FUZZY MODELING OF RECAST LAYER FORMATION IN LASER TREPAN DRILLING OF SUPERALLOY SHEET

Transcription

1 INTERNATIONAL JOURNAL OF MANUFACTURING TECHNOLOGY AND INDUSTRIAL ENGINEERING (IJMTIE) Vol. 2, No. 2, July-December 2011, pp FUZZY MODELING OF RECAST LAYER FORMATION IN LASER TREPAN DRILLING OF SUPERALLOY SHEET Rupesh Goyal 1*, A. K. Dubey 2 & P. K. Mishra 3 ABSTRACT: Laser drilling has proven to be the preferred method for drilling of cooling holes in aerospace components due to its ability of producing very small, closely spaced and high quality holes. For drilling microholes in thin sheetmetals laser percussion drilling is used, while laser trepan drilling gives better accuracy and quality for thick sheets. The previous works indicate that the laser drilled holes quality can be improved by controlling the various defects such as spattering, heat affected zone, tapering and recast layer. This paper presents a fuzzy logic based modeling of recast layer formation in laser trepan drilling of Inconel 718 (a super alloy) sheet. The Developed fuzzy model has been compared with the experimental observations from referred work, and it was found that the predicted values of recast layer thickness were very close to the experimental values. Keywords: Laser trepan drilling, Recast layer, Fuzzy modeling. 1. INTRODUCTION Laser drilling is a thermal process, where a high intensity laser beam is focused to a spot of sufficiently high energy so as to melt and vaporize almost any substance in its path. Two types of laser drilling exist, trepan and percussion laser drilling. Trepan drilling involves cutting around the circumference of the hole to be generated, whereas percussion drilling punches directly through the workpiece material with no relative movement of the laser or workpiece (Figure 1). The inherent advantage of the percussion drilling process is the reduction in processing time, but hole quality is poor due to formation of recast layer and spattering. Trepan drilling may be preferred where hole quality characteristics matters most [1]. Many parametric studies have been carried out on aerospace superalloys and other materials to assess the relationship between various laser parameters and hole quality characteristics. Some researchers have applied the Taguchi s robust parameter design method to optimize the various process parameters. Tam et al. [2] used this technique to optimize the parameters such as pulse energy, pulse duration, pulse shape focal position, and assist gas pressure. Using this method, the optimum parameters for minimizing the process time, when drilling deep holes in Inconel 718 using Nd: YAG laser, were identified. Ng and Li [3] performed experimental study to examine the hole geometry repeatability associated with laser percussion drilling. The results indicated that variations of the laser peak power and the pulse width determined the melt ejection characteristics and, hence, influenced the hole geometry and repeatability. If molten material is not blown away during the laser drilling process, it will resolidify and accumulate on the side walls of the hole in the form of a recast layer. Due to the rapid rate of cooling of this recast layer, microcracks are often generated. In severe cases, these microcracks may extend into the parent material itself [4]. Few researchers have investigated the formation of the recast layer in an attempt to minimize its thickness, thereby, reducing microcracks on the side walls of laser-drilled holes [5-7]. In Experimental Modeling with conventional methods, one has to run many experiments to set some optimum levels of process parameters for desired performance or Output, which is a cumbersome task Soft computing methods with artificial intelligence tools as Fuzzy logic (FL), Artificial Neural network (ANN) and Genetic Algorithms (GA) provide the solution for the above problem. Prof. Loft Zadeh Introduced the 1,2,3 Department of Mechanical Engineering, MNNIT, Allahabad , U.P., India, ( * rme0952@mnnit.ac.in)

