A carbon emission quantitation model and experimental evaluation for machining process considering tool wear condition

Finite Element (FE) modeling of machining processes has received growing attention over the last two decades. Since machining processes operate at severe deformation conditions, involving very high strain, strain rate, stress, and temperature, the modeling procedure is still a challenging task even with new advanced software and computers. Therefore, most of the published research works were mainly performed for the simplest configuration of machining known as orthogonal cutting, in which a plane strain deformation state is assumed. This configuration leads to reducing the number of elements in the FE model and the computational time of the solution, compared to conventional machining processes (turning, milling, and drilling). Nevertheless, FE modeling of orthogonal turning is still considered as an open-ended subject as most of the phenomena involved in the orthogonal turning process, which also exist in other machining operations, are not fully well understood. The present review article deals with finite element modeling of orthogonal machining process. The paper consists of several related parts. First, the fundamentals of the FE simulation of orthogonal turning process are briefly described. Then, a detailed review on the FE prediction of machining characteristics including chip morphology, cutting forces, cutting temperature, tool wear, and burr formation is provided. Also, the FE prediction of surface integrity characteristics including residual stresses and microstructural changes is discussed. The influence of input model and parameters including thermal, material, and frictional models as well as size and arrangement of elements on machining and surface integrity characteristics are explained as well. Both technical and statistical aspects of the FE simulation of orthogonal turning are treated. Since there is no review paper thoroughly and specifically discussing the methods and findings of the finite element simulation of turning operations, the present article can be used as a reference for researchers who are active and/or interested in this filed.

Optimization of cutting parameters considering tool wear conditions in low-carbon manufacturing environment

In order to calculate the carbon emissions in the construction process to achieve low-carbon buildings and low-carbon construction, the author puts forward the calculation and evaluation of building thermal energy consumption and carbon emissions based on building information modeling (BIM) technology. The author first proposed the important value and application of BIM technology in energy consumption evaluation of green buildings, taking a gymnasium as an example, a carbon emission accounting system for building construction and installation process is established based on BIM technology, and the carbon emissions in building construction and installation process are calculated and analyzed. The results show that the carbon emission during the construction and installation of a gymnasium is 766300 tons, of which the carbon emission caused by building materials is 737200 tons, the carbon emission caused by mechanical equipment is 4500 tons, and that caused by office and living is 34500 tons, accounting for 94.90%, 0.59%, and 4.51%, respectively. In conclusion through data analysis, determine the largest carbon emission source in the construction process, and then propose targeted carbon emission reduction measures in the construction process of the construction industry.

