Experimental study on energy consumption of computer numerical control machine tools

Machining processes are responsible for substantial environmental impacts due to their great energy consumption. Accurately characterizing the energy consumption of machining processes is a starting point to increase manufacturing energy efficiency and reduce their associated environmental impacts. The energy calculation of machining processes depends on the availability of energy supply data of machine tools. However, the energy supply can vary greatly among different types of machine tools so that it is difficult to obtain the energy data theoretically. The aim of this research was to investigate the energy characteristics and obtain the power models of computer numerical control (CNC) machine tools through an experimental study. Four CNC lathes, two CNC milling machines and one machining center were selected for experiments. Power consumption of non-cutting motions and material removal was measured and compared for the selected machine tools. Here, non-cutting motions include standby, cutting fluid spraying, spindle rotation and feeding operations of machine tools. Material removal includes turning and milling. Results show that the power consumption of non-cutting motions and milling is dependent on machine tools while the power consumption of turning is almost independent from the machine tools. The results imply that the energy saving potential of machining processes is tremendous

An investigation into reducing the spindle acceleration energy consumption of machine tools

Machine tools are widely used in the manufacturing industry, and consume large amount of energy. Spindle acceleration appears frequently while machine tools are working. It produces power peak which is highly energy intensive. As a result, a considerable amount of energy is consumed by this acceleration during the use phase of machine tools. However, there is still a lack of understanding of the energy consumption of spindle acceleration. Therefore, this research aims to model the spindle acceleration energy consumption of computer numerical control (CNC) lathes, and to investigate potential approaches to reduce this part of consumption. The proposed model is based on the principle of spindle motor control and includes the calculation of moment of inertia for spindle drive system. Experiments are carried out based on a CNC lathe to validate the proposed model. The approaches for reducing the spindle acceleration energy consumption were developed. On the machine level, the approaches include avoiding unnecessary stopping and restarting of the spindle, shortening the acceleration time, lightweight design, proper use and maintenance of the spindle. On the system level, a machine tool selection criterion is developed for energy saving. Results show that the energy can be reduced by 10.6% to more than 50% using these approaches, most of which are practical and easy to implement

An investigation into methods for predicting material removal energy consumption in turning

The wide use of machining processes has imposed a large pressure on environment due to energy consumption and related carbon emissions. The total power required in machining include power consumed by the machine before it starts cutting and power consumed to remove material from workpiece. Accurate prediction of energy consumption in machining is the basis for energy reduction. This paper investigates the prediction accuracy of the material removal power in turning processes, which could vary a lot due to different methods used for prediction. Three methods, namely the specific energy based method, cutting force based method and exponential function based method are considered together with model coefficients obtained from literature and experiments. The methods have been applied to a cylindrical turning of three types of workpiece materials (carbon steel, aluminum and ductile iron). Methods with model coefficients obtained from experiments could achieve a higher prediction accuracy than those from literature, which can be explained by the inability of the coefficients from literature to match the specific machining conditions. When the coefficients are obtained from literature, the prediction accuracy is largely dependent on the sources of coefficients and there is no definitive dominance of one approach over another. With model coefficients from experiments, the cutting force based model achieves the best accuracy, followed by the exponential function based method and specific energy based method. Furthermore, the power prediction methods can be used in process design stage to support energy consumption reduction of a machining process

Adapting a generic tuberculosis control operational guideline and scaling it up in China: a qualitative case study

<p>Abstract</p> <p>Background</p> <p>The TB operational guideline (the <it>deskguide</it>) is a detailed action guide for county TB doctors aiming to improve the quality of DOTS, while the China national TB policy guide is a guide to TB control that is comprehensive but lacks operational usability for frontline TB doctors. This study reports the process of deskguide adaptation, its scale-up and lessons learnt for policy implications.</p> <p>Methods</p> <p>The deskguide was translated, reviewed, and revised in a working group process. Details of the eight adaptation steps are reported here. An operational study was embedded in the adaptation process. Two comparable prefectures were chosen as pilot and control sites in each of two participating provinces. In the pilot sites, the deskguide was used with the national policy guide in routine in-service training and supervisory trips; while in the control sites, only the national policy guide was used. In-depth interviews and focus groups were conducted with 16 county TB doctors, 16 township doctors, 17 village doctors, 63 TB patients and 57 patient family members. Following piloting, the deskguide was incorporated into the national TB guidelines for county TB dispensary use.</p> <p>Results</p> <p>Qualitative research identified that the deskguide was useful in the daily practice of county TB doctors. Patients in the pilot sites had a better knowledge of TB and better treatment support compared with those in the control sites.</p> <p>Conclusion</p> <p>The adaptation process highlighted a number of general strategies to adapt generic guidelines into country specific ones: 1) local policy-makers and practitioners should have a leading role; 2) a systematic working process should be employed with capable focal persons; and 3) the guideline should be embedded within the current programmes so it is sustainable and replicable for further scale-up.</p

Evaluating the impact of decentralising tuberculosis microscopy services to rural township hospitals in gansu province, china

