Burr formation is one of the main concerns usually faced by machining industries. Its presence leads to additional part edge finishing operations that are costly and time consuming. Burrs must be removed as they are source of dimensional errors, jamming and misalignment during assembly. In many cases burrs may injure workers during handling of machined part. Due to burr effect on machined part quality, manufacturing costs and productivity, more focus has been given to burr measurement/estimation methods. Large number of burr measurement methods has been introduced according to various criteria. The selection of appropriate burr size estimation method depends on number of factors such as desired level of quality and requested measuring accuracy. Traditional burr measurement methods are very time consuming and costly. This article aims to present empirical models using acoustic emission (AE) and cutting forces signals to predict entrance and exit burrs size in slot milling operation. These models can help estimating the burrs size without having to measure them. The machining tests were carried on Al 7075-T6 aluminum alloy using 3 levels of cutting speed, 3 levels of feed rate, 3 levels of cutting tool coating and 2 levels of depth of cut. Mathematical models were developed based on most sensitive AE parameters following statistical analysis, cutting forces and their interaction on predicting the entrance and exit burrs size. The proposed models correlate very well with the measured burrs size data.

Machining burrs are formed at all machined workpiece edges. One useful solution to decrease machining time and cost, in particular for milling parts, is to generate machined parts edges with minimum burr. This article proposes burr edge occupancy ηs as an index to evaluate deburring difficulty and, consequently, adequate selection of suitable deburring methods. Initially the sensitivity of ηs to cutting parameters must be evaluated. We investigated the main governing factors on ηs when slot milling two types of aluminium alloys (from different families) that are used in the automotive and aerospace industries. The cutting parameters that led to edges with minimum ηs are presented. It was found that, unlike most burr size attributes, ηs is sensitive to variation of the cutting parameters used: cutting speed, family of material, and cutting tools. Lower ηs means less time and effort for deburring and edge finishing of machined parts. Furthermore, ηs measurement is more convenient than the procedures used to measure other burr size attributes, including burr height (bh) and burr thickness (bt).