Session: 17-01-01 Research Posters

Paper Number: 77355

Start Time: Thursday, 02:25 PM

77355 - Preliminary Investigation on the Acoustic Characteristics of Turning Processes 

Process planning and monitoring methodologies have improved over the past decades. They have enabled manufacturing engineers to optimize process parameters at the planning stages of product development. Unfortunately, in process faults and anomalies during manufacturing are of a sudden nature and are often difficult to predict as they are typically part-, process-, or tool-specific and are highly dependent on the manufacturing environment. The advancement of monitoring techniques and data processing have enabled manufacturers around the globe to effectively monitor and control manufacturing equipment to ensure optimal operation during unforeseen process disturbances. One of the challenges facing monitoring capabilities of a machine is the use of embedded sensors for legacy machines or interference with the workpiece or the tool.

The objective of this research is to characterize the turning process using acoustic signals (AS) for the purpose of remote condition monitoring. This will allow for non-invasive machine monitoring, reducing costs and interference in the machining operation. Various combinations of process parameters were investigated, including spindle speed, depth of cut, and feed rate. The machining parameters used herein were closely matched with those of a milling operation that was utilized in previous research. The intent is to investigate the use of AS to monitor and differentiate multiple machines, around the shop floor, running simultaneously. The parameters investigated were: spindle speeds of 800, 1200, and 1400 rpm; depths of cut of 0.127, 0.254, and 0.508 mm. The feed rates for the turning process were mapped to mimic those for the milling process with a direct conversion from mm/min to mm/rev. The material and cutting tool used for this initial investigation was 1018 steel and Kennametal KC9040 carbide insert, respectively.

A spherical 32-microphone array was utilized for data collection with a sampling rate of 48 kHz. For the research presented herein, only the signal from the microphone capturing the strongest signal is discussed. Frequency and time domain characteristics were utilized to find distinguishing features of the AS. It was found that turning speeds produced noticeable differences in the observed peaks in the frequency content of the signal, providing a means of determining spindle speed from AS. Additionally, time-domain characteristics yielded discernible differences for both feed rate and depth of cut. An increase in the rms value was observed as the material removal rate (MRR) of the machining process increased. The results suggest that a combination of both frequency and time domain characteristics may be used to distinguish the process parameters.

Presenting Author: Zachery Deabenderfer Penn State University

Authors:

Zachery Deabenderfer Penn State University
Katherine Korn Penn State University
Scott Kerner Clemson University
Ihab Ragai Penn State University, Erie
Yabin Liao Embry-Riddle Aeronautical University
David Loker Penn State University