scholarly journals Development of a user-friendly drilling evaluation database system of CFRP

2018 ◽  
Vol 25 (6) ◽  
pp. 1205-1217
Author(s):  
Gong Dong Wang ◽  
Nan Li ◽  
Tian Peng ◽  
Ying Chi Li ◽  
Xu Hai Xiong ◽  
...  
Keyword(s):  
Classification Tree ◽  
Database System ◽  
Influential Factors ◽  
Standard Theory ◽  
Production Cycle ◽  
Drilling Process ◽  
Tree Model ◽  
Drilling Parameters ◽  
User Friendly ◽  
3D Evaluation

AbstractThe drilling process of carbon fiber reinforced plastics (CFRP) is the main secondary processing in the entire production cycle. However, there is no standard theory that could be used to estimate the hole quality and to quantify the assessment. Therefore, in this article, several influential factors of drilling quality, such as drilling parameters and materials, were utilized to establish the classification tree model. A comprehensive evaluation model [three-dimensional (3D) evaluation factor] was presented to measure and quantify the quality of the hole. Then a series of experiments were designed based on existing conditions. Further, a mathematical software (Origin) was used to fit and analyze the relationships of the drilling parameters and the 3D evaluation factor. Then a user-friendly drilling database system of CFRP was established based on the historical data of drilling experiments and the fitting function. In addition, in order to contrast the optimization results of the fitting function, the drilling experiments have been conducted, and the results have verified that the drilling database system could optimally predict the drilling schemes with a minor error.

scholarly journals Factors Affecting Maintenance Probability and Resurfacing Thickness Based on the Pavement Management System

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Hongmei Li ◽  
Jiwang Jiang ◽  
Fujian Ni
Keyword(s):  
Management System ◽  
Classification Tree ◽  
Surface Friction ◽  
Middle Layer ◽  
Severity Index ◽  
Influential Factors ◽  
Pavement Management ◽  
Key Factors ◽  
Tree Model ◽  
Pavement Management System

In order to investigate the key factors and analyze their effects on maintenance and rehabilitation (M&R) strategies, data for 2495 pavement sections were collected from the pavement management system (PMS), including pavement performance data, traffic data, material property data, and M&R record data. Logistic regression was first employed to explore the influential factors on maintenance probability. Afterward, the classification tree model was established to find out the key factors on resurfacing thickness. Results showed that road sections with higher IRI, rutting depth (RD), deterioration rate of surface friction coefficient (DRSFC), pavement patching ratio (PPR), and transverse cracking severity index (TCSI) before treatment had significantly higher maintenance probability, which could be quantified by the developed logistic model. Moreover, treatments implemented on bridge decks tended to have greater resurfacing thickness. For pavement M&R projects, with the tensile strength ratio (TSR) of top layer materials higher than 88.7% and pretreatment SFC higher than 49, the resurfacing thickness would be thinner. For bridge M&R projects, middle layer TSR higher than 88.3% led to thinner overlays, and much thinner resurfacing thickness can be observed if pretreatment RD was less than 8.72 mm. When middle layer TSR was lower than 88.3% and pretreatment IRI was higher than 2.383 m/km with larger AESAL, the resurfacing thickness would probably be the thickest. The two models built in this paper provided probabilistic estimation of maintenance probability and explored key factors together with their critical split points for resurfacing thickness, which could be regarded as an alternative decision-making tool for pavement engineers.

scholarly journals Optimum combination of machining parameters during drilling of Aluminium 7075 alloys using Grey based Taguchi approach

2020 ◽  
Vol 4 (3) ◽  
pp. 227-238
Author(s):  
Reddy Sreenivasulu ◽  
Ch. Srinivasa Rao
Keyword(s):  
Value Added ◽  
Influential Factors ◽  
Optimal Combination ◽  
Performance Characteristics ◽  
Burr Formation ◽  
Production Cycle ◽  
Drilling Process ◽  
Machining Parameters ◽  
Burr Size ◽  
Grey Relational

