Investigating Wear and Predicting Lifetime of the Roller Cavities for the Net-shape Blade Rolling

2021 ◽  
Author(s):  
Qichao Jin ◽  
Wenhu Wang ◽  
Ruisong Jiang ◽  
lei Guo
Keyword(s):  
Prediction Model ◽  
Rolling Process ◽  
Contact Load ◽  
Lifetime Prediction ◽  
Wear Model ◽  
Service Lifetime ◽  
Local Contact ◽  
Analysis Experiment ◽  
Accumulation Effect ◽  
The Impact

Abstract The die undergoing severe loads which induces inevitably wear in the pressure forming process, and the wear of die arouses obsessions about the die’s service lifetime. In order to obtain the geometrical shape transformation caused by wear and predict the service lifetime for a pair of rollers in net-shape blade rolling process, this paper quantified the distributions and evolutions of the local wear over roller cavities based on the local contact load responses, and predicted the lifetime which related to wear by a mathematical models. Firstly, the net-shape blade rolling process and the local contact load responses were summarized. Then, an improved wear model was provided based on the Archard formula, and the impact factors of the model was standardized by a regression analysis experiment. The transient wear distributions and evolutions over the roller cavities were enumerated, and the wear distribution for one rolling cycle was calculated based on wear accumulation effect. Finally, a lifetime prediction model was proposed to predict the service lifetime of the rollers according to the wear accumulation effect, and an experimental verification was carried out to validate the model. The results showed that the wear model and lifetime prediction model can be used for investigating the wear and lifetime prediction of the roller cavities for the net-shape blade rolling process.

Geometry Design and Contact Ratio Analysis for Circular Arc Parallel-Axis Helical Gears

2016 ◽  
Author(s):  
Z. Chen ◽  
B. Lei ◽  
Q. Zhao
Keyword(s):  
Rolling Process ◽  
Helical Gear ◽  
Space Curve ◽  
Impact Factors ◽  
Contact Ratio ◽  
Circular Arc ◽  
Practical Applications ◽  
Geometry Design ◽  
Gear Mechanism ◽  
The Impact

Based on space curve meshing theory, in this paper, we present a novel geometric design of a circular arc helical gear mechanism for parallel transmission with convex-concave circular arc profiles. The parameter equations describing the contact curves for both the driving gear and the driven gear were deduced from the space curve meshing equations, and parameter equations for calculating the convex-concave circular arc profiles were established both for internal meshing and external meshing. Furthermore, a formula for the contact ratio was deduced, and the impact factors influencing the contact ratio are discussed. Using the deduced equations, several numerical examples were considered to validate the contact ratio equation. The circular arc helical gear mechanism investigated in this study showed a high gear transmission performance when considering practical applications, such as a pure rolling process, a high contact ratio, and a large comprehensive strength.

A First Step towards a Tribological Approach to Investigate Cutting Tool Wear

2014 ◽  
Vol 611-612 ◽  
pp. 452-459 ◽  
Author(s):  
Giovenco Axel ◽  
Frédéric Valiorgue ◽  
Cédric Courbon ◽  
Joël Rech ◽  
Ugo Masciantonio
Keyword(s):  
Tool Wear ◽  
Cutting Tool ◽  
304L Stainless Steel ◽  
Fe Modelling ◽  
Wear Model ◽  
Cutting Tool Wear ◽  
The Will ◽  
Preliminary Study ◽  
The Impact ◽  
Macroscopic Parameters

The present work is motivated by the will to improve Finite Element (FE) Modelling of cutting tool wear. As a first step, the characterisation of wear mechanisms and identification of a wear model appear to be fundamental. The key idea of this work consists in using a dedicated tribometer, able to simulate relevant tribological conditions encountered in cutting (pressure, velocity). The tribometer can be used to estimate the evolution of wear versus time for various tribological conditions (pressure, velocity, temperature). Based on this design of experiments, it becomes possible to identify analytically a wear model. As a preliminary study this paper will be focused on the impact of sliding speed at the contact interface between 304L stainless steel and tungsten carbide (WC) coated with titanium nitride (TiN) pin. This experiment enables to observe a modification of wear phenomena between sliding speeds of 60 m/min and 180 m/min. Finally, the impact on macroscopic parameters has been observed.

