Fracture in self-lubricating inserts: A case study

Pratap, Ayush; Singh, Bipin Kumar; Sardana, Neha

doi:10.1016/j.matpr.2022.06.005

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…Researchers also demonstrated an accuracy of about 96.90%, for the face milling process. 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

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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.

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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

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“…In complex and harsh conditions such as high temperature, high speed, high load, vacuum, and strong radiation, the evaluation for coating is the key to ensuring the normal service of self-lubricating sliding bearings. In the coating structure, it is easy to form weak spots due to insufficient bonding strength [4], and these weak spots may induce the coating cracking under the impact of load [5,6]. According to the forming locations, the crack defects inner the coating are divided into two types.…”

Section: Introductionmentioning

confidence: 99%

Evaluation for Crack Defects of Self-Lubricating Sliding Bearings Coating Based on Terahertz Non-Destructive Testing

Huang

Zhong

et al. 2023

Coatings

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In this study, a non-destructive testing method for crack defects of self-lubricating sliding bearing coating based on terahertz time-domain spectroscopy was proposed. The self-lubricating coating materials were revealed to have good penetration and characteristic response in the terahertz band through experiments. To solve the problem of difficulty in signal feature extraction caused by overlap, the broad learning system was used to classify and predict time-domain signals of crack defects. The identification accuracy for crack defects is 96.08%, and the mean relative errors of prediction for interface and internal cracks (5 to 95 μm in size) are 4.16% and 3.40%, respectively. The method proved the applicability for qualitative and quantitative evaluation in crack defects, which is considered a new idea for the non-destructive testing of self-lubricating coating.

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“…Experiments with different water nanofluids in basin also showed remarkable improvement in the performances. Incorporated sensible heat storage materials like cement concrete bits, quartzite rock, washed pebbles, iron scraps, and red brick bits into the still found that adding of an inch quartzite rock showed increased productivity than the other materials [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Solar Still by Using Octagonal-Pyramid Shape in the Solar Desalination Techniques

Singh¹,

Ramji

Ganeshan³

et al. 2023

International Journal of Photoenergy

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This research work explored and compared the experimental performance of a solar still having novel octagonal-pyramid shape with a single slope solar still. It is found that the novel still provides twice distillation compared with conventional still. The experiments also evaluated the desalination productivity of octagonal-pyramid solar still by varying the depth of saline water inside the basin and angle of inclination of glass cover. It is observed that the optimum condition for high distillation is obtained when depth of water inside the basin is 5 cm with angle of inclination of glass cover which is 30°. Four types of water, i.e., underground borewell water, sea water, leather industry effluent, and plastic industry effluent were also used to see the effect on distillation. Results showed that underground borewell water provides high distillation due to low density. Furthermore, the performance of the octagonal-pyramid solar still is enhanced by adding different latent heat and sensible heat materials in the octagonal-pyramid solar still. Hence, the addition of brick to the octagonal-pyramid still yields the highest productivity compared to incorporation of paraffin wax. Hence, it can be concluded that the octagonal design of the solar still has shown an increased productivity when compared to a single slope solar still (conventional still) under all the conditions.

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Fracture in self-lubricating inserts: A case study

Cited by 13 publications

References 17 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

Evaluation for Crack Defects of Self-Lubricating Sliding Bearings Coating Based on Terahertz Non-Destructive Testing

Performance Analysis of Solar Still by Using Octagonal-Pyramid Shape in the Solar Desalination Techniques

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