115/imece2000-1469","url":null,"abstract":"\u0000 Artificial neural networks (ANNs) are good at approximating complex and non-linear data. In addition, they have excellent predictive capabilities and can be configured to be self-adaptive. As a result of these characteristics, the potential applications of ANNs are many and in diverse fields. These range from predicting the output of a manufacturing process through differentiating between handwritten letters to predicting the winner of a horse race. In this paper, we focus on applications of artificial neural networks to thermal systems including chemical vapor deposition, thermal management and heat exchangers.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132734232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The importance of predicting and reducing thermal expansion errors in workpieces is becoming greater as better precision machining processes are developed. An artificial neural network model to estimate the workpiece thermal expansion errors in real-time during precision machining operations is developed and compared with experimental results. A finite element model of workpiece thermal expansion has been created to predict expansions in a thin cylinder undergoing a turning process. The neural network has been trained using finite element model solutions over a range of conditions to allow for changing machining parameters. To realize “on-line” capability, the measurable values of heat flux into the workpiece, surface heat transfer coefficient, and tool location are used as inputs and the expansion as the output for the neural network. The estimations of the network are compared with experimental results from a turning process on a large diameter aluminum cylinder. There is reasonable agreement between measured and estimated expansions with an average error of 18%. The neural network has not been trained at the cutting conditions used during the experiment. The speed of the neural network estimation is much greater than the solution to the finite element model. The finite element model required over 15 minutes to solve on a Pentium 133Mhz computer. The neural network calculated the expansions easily at 1 Hz during the experiment on the same computer. With real-time estimation using measurable data, compensation can be made in the tool path to correct for these errors. The application of this method to precision machining processes has the capability of greatly reducing the error caused by workpiece thermal expansions.
{"title":"A Real-Time Neural Network Estimator for Workpiece Thermal Expansion Errors","authors":"A. Yoder, R. Smith","doi":"10.1115/imece2000-1472","DOIUrl":"https://doi.org/10.1115/imece2000-1472","url":null,"abstract":"\u0000 The importance of predicting and reducing thermal expansion errors in workpieces is becoming greater as better precision machining processes are developed. An artificial neural network model to estimate the workpiece thermal expansion errors in real-time during precision machining operations is developed and compared with experimental results. A finite element model of workpiece thermal expansion has been created to predict expansions in a thin cylinder undergoing a turning process. The neural network has been trained using finite element model solutions over a range of conditions to allow for changing machining parameters. To realize “on-line” capability, the measurable values of heat flux into the workpiece, surface heat transfer coefficient, and tool location are used as inputs and the expansion as the output for the neural network. The estimations of the network are compared with experimental results from a turning process on a large diameter aluminum cylinder. There is reasonable agreement between measured and estimated expansions with an average error of 18%. The neural network has not been trained at the cutting conditions used during the experiment. The speed of the neural network estimation is much greater than the solution to the finite element model. The finite element model required over 15 minutes to solve on a Pentium 133Mhz computer. The neural network calculated the expansions easily at 1 Hz during the experiment on the same computer. With real-time estimation using measurable data, compensation can be made in the tool path to correct for these errors. The application of this method to precision machining processes has the capability of greatly reducing the error caused by workpiece thermal expansions.","PeriodicalId":306962,"journal":{"name":"Heat Transfer: Volume 3","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133873718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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