The Influence of the Interstand Tension of the Band on Roll Wear during the Continuous Groove-Rolling Process

2015 ◽  
Vol 220-221 ◽  
pp. 898-904 ◽  
Author(s):  
Piotr Szota ◽  
Sebastian Mróz ◽  
Andrzej Stefanik ◽  
Henryk Dyja
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Quantitative Evaluation ◽  
Theoretical Study ◽  
Rolling Process ◽  
Wear Model ◽  
Bar Rolling ◽  
Groove Rolling ◽  
Roll Wear ◽  
Round Bar

Numerical modelling of the round bar rolling process, while considering the wear of passes depending on their shape, was carried out within the present work. The analysis of the rolling process was conducted thus analysing the influence of interstand tension on roll wear. For the theoretical study of the rolling process, Forge2011® was employed, which is finite element method-relying software that enables the thermo-mechanical simulation of rolling processes in a triaxial state of strain. The wear model implemented in Forge2011® permits no quantitative evaluation, but only a comparative analysis of the wear of rolls. In order to use the results of simulation employing the simplified Archard model for the quantitative evaluation of roll wear, it is necessary to define the factor of wear and the hardness of the tool as a function of temperature.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Retweet Prediction Based on User Behavior

2021 ◽  
Author(s):  
Syeda Nadia Firdaus
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Network ◽  
Prediction Model ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
User Behavior ◽  
Past Research ◽  
The Difference ◽  
The Impact

Social network is a hot topic of interest for researchers in the field of computer science in recent years. These social networks such as Facebook, Twitter, Instagram play an important role in information diffusion. Social network data are created by its users. Users’ online activities and behavior have been studied in various past research efforts in order to get a better understanding on how information is diffused on social networks. In this study, we focus on Twitter and we explore the impact of user behavior on their retweet activity. To represent a user’s behavior for predicting their retweet decision, we introduce 10-dimentional emotion and 35-dimensional personality related features. We consider the difference of a user being an author and a retweeter in terms of their behaviors, and propose a machine learning based retweet prediction model considering this difference. We also propose two approaches for matrix factorization retweet prediction model which learns the latent relation between users and tweets to predict the user’s retweet decision. In the experiment, we have tested our proposed models. We find that models based on user behavior related features provide good improvement (3% - 6% in terms of F1- score) over baseline models. By only considering user’s behavior as a retweeter, the data processing time is reduced while the prediction accuracy is comparable to the case when both retweeting and posting behaviors are considered. In the proposed matrix factorization models, we include tweet features into the basic factorization model through newly defined regularization terms and improve the performance by 3% - 4% in terms of F1-score. Finally, we compare the performance of machine learning and matrix factorization models for retweet prediction and find that none of the models is superior to the other in all occasions. Therefore, different models should be used depending on how prediction results will be used. Machine learning model is preferable when a model’s performance quality is important such as for tweet re-ranking and tweet recommendation. Matrix factorization is a preferred option when model’s positive retweet prediction capability is more important such as for marketing campaign and finding potential retweeters.

scholarly journals Comments on "Researcher bias: The use of machine learning in software defect prediction"

2015 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Shepperd et al. (2014) find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al. (2014)’s data. We observe that (a) researcher group shares a strong association with the dataset and metric families that are used to build a model; (b) the strong association among the explanatory variables introduces a large amount of interference when interpreting the impact of the researcher group on model performance; and (c) after mitigating the interference, we find that the researcher group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model may have more to do with the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat potential bias in their results.

An Available-to-Promise System for Network-Manufacturing

2011 ◽  
Vol 268-270 ◽  
pp. 303-308 ◽  
Author(s):  
Wen Hao Wang ◽  
Qiong Zhu ◽  
Jie Zhang
Keyword(s):  
Production Planning ◽  
Planning Horizon ◽  
Global Environment ◽  
Quick Response ◽  
Key Factors ◽  
Planning And Scheduling ◽  
Production Planning And Scheduling ◽  
Analysis Experiment ◽  
Order Promising ◽  
The Impact

This Paper Presents Development of an Available-to-Promise (ATP) System for Network-Manufacturing in a Global Environment where Multi-Plants Are Manufacturing Products Collaboratively and Are Globally Networked. within the Multi-Plant Mode, the ATP Process Is Challenged to Give Not only Quick Response but Also Precise Order Promising by Considering the Coordination of Production Planning and Scheduling among Plants. in this Paper, an Order Decision and Production Planning Integrated ATP Model Was Developed and a Series of Performance Analysis Experiment Was Conducted to Reveal the Impact of some Key Factors such as Planning Horizon, Batch Interval Etc. on Overall Profit.

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