Vibration Model to Detect Local Defect Characteristics of Deep Groove Ball Bearing

Verma, Ajay; Saini, P. K.

doi:10.5109/6625791

Cited by 2 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some more works carried out by the researchers also showed systematic failure [12][13][14] occurred inside the cutting tools which are taken care for reference in the manuscript. Furthermore, the earlier work [15][16][17][18][19] that motivated the authors to carried out this novel work that cope up with the demand of Industry 4.0.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Tool Wear Using Machine Learning Approaches for Machining on Lathe Machine

Ashish Kumar Srivastava,

Bipin Kumar Singh,

Gupta

2023

Evergreen

View full text Add to dashboard Cite

In manufacturing industries, removal of material from the workpiece is the prime processes that convert raw material into finished product. During removal processes the cutting tool are incessantly deteriorated in health, which can be stated as perks and drawbacks of process. The precision and roughness of the material are directly related to the condition of the tools during the machining process. Machining analysis depends on numerous of cutting conditions when it is being performed. The likelihood of wearing increases with repeated use. So, by implementing the proposed approach for tool wear prediction can improve the quality as well as reduce the machining time. However, to maintain the healthy tool's conditions for prolong time is a major challenge for the scientific community. Hence, as a component of industry 4.0, this study explored the possibilities to monitor the tool condition using the machine learning techniques. So, an endeavor has been made to present a solution of this problem without hampering the productivity losses in terms of time, material, and tool, consequences in high productivity. For the proposed work, machine learning techniques such as k-NN, Random forest, Adaboost, k-Star, and Decision Tree are implemented and there accuracy of prediction is demonstrated. Furthermore, WEKA, open source software has been used to employ several tool learning algorithms for better understanding. The investigation noticed that the random forest algorithm has a higher accuracy of 97.30% and a root mean square error value of 0.144 among all other algorithm.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Tool Wear Using Machine Learning Approaches for Machining on Lathe Machine

Ashish Kumar Srivastava,

Bipin Kumar Singh,

Gupta

2023

Evergreen

View full text Add to dashboard Cite

show abstract

Analysis of Vibration Model to Study the Effect of Defect Sizes and Load on Stiffness, Contact Deformation, and Contact Force Variation in Deep Groove Ball Bearing

Verma,

Saini

2024

Advances in Science and Technology

View full text Add to dashboard Cite

In this paper, the vibration model has been suggested to study the local defects in the deep groove ball-bearing system. MATLAB is used to solve the equations governing the bearing system to simulate its vibration signals. The vibration model of the deep groove ball bearing system is constructed and takes into account the masses of the housing, balls, races, and shaft in addition to the damping at the ball-race interface and different-sized race defects. The effect of different defect sizes on the measured load distribution has been analyzed and discussed. The contact deformation and load distribution for a fault-free bearing system as well as for faults with βball=50 and 400 circumferential extents are measured and displayed for interpretation. Due to de-stressing and losing all or some of its ability to support a load, the load that a ball placed inside the defect zone previously carried has been split between the balls outside of the defect region. This implies that balls lose all or part of their load-bearing capacity when they are positioned in the defect zone, increasing the static loading on the raceway sections without any defects. Moreover, it is seen that the radial clearance, applied load, and defect shape all significantly affect the load redistribution that follows the loss of load-bearing capacity.

show abstract

Vibration Model to Detect Local Defect Characteristics of Deep Groove Ball Bearing

Cited by 2 publications

References 28 publications

Prediction of Tool Wear Using Machine Learning Approaches for Machining on Lathe Machine

Prediction of Tool Wear Using Machine Learning Approaches for Machining on Lathe Machine

Analysis of Vibration Model to Study the Effect of Defect Sizes and Load on Stiffness, Contact Deformation, and Contact Force Variation in Deep Groove Ball Bearing

Contact Info

Product

Resources

About