Analysis and assessment of life-cycle carbon emissions of space frame structures

PDF HTML阅读 XML下载 导出引用 引用提醒 基于小尺度的高寒牧区碳排放估算——以甘南州合作市为例 DOI: 10.5846/stxb201301280176 作者: 作者单位: 西北师范大学地理与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然基金地区项目(41261042); 2011年度省属高校基本科研业务费专项资金项目; 2010年度甘肃省高校研究生导师基金项目(1001-22); 西北师范大学青年教师科研能力提升计划-骨干项目(SKQNGG10029) The estimates of carbon emission based on small-scale in alpine pastures: a case study of Hezuo in Gannan Autonomous Prefecture, China Author: Affiliation: Geography and Environmental Science College, Northwest Normal University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:碳排放是目前国内外研究的热点问题.纵观碳排放计算,关键涉及到两个环节:一是研究尺度的选择,二是评估参数(即碳排放/吸收系数)的确定.高寒牧区还必须充分考虑牲畜碳排放.以甘南州合作市为例,将研究尺度缩小到41个行政村,并采用最新的土地二调数据,对土地利用碳排放和碳汇、生活和牲畜碳排放进行了系统地测算.结果表明: (1)从土地利用的角度考察,合作市总碳排放表征为"碳亏"态势.总碳排放量为24374.82 t/a,其中土地利用碳排放量为4908.21 t/a,牲畜代谢碳排放量为3703.94 t/a,城乡居民生活碳排放量为15762.67 t/a.而碳汇量仅为1949.74 t/a,"碳亏"量为22425.08 t/a.(2)牲畜是高寒牧区呼吸碳排放的主体,其碳排量为3703.94 t/a,其中牛是碳排放的主体,其碳排量占90%以上.(3)城乡居民生活碳排量为15762.67 t/a,城镇明显高于农村,生存型碳排放强度高于发展型碳排放强度.(4)碳排放局分布势面与碳汇分布局势面基本呈反向分布态势,但总的碳排放格局取决于碳排放而非碳汇,空间分布表现为从城区-半农半牧区-纯牧区逐级递减的趋势. Abstract:Carbon emissions are the hot issues of the current domestic and international research. Throughout the calculation of carbon emissions, the key involves two aspects: First, the choice of research scale, second, the definition to assessment parameters (carbon emission/absorption coefficient). The alpine pastoral must also full consider Livestock carbon emissions. The paper carried out a systematic calculation of land use carbon emissions and carbon sinks, which combined characteristics of alpine pastures and the lastest second national survey data of land use,by the way of scale-down to the 41 administrative villages. The results show that: (1) The total carbon emissions characterization show as "carbon deficit" situation. The total carbon emissions24374.82t/a, Land-use carbon emissions 4908.21 t/a, the livestock metabolism carbon emissions 3703.94 t/a, and urban-rural residents living carbon emissions 15762.67 t/a. Carbon sinks only have 1949.74 t/a,the amount of "carbon deficit "reaches to 22425.08 t/a. (2)In the alpine pastoral, livestock is the main body of breathing carbon emissions, carbon emissions for 3703.94 t/a, which the cattle are the main factors of the carbon emissions, more than 90% of its carbon emissions.(3)Urban-rural residents living carbon emissions reaches to 15762.67 t/a, the towns significantly higher than rural areas, This suggests that the survival-type carbon emission intensity is higher than the development of carbon emission intensity.(4)From the city-semi-agricultural and semi-pastoral areas-pure pastoral areas, the spatial pattern of carbon emission shows a decreasing trend. 参考文献 相似文献 引证文献

The application of artificial intelligence (AI) techniques in modeling of machining process has been investigated by many researchers. Fuzzy logic (FL) as a well-known AI technique is effectively used in modeling of machining processes such as to predict the surface roughness and to control the cutting force in various machining processes. This paper is started with the introduction to definition of FL and machining process, and their relation. This paper then presents five types of analysis conducted on FL techniques used in machining process. FL was considered for prediction, selection, monitoring, control and optimization of machining process. Literature showed that milling contributed the highest number of machining operation that was modeled using FL. In terms of machining performance, surface roughness was mostly studied with FL model. In terms of fuzzy components, center of gravity method was mostly used to perform defuzzification, and triangular was mostly considered to perform membership function. The reviews extend the analysis on the abilities, limitations and effectual modifications of FL in modeling based on the comments from previous works that conduct experiment using FL in the modeling and review by few authors. The analysis leads the author to conclude that FL is the most popular AI techniques used in modeling of machining process.

Wavelet transforms and fuzzy techniques are used to monitor tool breakage and wear conditions in real time according to the measured spindle and feed motor currents, respectively. First, continuous and discrete wavelet transforms are used to decompose the spindle and feed ac servo motor current signals to extract signal features so as to detect the breakage of drills successfully. Next, the models of the relationships between the current signals and the cutting parameters are established under different tool wear states. Subsequently, fuzzy classification methods are used to detect tool wear states based on the above models. Finally, the two methods above are integrated to establish an intelligent tool condition monitoring system for drilling operations. The monitoring system can detect tool breakage and tool wear conditions using very simple current sensors. Experimental results show that the proposed system can reliably detect tool conditions in drilling operations in real time and is viable for industrial applications.

