INVESTIGATION OF FACTORS GOVERNING SLOT MILLING BURR FORMATION 

INVESTIGATION OF FACTORS GOVERNING SLOT MILLING
BURR FORMATION 

INVESTIGATION OF FACTORS GOVERNING SLOT MILLING BURR FORMATION:

Burr formation and edge finishing are research topics with high relevance to industrial applications. To remove burrs, however, a secondary operation known as deburring is usually required. Until now more than 100 deburring methods have been developed (Gillespie, 1999), which their appropriate selection depends on many factors including, burr location and dimension. Among machining operations, milling burr formation has a more complex mechanism with multiple burrs formed at different locations with varying sizes. This yields to many difficulties during deburring process. Therefore, it is extremely beneficial to limit burr formation rather than deburring them in subsequent finishing operations. One approach is to develop analytical models of burr formation process. Since theoretical approaches are usually not available, more focus has been paid to experimental studies to identify the effects of cutting parameters on burr formation.

The effects of numerous process parameters on face milling burrs were reported in (Avila and Dornfeld, 2004; Chern, 1993; Hashimura, Hassamontr and Dornfeld, 1999; Kishimoto et al., 1981; Kitajima et al., 1990; Korkut and Donertas, 2007; Olvera and Barrow, 1996; Olvera and Barrow, 1998; Tsann-Rong, 2000). Furthermore, most of the existing research works in literature characterized the burr height, while from deburring perspective, the burr thickness is of interest, because it describes the time and method necessary for deburring a workpiece (Aurich et al., 2009). In addition, only few studies have used statistical analysis to precisely determine the dominant process parameters on burrs size (Lekkala et al., 2011; Mian, Driver and Mativenga, 2011a). As per author’s knowledge, surprisingly except few works (Chen, Liu and Shen, 2006; Mian, Driver and Mativenga, 2011a; Tang et al., 2011), very low volume of information is available about factors governing slot milling burrs.

Experimental procedur:

Experimental plan
A multi-level full factorial design of experiment (33 ×22 ) is selected in this study. The AA 6061-T6 and AA 2024-T351 with relatively similar mechanical properties are used for experiments (see Table 2.2). The experimental factors and their levels are shown in Table 2.1. Cutting tool and workpiece materials were treated as qualitative factors while other remaining factors were considered quantitative. In total, 108 experiments were performed under dry milling using a 3-axis CNC machine tool (Power: 50kW, Speed: 28000 rpm; Torque: 50 Nm), as shown in Figure 2.1(a). An Iscar coated end milling cutting tool (E90-AD.75-W.75-M) with three flutes (Z=3), and tool diameter (D) 19.05mm was used (see Figure 2.1(b)). With respect to cutting conditions used, the suitable inserts were consequently used in cutting tests.

MODELING OF BURR THICKNESS IN MILLING OF DUCTILE MATERIALS:

Among machining operations, analytical modeling of burr formation in milling is very challenging. Most of reported works in the literature aim to measure and/or predict the burr height, but this information is not very useful for deburring purposes. The thickness of the burr is much of interest because it describes the time and method necessary for deburring a workpiece (Aurich et al., 2009). However burr thickness measurements are costly and nonvalue-added operations that in most of cases require the use of Scanning Electron Microscope (SEM) for accurate burr characterization. Therefore, to avoid such non-desirable expenses, the use of alternative methods for burr thickness prediction is strongly recommended.

According to experimental results in chapter 2, it was found that exit up milling side burr (B1) is the longest and thickest milling burr. In addition, B1 thickness can be controlled by feed per tooth and depth of cut. In this chapter, an analytical model is proposed to predict the B1 thickness (Bt) in the exit up milling side burr (see Figure 3.1) during slot milling operation of ductile materials. The model is built on the geometry of burr formation and the principle of continuity of work at the transition from chip formation to burr formation that also considers the effect of cutting force on burr formation. For experimental validation, the Bt was recorded at four locations and the average of readings was taken as the burr size. We also anticipate proposing a procedure for computational modeling of Bt by assuming negligible effect of coated insert nose radius and cutting speed on Bt.

SIMULTANEOUS OPTIMIZATION OF BURR SIZE AND SURFACE FINISH DURING SLOT MILLING OPERATION:

Nowadays due to growth of industrials competition, the use of suitable optimization methods for correct selection of process parameters is extremely necessary to avoid non- value added expenses. The optimization of process parameters requires a systematic methodological approach by using experimental methods and mathematical/statistical models (Gaitonde, Karnik and Davim, 2009). Fuzzy logic (FL), genetic algorithm (GA), Neural Network (NN), Taguchi method and response surface methodology (RSM) are the latest optimization techniques that are being applied successfully in industrial applications. Artificial neural network (ANN) is a computational learning system which attempts to simulate the human intuition in making decisions and drawing conclusions when presented with complex, irrelevant, and partial information (Karnik, Gaitonde and Davim, 2008). In the past, ANNs have been successfully employed to model several processes and also define the optimum conditions of process parameters (Tong, Kwong and Yu, 2004; Vafaeesefat, 2009).

