Degradation monitoring of machine tool ballscrew using deep convolution neural network
Degradation monitoring of machine tool ballscrew using deep convolution neural network
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发表时间：
2020-06
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通讯作者：
Nurudeen Alegeh;Abubakar Shagluf;A. Longstaff;S. Fletcher
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作者：
Nurudeen Alegeh;Abubakar Shagluf;A. Longstaff;S. Fletcher
高价值制造通常需要高精度。虽然这可能是一个可以实现的目标，但消费者和最终用户的需求也是为了降低成本、提高效率和资源节约型产品等经常相互竞争的目标。尽管有更高精度的雄心，但提高生产机器的可用性是保持制造业竞争力的基本要求。滚珠丝杠是大多数高价值机床传动系统的基本部件。因此，它们是机器的位置精度和性能的组成部分，也是可用性方面的薄弱环节。因此，滚珠丝杠的状态对于确定机床精度和可用性至关重要。这项工作提出了一种用于滚珠丝杠性能监控的深度学习方法。该技术的工作原理是，当在故障发生之前检测到退化时，可以安排和执行补救活动。深度学习算法使用卷积来区分机床中的磨损和良好滚珠丝杠。该技术进行了测试的五轴龙门式机床与两个平行轴滚珠丝杠。试验结果表明，该技术可达到94%的总体准确度。
High-value manufacturing often requires a high level of accuracy. While this may be an achievable aim, the demands of consumers and end-users are also for the often competing targets of lower cost, greater efficiency and resource-lean products. Notwithstanding the ambition for higher accuracy, increased availability of production machines is a fundamental requirement to maintain competitiveness in the manufacturing industry. Ballscrews are a fundamental part of the transmission system for most high-value machine tools. They are therefore integral to the positional accuracy and performance of the machine and also represent a weak-link in terms availability. Hence, the state of the ballscrew is essential in determining machine accuracy and availability. This work proposes a deep learning approach for ballscrew performance monitoring. The technique works such that remedial activities can be scheduled and carried out when degradation is detected before breakdown occurs. The deep learning algorithm uses convolution to distinguish between a worn and good ballscrew in a machine tool. The technique was tested on a five-axis gantry-type machine tool with two parallel axis ballscrew. The results from the test carried out indicates that an overall accuracy of 94 % can be achieved with this technique.