LATHE MACHINE VIBRATION PREDICTION: AN INDUSTRY 4.0 PERSPECTIVE 候选 · 未审核

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The prediction of vibration between the tool and workpiece is important as a guideline to the machine tools used for an optimal selection of depth of cut and spindle rotation to minimize the vibration. This can be done by different approaches. Industrial vibration analysis is a measurement tool used to identify, predict, and prevent failures in machinery. Implementing vibration analysis on the machines will improve the reliability of the machines and lead to better machine efficiency and reduced downtime eliminating mechanical or electrical failures. Vibration analysis programs are used throughout industry worldwide to identify faults in machinery and keep machinery functioning for as long as possible without failure. The present work concentrates and aims at Using IoT. We can track real-time vibrations and monitor the system to increase productivity. During the machining operation, low surface finish is occurred due to wear and tear of a tool, and also to lose the mounting lead to increase vibrations which will accelerate machine wear, consume excess power, and cause equipment to be taken out of service, resulting in unplanned downtime. Other effects of vibration include safety issues and diminished working conditions After collecting all this information. Out of the above-mentioned factors we chose the vibration factor because it not only affects jobs or products but also harms people in many ways. Hence, we have developed IoT-based machine monitoring solution which can adapt to track the real-time performance of lathe machine. To monitor excessive vibration from the lathe machine to achieve better results in the job with the help of sensors & the HC-05 Bluetooth module, we can catch the real-time values of vibration on our mobile as a notification. In this method, we have established the system which uses a vibration sensor (sound sensor) that detects the excessive noise during machining operation and sends the signal to various devices like mobiles, laptops, or desktops through Arduino.

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落地页

https://osf.io/cgdtf/

国内可访问性

国内直连:可达 (2026-07-11 检测) 非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

发布年份

2022

设备类型

machine_tool

PHM 任务

condition_monitoring