From ancient days to till today manufacturing industries, especially making of holes on the parts during drilling process for precision assembling of parts facing problems with burr formation. Drilling operation is one of the finishing operation in the production cycle, removing of burrs during drilling process is a time consuming and non-value added process to the manufacturing sector. So reducing the size of burrs is the main aim of the present study. In the present work, optimization of burr size is considered during drilling of aluminium 7075 alloy. From Grey relational grades of responses selected optimal combination of parameters to attain multiple performance characteristics of responses with a corresponding higher grey relational grade. For identifying the most significant input parameters that influence the output responses ANOVA is conducted. Finally, observations reveals that feed rate, point and clearance angles are the most influential factors on burr size and also experimental results divulge that the lower the thrust force causes to decrease the burr height. The proposed approach is helpful to the budding entrepreneurs in the related areas to select optimal combination of drilling parameters to attain multiple performance characteristics of responses especially in burr size to prevent the post finishing operations.

scholarly journals A Force Sensorless Method for CFRP/Ti Stack Interface Detection during Robotic Orbital Drilling Operations

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Fang ◽  
Ze-Min Pan ◽  
Bing Han ◽  
Shao-Hua Fei ◽  
Guan-Hua Xu ◽  
...  
Keyword(s):  
Cutting Force ◽  
Thrust Force ◽  
Cutting Parameters ◽  
Force Model ◽  
Drilling Process ◽  
Reinforced Plastics ◽  
Orbital Drilling ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Machining Properties

Drilling carbon fiber reinforced plastics and titanium (CFRP/Ti) stacks is one of the most important activities in aircraft assembly. It is favorable to use different drilling parameters for each layer due to their dissimilar machining properties. However, large aircraft parts with changing profiles lead to variation of thickness along the profiles, which makes it challenging to adapt the cutting parameters for different materials being drilled. This paper proposes a force sensorless method based on cutting force observer for monitoring the thrust force and identifying the drilling material during the drilling process. The cutting force observer, which is the combination of an adaptive disturbance observer and friction force model, is used to estimate the thrust force. An in-process algorithm is developed to monitor the variation of the thrust force for detecting the stack interface between the CFRP and titanium materials. Robotic orbital drilling experiments have been conducted on CFRP/Ti stacks. The estimate error of the cutting force observer was less than 13%, and the stack interface was detected in 0.25 s (or 0.05 mm) before or after the tool transited it. The results show that the proposed method can successfully detect the CFRP/Ti stack interface for the cutting parameters adaptation.

Collaborative evaluation and management of students’ health-related physical fitness: applications of cluster analysis and the classification tree

2012 ◽  
Vol 24 (2) ◽  
Author(s):  
Jou-An Chen ◽  
Chi-Chuan Shih ◽  
Pay-Fan Lin ◽  
Jin-Jong Chen ◽  
Kuan-Chia Lin
Keyword(s):  
Cluster Analysis ◽  
Physical Fitness ◽  
Classification Tree ◽  
Metabolic Risk ◽  
Tree Model ◽  
School Year ◽  
Classification Tree Model ◽  
Health Related ◽  
Risk Approach ◽  
Collaborative Evaluation

Abstract Health-related physical fitness has decreased with age; this is od immense concern to adolescents. School-based health intervention programs can be classified as either population-wide or high-risk approach. Although the population-wide and risk-based approaches adopt different healthcare angles, they all need to focus resources on risk evaluation. In this paper, we describe an exploratory application of cluster analysis and the tree model to collaborative evaluation of students’ health- related physical fitness from a high school sample in Taiwan (n=742). Cluster analysis show that physical fitness can be divided into relatively good, moderate and poor subgroups. There are significant differences in biochemical measurements among these three groups. For the tree model, we used 2004 school-year students as an experimental group and 2005 school-year students as a validation group. The results indicate that if sit-and-reach is shorter than 33 cm, BMI is >25.46 kg/m2, and 1600 m run/walk is >534 s, the predicted probability for the number of metabolic risk factors ≥2 is 100% and the population is 41, both results are the highest. From the risk-based healthcare viewpoint, the cluster analysis can sort out students’ physical fitness data in a short time and then narrow down the scope to recognize the subgroups. A classification tree model specifically shows the discrimination paths between the measurements of physical fitness for metabolic risk and would be helpful for self-management or proper healthcare education targeting different groups. Applying both methods to specific adolescents’ health issues could provide different angles in planning health promotion projects.

