scholarly journals Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4761
Author(s):  
Ardamanbir Singh Sidhu ◽  
Sehijpal Singh ◽  
Raman Kumar ◽  
Danil Yurievich Pimenov ◽  
Khaled Giasin
Keyword(s):  
Sustainable Manufacturing ◽  
Absolute Error ◽  
Active Power ◽  
Drilling Process ◽  
Regression Equations ◽  
Industrial Case Study ◽  
Equal Importance ◽  
Order Preference ◽  
Electric Parameters ◽  
Machining Operations

Increasing the energy efficiency of machining operations can contribute to more sustainable manufacturing. Therefore, there is a necessity to investigate, evaluate, and optimize the energy consumed during machining operations. The research highlights a method employed to prioritize the most energy-intensive machining operation and highlights the significance of electric parameters as predictors in power estimation of machining operations. Multi regression modeling with standardized regression weights was used to identify significant power quality predictors for active power evaluation for machining operations. The absolute error and the relative error both decreased when the active power was measured by the power analyzer for each of the identified machining operations, compared to the standard power equation and that obtained from the modeled regression equations. Furthermore, to determine energy-intensive machining operation, a hybrid decision-making technique based on TOPSIS (a technique for order preference by similarity to ideal solution) and DoM (degree of membership) was utilized. Allocation of weights to energy responses was carried out using three methods, i.e., equal importance, entropy weights, and the AHP (analytical hierarchy process). Results revealed that a drilling process carried out on material ST 52.3 is energy-intensive. This accentuates the significance of electric parameters in the assessment of active power during machining operations.

scholarly journals P112 Prediction of free Ceftriaxone concentration in children: disease and maturation do matter

2019 ◽  
Vol 104 (6) ◽  
pp. e64.2-e64
Author(s):  
H-Y Shi ◽  
X Huang ◽  
Q Li ◽  
Wu Y-E ◽  
MW Khan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mean Absolute Error ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Absolute Error ◽  
Regression Equations ◽  
Free Concentration ◽  
In Vivo Binding ◽  
Mean Prediction Error

BackgroundTo evaluate the predictive ability of the existing formula to measure free ceftriaxone levels in children, and optimize the formula by adding disease and maturation factors.MethodsFifty children receiving ceftriaxone were evaluated, and the predictive performance of the different equations were assessed by mean absolute error (MAE), mean prediction error (MPE) and linear regression of predicted vs. actual free levels.ResultsThe average free ceftriaxone concentration was 2.11 ± 9.51µg/ml. The predicted free concentration was 1.15 ± 4.39µg/ml with the in vivo binding equation, which increased to 1.58 ± 7.73µg/ml and 2.01 ± 9.53µg/ml when adjusted for age (disease adapted equation), and age and albumin (disease-maturation equation) respectively. The average MAE values were 0.48 (in vivo banding equation), 0.34 (disease adapted equation) and 0.41 (disease maturation equation). The average MPE values were -0.41 (in vivo binding equation), 0.14 (disease adapted equation) and 0.09 (disease maturation equation). The respective linear regression equations and coefficients were y=1.8647x+1.0731(R2=0.7398), y=1.1455x+0.8414(R2=0.8674), and y=0.9664x(R2=0.8641) for the in vivo binding, disease adapted and disease maturation equations respectively.ConclusionCompared to the in vivo binding equation, the disease adapted and disease maturation equations showed lower MAE and MPE values, and the latter showed the lowest MPE value. In addition, the slope of the disease maturation equation was closer to 1 compared to the other two. Therefore, the optimized disease maturation equation should be used to measure free ceftriaxone levels in children.Disclosure(s)Nothing to disclose.

