Quality and Inspection of Machining Operations: Tool Condition Monitoring

被引:51
作者
Roth, John T. [1 ]
Djurdjanovic, Dragan [2 ]
Yang, Xiaoping [3 ]
Mears, Laine [4 ]
Kurfess, Thomas [4 ]
机构
[1] Penn State Erie, Erie, PA 16563 USA
[2] Univ Texas Austin, Austin, TX 78712 USA
[3] Cummins Inc, Columbus, IN 47202 USA
[4] Clemson Univ, Clemson, SC 29634 USA
来源
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME | 2010年 / 132卷 / 04期
关键词
FUZZY TRANSITION-PROBABILITY; HIDDEN MARKOV-MODELS; GRINDING WHEEL; NEURAL-NETWORK; PROGRESSIVE FAULTS; WEAR DETECTION; SYSTEM; FORCE; ONLINE; PREDICTION;
D O I
10.1115/1.4002022
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Tool condition monitoring (TCM) is an important aspect of condition based maintenance (CBM) in all manufacturing processes. Recent work on TCM has generated significant successes for a variety of cutting operations. In particular, lower cost and on-board sensors in conjunction with enhanced signal processing capabilities and improved networking has permitted significant enhancements to TCM capabilities. This paper presents an overview of TCM for drilling, turning, milling, and grinding. The focus of this paper is on the hardware and algorithms that have demonstrated success in TCM for these processes. While a variety of initial successes are reported, significantly more research is possible to extend the capabilities of TCM for the reported cutting processes as well as for many other manufacturing processes. Furthermore, no single unifying approach has been identified for TCM. Such an approach will enable the rapid expansion of TCM into other processes and a tighter integration of TCM into CBM for a wide variety of manufacturing processes and production systems. [DOI: 10.1115/1.4002022]
引用
收藏
页码:0410151 / 04101516
页数:16
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