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1

Meenakshi Arya

Abstract:
Predictive maintenance uses data analysis to anticipate equipment failures before they occur. This approach can significantly reduce downtime, improve operational efficiency, and extend the lifespan of machinery. Artificial intelligence (AI) has emerged as a powerful tool for predictive maintenance by enabling the analysis of complex sensor data and identifying patterns that indicate potential problems. This paper examines how AI can be used for predictive maintenance in industrial settings, exploring different AI techniques, their implementation considerations, and the benefits they offer for industrial machine health.


1-10
2

Mayur P. Patil1, Anil Rane2

Abstract:
This paper presents studies on developing the single-side lapping process for white ceramic with the addition of an external drive for use in improving the stock removal rate. The idea is to prove the effectiveness of the external drive in raising SRR levels toward eliminating obstacles to improving material removal rates in lapping processes. The experimental setup, methodology, and results give insight into how this new process approach will lead to an optimized lapping process. Implementation of an external drive greatly enhances SRR while keeping the surface quality at par; hence, a new step in precision manufacturing is established. In conclusion, it recommends further research and practical application in various industrial sectors.


11-20
3

Kamal Kumar1, Sangeeta Devi2, Pratiksha Tiwari3

Abstract:
In the majority of industrial settings, demand is erratic and difficult to predict. Numerous demand histories exhibit random walk characteristics, changing often in both direction and rate of rise or drop over time.Demand is erratic and testing to forecast in the majority of industrial environments. Others call for History behaves like a random walk that varies its orientation and development or fall rate frequently throughout time. This work takes a number of models into consideration, including lot-by-lot obtaining, purchasing through economic order quantities, purchasing byepisodic order quantities, purchasing through the least unit costs, the least total costs, the least epoch costs, purchasing using Wagner-Whitin algorithms, etc.The results of using each model aimed at different lengths of time are shown. From the results, it is clear that the periodical order quantity technique is stable over a long period of time.


21-30
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