Experimental Study on Optimizing the Effect of Drilling Parameters on Roundness Error of Holes of Aluminum 7075 Using Response Surface Methodology

2012 ◽  
Vol 184-185 ◽  
pp. 981-987
Author(s):  
Setayesh Hakak Zargar ◽  
Vahid Tahmasbi ◽  
K. Besharati ◽  
Mohammad Farzami
Keyword(s):  
Experimental Study ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Surface Quality ◽  
Cutting Speed ◽  
Drilling Process ◽  
Regression Equations ◽  
Roundness Error ◽  
Drill Diameter ◽  
Aluminum 7075

In the present work, an experimental study has been made to optimize the drilling process parameters. Response surface methodology based on central composite design (CCD) has been used to study and analyze the experiments. Twist drill diameter, cutting speed and feed of drilling were chosen as variables to study the process performance for the responses of the hole surface quality (Ra) and the roundness error on aluminum 7075. Experiments were performed on a newly designed experimental setup developed in the laboratory. The results identified the most important parameters to maximize the hole surface quality and minimize roundness error. Finally, regression equations were obtained to predict the responses for different values of variables.

scholarly journals An Investigation of the Effect of Parameters and Chip Slenderness Ratio on Drilling Process Quality of AISI 1050 Steel

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zülküf Demir ◽  
Rifat Yakut
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Thrust Force ◽  
Slenderness Ratio ◽  
Chip Morphology ◽  
Drilling Process ◽  
Steel Alloy ◽  
Drilling Operations ◽  
Machining Operations

The chip slenderness ratio is a vital parameter in theoretical and applicable machining operations. In predrilled drilling operations of AISI 1050 steel alloy, HSS drills were employed, and the effect of the selected parameters on the chip slenderness ratio and also the effect of the chip slenderness ratio on the thrust force, surface roughness, drilled hole delamination, tool wear, and chip morphology were investigated. The major parameters, influential on the chip slenderness ratio, were feed rate and point angle, while spindle speed was too small to be negligible. With increasing the chip slenderness ratio, the thrust force and the tool wear decreased, which resulted in appropriate chip morphology, but there were increases in surface roughness. However, the chip slenderness ratio had no effect on the drilled hole delamination.

Predicting air permeability of multifilament polyester woven fabrics using developed fuzzy logic model

2020 ◽  
pp. 004051752094254
Author(s):  
Maher Alsayed ◽  
Halil İbrahim Çelik ◽  
Hatice Kübra Kaynak
Keyword(s):  
Fuzzy Logic ◽  
Regression Analysis ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Air Permeability ◽  
Woven Fabrics ◽  
Logic Model ◽  
Woven Fabric ◽  
Regression Equations ◽  
Fuzzy Logic Model

The number of filaments in yarn cross-section, weave density, and weave type are considered the most important factors that affect the property of air permeability of multifilament woven fabrics. Microfilament yarns significantly affect the air permeability property of this type of fabric because of the low porosity between the filaments. This study deals with the development of a fuzzy logic model for predicting the air permeability of multifilament polyester woven fabrics produced from conventional and microfilament yarns. The polyester multifilament yarns used in this study were produced with three different microfilament fineness and two conventional filament fineness levels. The woven fabric samples used in this study were obtained in three weave types: plain, twill, and satin, and with five different weave densities. In accordance with the experimental test results, both regression analysis and fuzzy logic system were built. The air permeability results generated from the developed fuzzy model and the regression equations were compared with the experimental values. Satisfactory and accurate prediction results were obtained with the developed fuzzy logic model. The mean absolute error of the fuzzy model and regression analysis were found to be 2.32%, 12.59%, respectively. Therefore, it was confirmed that the fuzzy model was superior in predicting air permeability.