The Use of a Sensor Modules System for Measuring Drilling Parameters in a Bit, Significantly Reduces the Construction Time of Wells in Eastern Siberia

2021 ◽  
Author(s):  
Andrey Alexandrovich Rebrikov ◽  
Anton Anatolyevich Koschenkov ◽  
Anastasiya Gennadievna Rakina ◽  
Igor Dmitrievich Kortunov ◽  
Nikita Vladimirovich Koshelev ◽  
...  
Keyword(s):  
Oil And Gas ◽  
New Technologies ◽  
Field Tests ◽  
Depth Of Cut ◽  
Drilling Process ◽  
Eastern Siberia ◽  
Construction Time ◽  
Stage Of Development ◽  
Drilling Parameters ◽  
Pdc Bit

Abstract Currently, production and exploration drilling has entered a stage of development where one of the highest priority goals is to reduce the time for well construction with new technologies and innovations. One of the key components in this aspect is the utilizing of the latest achievements in the design and manufacture of rock cutting tools – drill bits. This article presents some new ideas on methods for identifying different types of vibrations when drilling with PDC bits using a system of sensors installed directly into the bit itself. In the oil and gas fields of Eastern Siberia, one of the main reasons for ineffective drilling with PDC bits are vibrations, which lead to premature wear of the cutting structure of the bit and the achievement of low ROPs in the dolomite and dolerite intervals. For efficient drilling of wells of various trajectories with a bottom hole assembly (BHA), including a downhole motor (PDM) and a PDC bit, special attention is paid to control of the bit by limiting the depth of cut, as well as the level of vibrations that occur during drilling process. Often, the existing complex of surface and BHA equipment fails to identify vibrations that occur directly on the bit, as well as to establish the true cause of their occurrence. Therefore, as an innovative solution to this problem, a system of sensors installed directly into the bit itself is proposed. The use of such a system makes it possible to determine the drilling parameters, differentiated depending on the lithological properties of rocks, leading to an increase in vibration impact. Together with the Operators, tests have been successfully carried out, which have proven the effectiveness of the application of this technology. The data obtained during the field tests made it possible to determine the type and source of vibration very accurately during drilling. In turn, this made it possible to precisely adjust the drilling parameters according to the drilled rocks, to draw up a detailed road map of effective drilling in a specific interval. Correction of drilling parameters based on the analysis of data obtained from sensors installed in the bit made it possible to reduce the resulting wear of the PDC bit cutting structure and, if necessary, make changes to the bit design to improve the technical and economic indicators. Thus, the use of a system of sensors for measuring the drilling parameters in a bit ensured the dynamic stability of the entire BHA at the bottomhole when drilling in rocks of different hardness, significantly reduced the wear of the drilling tools and qualitatively improved the drilling performance.

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

Micro-Testing While Drilling for Rate of Penetration Optimization: Experiments and Simulations

2021 ◽  
pp. 1-49
Author(s):  
Magnus Nystad ◽  
Bernt Aadnoy ◽  
Alexey Pavlov
Keyword(s):  
Optimization Method ◽  
Extremum Seeking ◽  
Constraint Handling ◽  
Drilling Process ◽  
Test Results ◽  
Rate Of Penetration ◽  
Drilling Rig ◽  
Model Free ◽  
Drilling Parameters ◽  
Weight On Bit

Abstract The Rate of Penetration (ROP) is one of the key parameters related to the efficiency of the drilling process. Within the confines of operational limits, the drilling parameters affecting the ROP should be optimized to drill more efficiently and safely, to reduce the overall cost of constructing the well. In this study, a data-driven optimization method called Extremum Seeking (ES) is employed to automatically find and maintain the optimal Weight on Bit (WOB) which maximizes the ROP. The ES algorithm is a model-free method which gathers information about the current downhole conditions by automatically performing small tests with the WOB and executing optimization actions based on the test results. In this paper, this optimization method is augmented with a combination of a predictive and a reactive constraint handling technique to adhere to operational limitations. These methods of constraint handling within ES application to drilling are demonstrated for a maximal limit imposed on the surface torque, but the methods are generic and can be applied on various drilling parameters. The proposed optimization scheme has been tested with experiments on a downscaled drilling rig and simulations on a high-fidelity drilling simulator of a full-scale drilling operation. The experiments and simulations show the method's ability to steer the system to the optimum and to handle constraints and noisy data, resulting in safe and efficient drilling at high ROP.

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