Acoustic emission detection of metals and alloys during machining operations

Jameson Kyle Nelson, Purdue University


Practical correlation between material deformation attributes and theoretical concepts of machining has proven difficult to attain. The purpose of this study was to further explore trends and relationships using acoustic emission detection of materials undergoing single-point lathe turning machine processes. The majority of machining experiments that incorporate acoustic emissions focuses on tool degradation for the purposes of optimizing consumables required to manufacture mechanical devices. Experiments were implemented varying recording location, mechanical barrier condition, and machine parameters. The research focused on machining metal alloys of tantalum tungsten, nitronic 33 stainless steel, M42 tool steel, and 6061 aluminum. Variation of machine parameters included the alteration to depth of cut, coolant flow rate, cutting velocity, and feed rate of tooling. It was found that, by using capacitive microphone transducer technology, it is difficult to confidently discern the type material undergoing machine processes from significant distances. Structural spectrogram "acoustic mapping" using discrete Fourier transform based methods presented promise in uniquely identifying the type material being machined. Integral calculus methods of summation were implemented to determine the energy released during machine processes. Hypotheses proved that as stand-off distance increased and mechanical barriers were imposed detection of acoustic emissions varied in confidence. The alteration of machine parameters had varying effects on the detection of acoustic emissions. The experiments are discussed in a practical context for discovering as much about machining processes as possible.^




Rodney G. Handy, Purdue University.

Subject Area

Engineering, Mechanical

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