Experimental study on drilling characteristics of the hybrid sisal–jute fibre epoxy composites

2021 ◽  
pp. 251659842110154
Author(s):  
Harpreet Singh ◽  
Bhishm Dewangan ◽  
P. K. Jain
Keyword(s):  
Surface Roughness ◽  
Research Work ◽  
Experimental Studies ◽  
Microscopic Analysis ◽  
Natural Fibre ◽  
Drilling Process ◽  
Twist Drill ◽  
Fibre Composites ◽  
Geometry Configuration ◽  
Machining Operations

Natural fibre composites have received worldwide attention due to their good mechanical properties, lightweight and low density. Due to these advantages, the natural fibre composites have been used in various engineering applications. Drilling is one of the most frequent machining operations performed on hybrid sisal–jute polymer composites, to assemble the numerous structural components by using mechanical joining process. Furthermore, the machining of fibre reinforced composite material has attracted several researchers because of its non-homogeneous and anisotropic structure. The present research work concerns with the experimental studies on the drilling process of hybrid sisal–jute epoxy composite, using three different types of drill geometry (twist drill, step drill and core drill). The significance of the current work aims to reveal the effect of drill geometry configuration and drilling parameters in terms of drilling-induced force and damages (delamination and surface roughness) for the drilling of hybrid natural fibre composites. Drilling forces, drilling-induced damages and hole quality attributes were experimentally investigated for different drill geometries. The delamination and surface roughness type damages are revealed by microscopic analysis with the help of scanning electron microscope (SEM). The results show that twist drill is best suited for the hole- and force-induced damages.

scholarly journals An improved rapid power and energy prediction method of drilling process for sustainable manufacturing

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Shun Jia ◽  
Na Zhang ◽  
Jingxiang Lv ◽  
Wei Cai ◽  
Shuowei Bai ◽  
...  
Keyword(s):  
Sustainable Manufacturing ◽  
Prediction Method ◽  
Drilling Process ◽  
Energy Prediction ◽  
Power And Energy

scholarly journals Demirjian’s Eight Teeth Method for Dental age Estimation in Nepalese Population

2021 ◽  
Vol 18 (4) ◽  
pp. 686-691
Author(s):  
Nuwadatta Subedi ◽  
Umesh Parajuli ◽  
Ishwari Sharma Paudel ◽  
Mukesh Mallik
Keyword(s):  
Age Estimation ◽  
Developmental Stages ◽  
Tooth Development ◽  
Absolute Error ◽  
Original Method ◽  
Regression Equations ◽  
Dental Age ◽  
Dental Age Estimation ◽  
Maturity Score ◽  
Statistical Program

Background: Demirjian’s method is widely used method for dental age estimation. This study was conducted with objectives of applying Demirjian’s 8 teeth method to estimate age in Nepalese Population and to determine Nepal-specific formulas.Methods: We had used the Orthopantomographs of Nepalese people of age above five and below 23 years. The radiographs were compared to the ‘Tooth Development Chart’ and each tooth studied was assigned with any one of the 10 developmental stages using Demirijian’s 8 teeth method and total maturity scores determined. Formulas were derived using regression analysis, wherein the total maturity score obtained for each individual was considered as the independent variable and the corresponding age as the dependent variable in the STATA 15.1 statistical program. Results: There was underestimation of age in both the sexes by the original method. Regression equations were derived for males and females separately for age five to 18 years and again after adding cases up to 23 years. The estimation was better for males up to 18 years [R2=0.94, Mean Absolute Error (MAE) 0.747 years and SD 0.644] than for females up to 18 years (R2 = 0.89, MAE 0.886 years and SD 0.925). The estimation was better for up to 18 years than for up to 23 years in both sexes.Conclusions: Demirijian’s 8 teeth method underestimated age in the study population and thus population specific equations based on the method are better for dental age estimation. The age estimation utilizing the equations from Nepalese population has given acceptable results.Keywords: Age estimation; demirijian’s method; dental age estimation; forensic age estimation; tooth development chart

FE Study of the Chips Morphology when Drilling Process

2015 ◽  
Vol 798 ◽  
pp. 470-474 ◽  
Author(s):  
Nawel Glaa ◽  
Kamel Mehdi ◽  
Moez Ben Jaber
Keyword(s):  
Finite Element ◽  
Finite Element Modeling ◽  
Cutting Parameters ◽  
Drilling Process ◽  
Finite Element Program ◽  
Drilling Operation ◽  
Element Modeling ◽  
Element Program ◽  
The Cost ◽  
Machining Operations