<p>Abstract</p> <p>Background</p> <p>In 2004, the Ministry of Health issued the policy of decentralising microscopy services (MCs) to one third of all township hospitals in China. The study was conducted in Gansu Province, a poor western one in China. Ganzhou was one county in Gansu Province. Ganzhou County was identified as a unique case of further decentralisation of tuberculosis (TB) treatment services in township hospitals. The study evaluated the impact of the MC policy on providers and patients in Gansu Province. The second objective was to assess the unique case of Ganzhou County compared with other counties in the province.</p> <p>Methods</p> <p>Both quantitative and qualitative methods were used. All 523 MCs in the province completed an institutional survey regarding their performance. Four counties were selected for in-depth investigation, where 169 TB suspects were randomly selected from the MC and county TB dispensary registers for questionnaire surveys. Informant interviews were conducted with 38 health staff at the township and county levels in the four counties.</p> <p>Results</p> <p>Gansu established MCs in 39% of its township hospitals. From January 2006 to June 2007, 8% of MCs identified more than 10 TB sputum smear positive patients while 54% did not find any. MCs identified 1546 TB sputum smear positive patients, accounting for 9% of the total in the province. The throughputs of MCs in Ganzhou County were eight times of those in other counties. Interviews identified several barriers to implement the MC policy, such as inadequate health financing, low laboratory capacity, lack of human resources, poor treatment and management capacities, and lack of supervisions from county TB dispensaries.</p> <p>Conclusion</p> <p>Microscopy centre throughputs were generally low in Gansu Province, and the contribution of MCs to TB case detection was insignificant taking account the number of MCs established. As a unique case of full decentralisation of TB service, Ganzhou County presented better results. However, standards and quality of TB care needed to be improved. The MC policy needs to be reviewed in light of evidence from this study.</p

Minimising the machining energy consumption of a machine tool by sequencing the features of a part

Increasing energy price and emission reduction requirements are new challenges faced by modernmanufacturers. A cons iderable amount of their energy consumption is attributed to the machining en-ergy consumption of machine tools (MTE), including cutting and non-cutting energy consumption (CEand NCE). The value of MTE is affected by the processing sequence of the features within a specific partbecause both the cutting and non-cutting plans vary based on different feature sequences. This articleaims to understand and characterise the MTE while machining a part. A CE model is developed to bridgethe knowledge gap, and two sub-models for specific energy consumption and actual cutting volume aredeveloped. Then, a single objective optimisation problem, minimising the MTE, is introduced. Twooptimisation approaches, Depth-First Search (DFS) and Genetic Algorithm (GA), are employed togenerate the optimal processing sequence. A case study is conducted, where five parts with 11e15features are processed on a machining centre. By comparing the experiment results of the two algo-rithms, GA is recommended for the MTE model. The accuracy of our model achieved 96.25%. 14.13% and14.00% MTE can be saved using DFS and GA, respectively. Moreover, the case study demonstrated a20.69% machining time reduction

Optimisation of cutting parameters for improving energy efficiency in machining process

Reducing the machining energy consumption (MEC) of machine tools for turning operations is significant to promote sustainable manufacturing. It has been approved that selecting optimal cutting (turning) parameters is an effective approach to reduce the cutting energy consumption (CEC) within the MEC. However, the potentiality for this approach to reduce the non-cutting energy consumption (NCEC) has not received sufficient attentions. Especially, the energy consumed for spindle rotation change (SRCE) was neglected. Thus, this article aims at developing an integrated MEC model with NCEC and SRCE considered. Then, Simulated Annealing (SA) is employed to find the optimal spindle rotation speed (SRS) and feed rate which result in the minimum MEC. A case study is conducted, where five parts with different cutting lengths are processed on a lathe. The experiment results show that SA can obtain the global optimum in a short computation time when the step sizes for SRS and feed rate are 0.1 and 0.001, respectively. The optimal solution achieves a 19.28% MEC reduction. Finally, the relation between the part length and the optimal SRS is analysed, and the consequence of MEC minimisation on machining time is discussed.acceptedVersio

A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI)

Hyperspectral data processing technique has gained increasing interests in the field of chemical and biomedical analysis. However, appropriate approaches to fusing features of hyperspectral data-cube are still lacking. In this paper, a new data fusion approach was proposed and applied to discriminate Rhizoma Atractylodis Macrocephalae (RAM) slices from different geographical origins using hyperspectral imaging. Spectral and image features were extracted from hyperspectral data in visible and near-infrared (VNIR, 435&#8722;1042 nm) and short-wave infrared (SWIR, 898&#8722;1751 nm) ranges, respectively. Effective wavelengths were extracted from pre-processed spectral data by successive projection algorithm (SPA). Meanwhile, gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were employed to extract textural variables. The fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-FuSI) was implemented to integrate those features on three fusion dimensions, i.e., VNIR and SWIR fusion, spectrum and image fusion, and all data fusion. Based on data fusion, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were utilized to establish calibration models. The results demonstrated that VNIR-SWIR-FuSI could achieve the best accuracies on both full bands (97.3%) and SPA bands (93.2%). In particular, VNIR-SWIR-FuSI on SPA bands achieved a classification accuracy of 93.2% with only 23 bands, which was significantly better than those based on spectra (80.9%) or images (79.7%). Thus it is more rapid and possible for industry applications. The current study demonstrated that hyperspectral imaging technique with data fusion holds the potential for rapid and nondestructive sorting of traditional Chinese medicines (TCMs)

Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often significant incoherencies between the existing research and real applications. This paper presents an improved method to optimize machining allowance distribution and parameters comprehensively, considering energy-saving strategy and other multi-objectives of different phases. The empirical parametric models of different machining phases were established, with the allowance distribution problem properly addressed. Based on previous analysis work of algorithm performance, non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition were chosen to obtain Pareto solutions. Algorithm performances were compared based on the efficiency of finding the Pareto fronts. Two case studies of a cylindrical turning and a face milling were carried out. Results demonstrate that the proposed method is effective in trading-off and finding precise application scopes of machining allowances and parameters used in real production. Cutting tool life and surface roughness can be greatly improved for turning. Energy consumption of rough milling can be greatly reduced to around 20% of traditional methods. The optimum algorithm of each case is also recognized. The proposed method can be easily extended to other machining scenarios and can be used as guidance of process planning for meeting various engineering demands