Sand casting is one of the cornerstones of modern industrial production, which has an important impact on resource consumption and environmental impact. In order to realize the decarbonization of sand casting, it is necessary to conduct an effective quantitative analysis of the impact of the casting production process on the environment. This study focuses on the quantitative calculation and optimization of carbon emissions in the sand mold casting process of a single casting. Based on existing research on carbon emission evaluation in the casting process, the carbon emission characteristics of energy, materials, and exhaust gases in each production stage of the casting process are analyzed. Combined with actual production process parameters, a carbon emission evaluation model for the casting process is established, which can quantitatively calculate and analyze the carbon emissions in the production process of a single casting.Then, the effectiveness of the method was verified through production examples. The results of this study can provide an effective analytical method for achieving low-carbonization in the casting process from a microscopic perspective, which will provide accurate data support for further reducing carbon emissions.

This paper presents a review of recent advances in modeling and simulation of conventional metal machining processes, which continue to dominate a significant part of all machining processes, and in recent years, the need for predictive models for machining processes has grown in importance in the digital manufacturing age. Significant advances have been made in modeling the mechanics of cutting in conventional machining, driven by industrial need and enabled by rapid advances in computational power. The paper surveys the state-of-the-art in analytical and numerical modeling of conventional metal machining processes with a focus on their ability to predict useful performance attributes including chip geometry, forces, temperatures, tool wear, residual stress, and microstructure. Also included in the review is a discussion of the industrial use of modeling and simulation tools for conventional machining. Additionally, the practical applicability, implementation benefits, and methodological limitations of conventional machining process modeling have been examined. The paper concludes with a summary of future research directions in modeling and simulation of conventional metal machining processes.

In a machining process, the workpiece is usually machined by several passes to achieve the desired dimension and form. Workpiece deviation generated in the previous pass will affect the next pass. A new point-based variation propagation model for a single machining process with multi-passes was proposed in this article to investigate the relationships between different passes. The effects of locating errors, geometric errors of machine tool, and tool deflection on workpiece deviation are analyzed. Based on these analyses, a point-based variation propagation model for a machining process with multi-passes was derived. By including the influence of the tool deflection, a linkage between workpiece deviation and the cutting parameters is established. Since different passes are distinguished by their cutting parameters, the relationships between different passes are built up via this model. At the end of this article, case studies were carried out to validate the model and its application in cutting parameters’ optimization.

Monitoring tool wear conditions and estimating tool lives are crucial for automatic metal cutting manufacturing processes. Accurate estimation of tool wear status can optimize tool usage and tool replacement, which in turn leads to improvement of product quality and reduction of downtime and costs. Various methods have been proposed to evaluate tool wear conditions using multiple sensory signals during the cutting process. However, because the signal-to-noise ratio is extremely low in the machining process, the accuracy of tool wear evaluation still needs to be improved. In this paper, machine learning methods were explored to estimate the tool wear conditions based on the experimental data provided by the 2010 PHM society conference data challenge. A self-organizing map was designed to identify the tool wear conditions into 16 levels. According to the correlation coefficients between sensory datasets and the tool wear levels, 14 features were selected for tool wear estimation. A feedforward backprop neural network and a support vector machine with the fine Gaussian kernel were constructed for tool wear estimation. The testing results showed that the root mean square for neural network model was 0.9866 and for the support vector model was 1.4985.

Nickel-Based Superalloys (NBSAs) are widely used for components subjected to high-temperature applications due to their excellent mechanical strength, toughness, and corrosion resistance. Despite favorable properties, NBSAs work-harden during machining, resulting in acute temperature rise at the cutting edge, severe plastic deformation, and rapid tool wear. The lower thermal conductivity, intense friction at the chip-tool interface, chemical affinity with tool material, and temperature gradients typically lead to abrupt crater formation or cutting-edge chipping in addition to rapid flank wear. Three distinct phenomena characterize tool wear during end milling of NBSAs; rapid flank wear, abrupt crater formation, and cutting-edge chipping. The continued use of worn or damaged cutting tools leads to poor surface finish and, eventually, catastrophic failures, resulting in significant machine downtime. As each tool wear condition has a unique mitigation strategy, timely identification and classification are imperative to implement solutions that minimize wear and guide tool replacement. In recent years, the augmentation of vision-based systems with pre-trained Convolutional Neural Networks (CNNs) has shown great promise in failure identification and classification tasks. The present work develops an image-based classification model using a pre-trained CNN, Efficient-Net-b3, for identifying three tool wear conditions during end milling of Inconel 718 (IN718). The network training uses labeled image datasets that capture various tool wear characteristics generated using end-milling experiments. The extensive training dataset requirement of the CNN was met using image augmentation techniques by varying the brightness, contrast, and orientation of the captured images. The prediction abilities of the algorithm were corroborated by validating the model on a validation dataset and further testing on new unseen datasets. It has been shown that Efficient-Net-b3 demonstrates robust prediction accuracy for all three tool wear conditions. The proposed classification model can be further employed for developing an on-machine vision-based tool wear classification system.