The finite element modeling allows manufacturers to reduce the cost of machining operations. During a drilling operation, the chips morphology their sizes and thicknesses have a great effect on the process, whatever the material to be machined. One approach to a 3D simulation of a drilling process with the finite element program Abaqus/Explicit is displayed. We studied the morphology of chips during the drilling process, the influence of cutting parameters on their shape, size and clear velocity. This study allows us to optimize the conditions and cutting parameters for a smooth process.

scholarly journals Comparison of neural networks and neuro-fuzzy computing techniques for prediction of peak breach outflow

2016 ◽  
Vol 18 (4) ◽  
pp. 724-740 ◽  
Author(s):  
Hasan G. Elmazoghi ◽  
Vail Karakale (Waiel Mowrtage) ◽  
Lubna S. Bentaher
Keyword(s):  
Fuzzy Inference ◽  
Gene Expression Programming ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Data Set ◽  
Inference System ◽  
Embankment Dams ◽  
Small Dams ◽  
Neuro Fuzzy

Accurate prediction of peak outflows from breached embankment dams is a key parameter in dam risk assessment. In this study, efficient models were developed to predict peak breach outflows utilizing artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Historical data from 93 embankment dam failures were used to train and evaluate the applicability of these models. Two scenarios were applied with each model by either considering the whole data set without classification or classifying the set into small dams (48 dams) and large dams (45 dams). In this way, nine models were developed and their results were compared to each other and to the results of the best available regression equations and recent gene expression programming. Among the different models, the ANFIS model of the first scenario exhibited better performance based on its higher efficiency (E = 0.98), higher coefficient of determination (R2 = 0.98) and lower mean absolute error (MAE = 840.9). Moreover, models based on classified data enhanced the prediction of peak outflows particularly for small dams. Finally, this study indicated the potential of the developed ANFIS and ANN models to be used as predictive tools of peak outflow rates of embankment dams.

Prediction of protein–carbohydrate complex binding affinity using structural features

2020 ◽  
Author(s):  
N R Siva Shanmugam ◽  
J Jino Blessy ◽  
K Veluraja ◽  
M Michael Gromiha
Keyword(s):  
Binding Affinity ◽  
Web Server ◽  
Absolute Error ◽  
Accessible Surface Area ◽  
Structural Features ◽  
Regression Equations ◽  
Recognition Mechanism ◽  
Jackknife Test ◽  
Accessible Surface ◽  
The Web

Abstract Protein–carbohydrate interactions play a major role in several cellular and biological processes. Elucidating the factors influencing the binding affinity of protein–carbohydrate complexes and predicting their free energy of binding provide deep insights for understanding the recognition mechanism. In this work, we have collected the experimental binding affinity data for a set of 389 protein–carbohydrate complexes and derived several structure-based features such as contact potentials, interaction energy, number of binding residues and contacts between different types of atoms. Our analysis on the relationship between binding affinity and structural features revealed that the important factors depend on the type of the complex based on number of carbohydrate and protein chains. Specifically, binding site residues, accessible surface area, interactions between various atoms and energy contributions are important to understand the binding affinity. Further, we have developed multiple regression equations for predicting the binding affinity of protein–carbohydrate complexes belonging to six categories of protein–carbohydrate complexes. Our method showed an average correlation and mean absolute error of 0.731 and 1.149 kcal/mol, respectively, between experimental and predicted binding affinities on a jackknife test. We have developed a web server PCA-Pred, Protein–Carbohydrate Affinity Predictor, for predicting the binding affinity of protein–carbohydrate complexes. The web server is freely accessible at https://web.iitm.ac.in/bioinfo2/pcapred/. The web server is implemented using HTML and Python and supports recent versions of major browsers such as Chrome, Firefox, IE10 and Opera.

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