Burr formation and correlation with cutting force and acoustic emission signals

Abstract

The principle objective of this work is to present a methodology to evaluate the correlation between burr size attributes (thickness and height) and information computed from acoustic emission and cutting forces signals. In the proposed methodology, cutting force and acoustic emission signals were recorded in each cutting test, and each recorded original acoustic emission signal was segmented into two sections that correspond to steady-state cutting process (cutting signal) and cutting tool exit from the work part (exit signal). The dominant acoustic emission signal parameters including AE max and AE rms were computed from each segmented acoustic emission signal. The maximum values of directional cutting forces (F X , F Y and F Z ) were also measured in each trial. The experimental verification was conducted on slot milling operation which has relatively more complicated burr formation mechanism than that in many other traditional machining operations. Among slot milling burrs, the top-up milling side burrs and exit burrs along up milling side were largest and thickest burrs which were studied in this work. To evaluate the correlation between signal information and burr size, the computed signal information (5 parameters) and their interaction effects (10 parameters) were used to construct the input parameters of the multiple regression fitted models. Statistical methods were then used to assess the adequacy of individual input parameters and signal information. Using the acoustic emission and cutting force signals information in the input layer of multiple regression models, a high correlation was observed between the predicted and observed values of burr size. It was exhibited that due to complex burr formation mechanism in milling operation and strong interaction effects between cutting process parameters, no systematic relationship can be formulated between the milling burrs.

The Use of Wavelet Transform To Evaluate The Sensitivity of Acoustic Emission Attributes To Variation of Cutting Parameters in Milling Aluminium Alloys

“…These signals can include background noise such as mechanical, electrical, or acoustic. Aluminum alloys machining presents challenges such as burr formation, BUE, and adhesion of work parts to the cutting tools [38][39][40][41].…”

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

“…Generally, more complex signals can be observed in milling compared to those in many other non-traditional machining operations. As noted earlier, a certain number of difficulties including burr formation, BUE, and work part adhesion to the cutting tool are considered as frequent observations when machining AAs, [35][36][37][38] in principle the milling operation. To reduce the risk of the abovementioned issues, system stability and background noise were evaluated and controlled in preliminary experimental tests and new tool inserts were used after each cutting test.…”

“…AE signal, x(t) extraction in each cutting trial concerning the cutting tool progress and exit from machined parts (Figure 5 The significant and insignificant variables and models are identified using statistical parameters, including p value, R 2 , and R 2 adj (section ''Method of analysis''). As previously noted in Songmene and his colleagues, [35][36][37][38] machining aluminum alloys is associated with a certain amount of difficulties including burr formation, built-up edge (BUE), and work part adhesion to the cutting tool. Therefore, to decrease the risk of the aforementioned issues and avoid harmful effects into the experimental set-up and recorded results, the following assumptions were proposed:…”

An investigation on formation of burrs during milling of aluminium alloy under wet condition

“…Apart from the above, a number of other investigations 32–36 was carried out on burr minimization in face milling. Different factors governing burr formation were explored 32 during high-speed milling of wrought aluminum alloys by Niknam and Songmene.…”