SUBSTANTIAL SUMMARY OF THE RESEARCH WORK :

Burr formation is a common problem occurs in several industrial sectors, such as aerospace, ship construction, automobile, etc. It becomes an even more important issue when dealing with ductile materials, such as aluminium alloys. Any solution to prevent or at least minimizing burr formation starts by understanding the fundamentals of burr formation. Nevertheless, the burr formation minimization and prevention still require research and close attention. This therefore calls for a review of existing approaches. The research work presented in this thesis consisted of three aspects which construct the main and specific objectives of this research work (see Figure 5.1). The first aspect of this study is devoted to experimental and statistical studies to determine the factors governing burrs size during slot milling of aluminium alloys. The second aspect focuses on theoretical work, including predictive (analytical and computational) modeling of slot milling burrs in ductile materials. The simultaneous multiple responses optimization during milling (slotting) operation is the subject of the last aspect of this thesis. As described in introduction, all these three aspects are related to others and were designed for burr size minimization which is the main research objective of this work ). This chapter presents a discussion of the obtained results in each aspect and aims to link them with the proposed research objectives in this work.

CONCLUSION:

Burr formation mechanism and patterns in slot milling of aluminium alloys were investigated in this work. Factors governing burrs size (thickness and height) were identified. Analytical and computational models were proposed to predict the longest and thickest burr. Considering that parameters optimization for only minimizing burr formation could frequently deteriorate other machining quality index, surface roughness and cutting forces were also investigated with burr formation. Multiple responses optimization during slot milling of aluminium alloys is presented. From analysis and discussion of results, the following conclusions are drawn:

 -According to experimental results, feed per tooth controls most of burrs followed by depth of cut and tool geometry, depending on the burr considered. However factors governing each burr does not similarly identical for others. The side burrs, whether entrance (B5) or exit side burrs (B1) are dominated by feed per tooth, workpiece material and insert nose radius (Rε). The top burrs are mainly affected by variation of cutting conditions; insert nose radius (Rε) and tool coating.
-Factors governing slot milling burrs height and thickness were found dissimilar. Therefore no relationship could be formulated between the burr height and thickness at different edges of the machined part. One reason could be due to presence of strong interaction effects between process parameters which are difficult to observe and cannot be predicted by simple regression models. As a result, except few cases, empirical modeling of burrs size (height and thickness) was unsuccessful, particularly for slot milling, it does not serve a very good purpose.

Table des matières

INTRODUCTION 
CHAPTER 1 LITERATURE REVIEW 
Introduction
Definition and characterization of burr
Burr formation mechanism
Burr shapes.
Burr size measurement and detection methods
Burr removal (deburring)
Concerns on burr formation
Understanding and modeling of milling burr formation
Milling burrs shapes
Parameters governing milling burr formation .
Milling burr formation modeling
Optimization methods
Taguchi method
Response surface methodology (RSM)
Desirability function
Conclusion of literature review and refining of problematic
CHAPTER 2 INVESTIGATION OF FACTORS GOVERNING SLOT MILLING
BURR FORMATION 
Introduction
Experimental procedure
Experimental plan
Experimental observations
Assumptions
Results and discussion
Method of analysis
Effects of process parameters on slot milling burrs
Response surface models
Controllable response
Conclusion
CHAPTER 3 MODELING OF BURR THICKNESS IN MILLING OF DUCTILE
MATERIALS
Introduction
Theoretical modeling of milling burr thickness
Experimental results and discussion
Computational results and discussion
Conclusion
CHAPTER 4 SIMULTANEOUS OPTIMIZATION OF BURR SIZE AND SURFACE
FINISH DURING SLOT MILLING OPERATION 
Introduction
An overview of Taguchi Method .
Proposed methodology
Experimental results
Experimental procedure
Analysis of responses
Multiple responses optimization
Experimental validation
Conclusion .
CHAPTER 5 SUBSTANTIAL SUMMARY OF THE RESEARCH WORK 
Introduction
Dominant process parameters on slot milling burrs size
Controllable responses
Milling burr size modeling of ductile materials
Multiple responses optimization in slot milling
Key contributions and outcomes of the thesis
CONCLUSION

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