A Contribution to the Thermal Field Evaluation at the Tool-Part Interface for the Optimization of Machining Conditions

In this study, an experimental measurement methodology is implemented that allows obtaining consistent temperature data during the turning operation of semi-hard C20 steel using SNMG carbide insert, allowing us to have better control at the tool-part interface. The interactions of the phenomena influencing the cut led our choices on the development of a correlation model for the analysis and prediction of the relationships between the machining parameters by measurement of the temperature. The measurement procedure implemented for the temperature estimate is based on the use of an FLIR A325sc type infrared camera mounted and protected by a device on the machine tool. The Taguchi method was chosen to find the relationships between the input factors (cutting speed (Vc), feed rate (a), depth of cut (p)), and the output factor (temperature (T)). In the future, we will develop a numerical validation model to simulate the machining process in order to predict temperatures

The added water control system based on neural network model and double parameter corrected for loosening and conditioning cylinder

In order to improve the stability of outlet moisture in the of loosening and conditioning cylinder, we concentrate on loosening and conditioning cylinder, and design the prediction model of the added water volume based on neural network technology and the double parameter corrected control system of material balance and moisture deviation for loosening and conditioning cylinder by using historical production data. Taking the set value of inlet and outlet moisture of the loosening and conditioning cylinder as the input factor and the added water volume as the output factor, the prediction model of the added water volume was to predict the total volume of the added water. When there was a big deviation between the actual value and the set value of outlet moisture, the double parameter corrected control system of material balance and moisture deviation was used to correct the deviation, so as to improve the outlet moisture stability and the control accuracy of the added water volume. Using Cigarette brand “diamond (hard-case Yingbin)” produced by Zhangjiakou cigarette factory to built model and analysis, the results showed that after the improvement, the standard deviation of output moisture of loosening and conditioning cylinder reduced by 0.27%, and the control stability and the control level of production process were improved.

Relative output factors of different collimation systems in truebeam STx medical linear accelerator

The IAEA TRS483 and TRS398 Code of Practices (CoP) were used to calculate relative output factors for small photon beams of 6X, 6XFFF energies shaped by High Definition Multileaf Collimator (HDMLC), jaws and cones mounted on TrueBeam STx medical linear accelerator (Varian Medical Systems), respectively. A comparison between these results were made. The results show a large discrepancy in relative output factor curves found among different collimation systems of the same equivalent field sizes and between the CoPs. Therefore, the specific beam modelling in treatment planning system for each type of the collimation system to be used for small fields maybe required for better computational accuracy.

Determining the Shear Capacity of Steel Beams with Corrugated Webs by Using Optimised Regression Learner Techniques

The use of corrugated webs increases web shear stability and eliminates the need for transverse stiffeners in steel beams. Optimised regression learner techniques (ORLTs) are rarely used for calculating shear capacity in steel beam research. This study proposes a new approach for calculating the maximum shear capacity of steel beams with trapezoidal corrugated webs (SBCWs) by using ORLTs. A new shear model is proposed using ORLTs in accordance with plate buckling theory and previously developed formulas for predicting the shear strength of SBCWs. The proposed ORLT models are implemented using the regression learner toolbox of MATLAB software (2020b). The available data of more than 125 test results from different specimens prepared by previous researchers are used to create the model. In this study, web geometry and relevant web steel grades determine the shear capacity of SBCWs. Four regression methods are adopted. Results are compared with those of an artificial neural network model. The model output factor represents the ratio of the web vertical shear stress to the normalised shear stress. Shear capacity can be estimated on the basis of the resulting factor from the model. The proposed model is verified using two methods. In the first method, a series of tests are performed by the authors. In the second method, the results of the model are compared with the shear values obtained experimentally by other researchers. On the basis of the test results of previous studies and the current work, the proposed model provides an acceptable degree of accuracy for predicting the shear capacity of SBCWs. The results obtained using Gaussian process regression are the most appropriate because its recoded mean square error is 0.07%. The proposed model can predict the shear capacity of SBCWs with an acceptable percentage of error. The recoded percentage of error is less than 5% for 93% of the total specimens. By contrast, the maximum differential obtained is ±10%, which is recorded for 3 out of 125 specimens.

