Wavelets for fault diagnosis of rotary machines: A review with applications

Gear crack level identification based on weighted K nearest neighbor classification algorithm

Intelligent spindles are core components of the next-generation of intelligent/smart machine tools in the Industry 4.0 Era. The purpose of this paper is to clarify the concept of intelligent spindles and provide an in-depth review of the state-of-the-art of related technologies. A new integrated concept for intelligent spindles is proposed, followed by descriptions of required characteristics, key enabling technologies and expected intelligent functions. Relevant research that may be beneficial to the development of intelligent spindles is reviewed from six thrust areas, which include monitoring and control of tool condition, chatter, spindle collision, temperature/thermal error, spindle balance, and spindle health. Finally, current limitations and challenges are discussed, and future trends of intelligent spindles are prospected from various perspectives.

The concept and progress of intelligent spindles: A review

The most important improvement in metal the cutting industry is the continuous utilization of cutting tools and tool condition monitoring system. In the metal cutting process, the tool condition has to be administered either by operators or by online condition monitoring systems to prevent damage to both machine tools and workpiece. Online tool condition monitoring system is highly essential in modern manufacturing industries for the rising requirements of cost reduction and quality improvement. This paper summaries various monitoring methods for tool condition monitoring in the milling process that have been practiced and described in the literature.

Tool condition monitoring techniques in milling process — a review

Tool life predictions in milling using spindle power with the neural network technique

Artificial neural network-based prediction technique for wear loss quantities in Mo coatings

Monitoring the condition of cutting tools in any machining operation is very important to avoid unexpected machining trouble and improve machining accuracy. This paper presents the use of vibration analysis of the cutting process in milling to indicate the presence and progression of damage incurred by an end mill. The metal cutting experiments were performed on a mild steel workpiece without using any coolant to accelerate damage to cutter, and classical processing schemes in time and frequency domains were applied to the resulting vibrations of cutting process to obtain diagnostic information. Moreover, developing fault features were also illustrated using both scalogram and its mean frequency variation. It has been found that scalogram and its mean frequency are both capable of revealing the features of not only localized, but progressive fault more clearly in the presence of strong noise than conventional time and frequency domain analyses. Furthermore, the global average of the mean frequency variati...

Tool condition monitoring in milling using vibration analysis

A local gear-tooth defect such as a fatigue crack, pit or chip weakens the tooth and causes transient events when that faulty tooth is in mesh. The magnitude and duration of these events depend mainly upon the severity of the defect and the contact ratio of the gear pair. If the tooth fault severity is small and the contact ratio is high, the resulting transient may not show distinctively in the vibration signal; time-frequency analysis can be used to reveal such events. This paper presents the use of a scalogram and its mean frequency variation for the detection and monitoring of pitting faults in gears. Real gear vibrations are obtained from a test rig utilising a two-stage industrial gearbox. Pits representing differing degrees of fault severity are simulated on a few tooth surfaces. Classical processing schemes in the time and frequency domain are firstly employed to obtain general characteristics of the gear vibration. The continuous wavelet transform is then used to obtain a scalogram and its mean f...

Hasan Ozturk

Papers

Artificial neural network-based prediction technique for wear loss quantities in Mo coatings

Tool condition monitoring in milling using vibration analysis

Early Detection of Pitting Damage in Gears using Mean Frequency of Scalogram

Crack effects on the in-plane static and dynamic stabilities of a curved beam with an edge crack

In-plane stability analysis of non-uniform cross-sectioned curved beams