This paper studied the rule of micro-hole in electrochemical jet machining (EJM) of TC4 alloy and established the mathematical model of machining process and predicted the machining profile. Considering the influence of machining gap and machining time, orthogonal experiment was designed. This paper established the mathematical model of the electrochemical jet machining process of TC4 alloy based on the response surface analysis (RSA) method. The results indicate that the electrochemical jet can improve the directivity of machining, reducing the machining gap can improve the machining efficiency, but the jet will cause secondary corrosion and abrupt change of current at the edge of inlet. The mathematical model based on response surface analysis is accurate after variance test. The experimental results show that the average error between the established prediction model of machining depth and the actual value is 2.32%, and the average error of the prediction model of inlet radius is 2.18%.

Based on the data of foreign trade import and export volume and carbon emissions in China from1990 to 2012, statistical analysis through econometric models, the results showed that both showed a significant linear correlation. By stationary test and co-integration explain the existence of long-term stable equilibrium relationship between carbon emissions and foreign trade, through error correction shows the short-term dynamic relationship between them, the results show that the development of foreign trade will continue affect our environment. Finally, we put forward a proposal of industrial development. As economic of China strength, our influence in the world also greater and greater. According to customs statistics, 2012 foreign trade volume of China reached $3 866.76 billion, more than the United States ranks first in the world. On the surface, China has obtained the aura of world trade, but follow the harvest is big business environmental issues, in particular pollution problems. According to the British Tyndall Centre for Climate Change Research Global Carbon Project research in 2012 shows that, in 2011, the first three countries in global carbon emissions were China (28%), the U.S. (16%), EU (11%)[1],as can be seen from the data, our carbon emissions have exceeded the sum of the U.S. and the EU. So the analysis of the relationship between carbon emissions and our foreign trade, have particularly important practical significance for the sustainable development of trade and ecological terms. Analysis of the data and relationships 1 Calculation of carbon emissions Since in the China Statistical Yearbook, did not give the corresponding carbon emissions statistics, so we need to find a formula of carbon emissions over the years. The main source of carbon emissions is the fossil fuel, fossil fuel energy including coal, oil and natural gas three categories. So for the calculation of carbon emissions, Academia use the consumption of various types of energy multiplied by the carbon emission factor of various types energy, and then add the sum total[2].This article uses a formula for estimating carbon emissions from Xu Guoquan[3] and other scholars. The formula is:

Reliable and precise multi-step-ahead tool wear state prediction is significant to modern industries for maintaining part quality and reducing cost. This study proposes a Clustering Feature-based Recurrent Fuzzy Neural Network (CFRFNN) for tool wear state monitoring and remaining useful life (RUL) prediction based on K-means Clustering, Recurrent Fuzzy Neural Network (RFNN) and Genetic Algorithm (GA). K-means Clustering method is utilized to realize tool wear state definition and input signal division, which reduces the dependence on the prior knowledge of tool wear degree and improves the prediction accuracy. Then, an enhanced RFNN model is designed and applied on the clustered features to predict tool wear state. The optimized GA technique is helpful for adaptive optimization of model parameters, which significantly improves convergence rate and prediction accuracy. The experiments on tool state prediction are performed to validate superiority of CFRFNN, and the results demonstrate that the proposed network could reasonably configure the complex non-stationary tool wear process and have high prediction accuracy of tool wear state.