A Novel Groundwater Resource Forecasting Technique for Cultivation Utilizing Wireless Sensor Network (WSN) and Machine Learning (ML) Model

Groundwater is almost essential part of cultivation process, particularly in parched regions. The prophecy of groundwater intensity is crucial for understanding groundwater availability and provide systematic assistance for the cultivator that the stable convention of groundwater resource. A proposed efficient technique (ET) of forecasting the groundwater level (WFC) methodology was recognized in this study to precisely forecast groundwater intensity for cultivation in the parched regions of south India. Pumping facility, recharge capability of bore wells, exposure of cultivation area and weather conditions are used as key parameters, whereas groundwater level was considered as an output factor. The lack of systematic assistance about the groundwater availability also causes a decrease in the farmer’s emotional stability and thereby affects their plan of enlightening the right crops and craft financial hardship. The proposed technique can be a new method for forecasting groundwater resource. The analytical and prediction reports of the proposed method will assist the farmers about the accessibility of groundwater resource and lead them to plan the cultivation of right crops and prevent from economic defeat.

Enhance dynamic wedge verification by using electronic portal imaging device

Objectives The purpose of this study was to verify the 80% enhanced dynamic wedge (EDW) beam profile using an electronic portal imaging device (EPID). Methods This study investigated symmetric and asymmetric field sizes using a 6 MV photon beam. Verification of the wedge output factor with an 80% beam profile was performed by comparing EPID measurements and treatment planning systems (TPS) calculations in both symmetric and asymmetric field sizes at different wedge angles (15, 30, 45, and 60 degrees). Results For the symmetric field size, the average difference between the measured and calculated beam profile was less than 2% (range 0.57-1.12%). For the asymmetric field size, the difference was also less than 2% (range 0.3-0.52%). Conclusion This study indicates that EPID can be used to verify the 80% enhanced dynamic wedge beam profile at different field sizes and wedge angles. The difference in beam profiles was less than 2% which is in accordance with AAPM TG no.142 recommendations.

Uncertainties in the measurement of relative doses in radiotherapy

Both the measurement of the dose and the measurement of its distribution, like any other measurements, are subject to measurement uncertainties. These uncertainties affect all dose calculations and dose distributions in a patient’s body during treatment planning in radiotherapy. Measurement uncertainty is not a medical physicist’s error, but an inevitable element of their work. Planning the dose distribution in a patient’s body, we often try to reduce it in the volume of critical organs (OaR - Organ at Risk) or increase the minimum dose in the PTV region by a few percent. It is believed that the measurement uncertainty should be taken into account in these calculations at the stage of treatment planning. The paper presents the method of calculating the measurement uncertainty for different physical quantities in radiotherapy as percentage depth dose, profile function and output factor, due to the fact that these quantities have a particular impact on the calculated dose distributions in a patient’s body. The uncertainties that must be taken into account in planning treatment the planned dose per fraction and real in PTV, maybe different up to 4%.

Marginal Trade-Offs for Improved Agro-Ecological Efficiency Using Data Envelopment Analysis

Today’s agricultural management decisions impact food security and sustainable ecosystems, even when operating with back-to-basic operations. In such endeavors, policymakers usually need a quantitative tool, such as trade-offs margins, to effectively adjust resource consumption or production. This paper applies the weighted slack-based measurement (SBM-DEA) program to 136 developing countries’ agricultural performance. First, it finds the current agricultural efficiency and then makes marginal trade-offs on desirable-output variables (such as crop yield and forest area) to see the effective changes in undesirable-output (such as methane and nitrous oxide emissions). The results show that choosing effective marginal trade-offs does not deteriorate the relative efficiency of the decision-making units (DMUs) below the efficient frontier line. Thus, such a method enables the decision-makers to determine the best marginal trade-off points to reach the optimal efficiencies and decide which output factor needs special brainstorming to design effective policy.

EVALUATION OF REGIONAL DEVELOPMENT WITH BANKING AND FINANCE DATA

This research aims to measure the change of provincial development levels of Turkey’s 81 provinces through banking sector data. The applied methodology is the Malmquist Total Factor Productivity Index (MTFPI). We perform the analysis based on two models. The main difference between models relies on handling the factor associated with ‘loans’. The first approach sees the loans as an output factor, which is based on the idea that loans indicate investment and therefore development. In the second model, we take the loans as an input factor, which represents the idea that loans indicate debt and therefore they should be minimized. We evaluate the findings with respect to geographical classification and the province development index.