2 56 International Journal of Manufacturing Technology and Industrial Engineering (IJMTIE) concept of soft computing for the first time in These tools provide us the better opportunity to perform modeling and optimization work in laser beam machining (LBM). There is not much work done on fuzzy logic (FL) modeling for laser beam trepan drilling yet. Although it has been applied by some researchers in the field of Conventional machining [8-10]. (a) Percussion Drilling Figure 1: Types of Laser Drilling [5] (b) Trepan Drilling In the present paper, a fuzzy model has been developed to predict the thickness of recast layer in laser trepan drilling of thin Inconel 718 sheets. The fuzzy rule base is formed using the Expert knowledge obtained through the study of previous works on laser trepan drilling. This fuzzy model is developed by using fuzzy tool box of MATLAB. The predictions from the fuzzy model have been compared and validated with the experimental data obtained from referred paper. 2. FUZZY MODELING A fuzzy model (Mamdani type) for the prediction of recast layer thickness in laser trepan drilling of thin sheets of Inconel 718 has been developed. In the development of model three input process parameters such as laser peak power, assist gas pressure and Focal position of laser spot considered. For developing the expert knowledge about the process studies of literature published on similar work were carried out. Figure 2: Block Diagram of Fuzzy Model of Recast layer Thickness

3 Fuzzy Modeling of Recast Layer Formation in Laser Trepan Drilling of Superalloy Sheet Fuzzification As the first step in modeling, all the crisp form Inputs and output variables were Fuzzified with the use of triangular and trapezoidal Membership functions as for different levels of each variable shown in figures 3 and 4 for inputs and output respectively. The inputs with 3 levels, Low (L), Medium (M), High (H) and output with 5 levels (for more accurate output) i. e. Very Small (VS), Small(S), Medium (M), High (H), Very high (VH). Peak power (KW) Assist gas pressure (MPa) Focal position (mm) Figure 3: Membership Functions of Fuzzified Input Variables 2.2. Inference Recast layer thickness (mm) Figure 4: Membership Functions of Fuzzified Output Variable Secondly, in developing inference, the rule bank for the system was based on the experimental data, as mentioned by Chien and Hou [7]. The human expertise was also required to obtain an optimum fuzzy result. Table 1 shows the rule bank for the model. IF THEN logic was employed in the inference to determine the output. The total number of fuzzy rules formed 27 (3 x 3 x 3), as 3 levels, for each input variables were considered. Table 1 Rule Bank Peak power L M H Assist Gas Pressure L M H L M H L M H Focal position L H H M H H M H M S M M H M M M M M M VS H H VH H H H M H M S

4 58 International Journal of Manufacturing Technology and Industrial Engineering (IJMTIE) 2.3. Defuzzification Once the inference part is over we can get the fuzzified output from it, and this fuzzified output can be converted into the crisp value of output by applying centre of area method. We can have the predicted values of output for any particular set of inputs in the similar manner with the help of rule view window of fuzzy tool box of MATLAB. Table 2 Validation Table Data set S No. Experimental Values Fuzzy Predicted values Standard deviation Mean error MODEL VALIDATION The values of recast layer thickness predicted by developed Fuzzy model are quite close to that of observations obtained from the experimental values of referred data as shown in Figure 5. For the validation of the model, eighteen data sets with different input parameter settings as suggested by Chien and Hou [7] were taken into consideration for prediction of recast layer thickness by model. The values of recast layer thickness predicted from model are shown in the Table 2. The standard deviation of predicted values from model is and for experimentation is The mean percentage error of predicted recast layer thickness values with respect to experimental values is which is quite acceptable. Therefore by this comparison it is clear that model is valid for prediction of recast layer thickness. Figure 5: Schematic Plot of Experimental Vs Predicted Recast Layer Thickness

5 Fuzzy Modeling of Recast Layer Formation in Laser Trepan Drilling of Superalloy Sheet CONCLUSION A fuzzy model for prediction of recast layer thickness in laser trepan drilling process has been developed. As the standard deviation of predictions and mean percentage error show that, obtained predictions by developed fuzzy model are very close to the experimental values. Hence this may be concluded that the model shows high level of accuracy and may be considered quite reliable and acceptable to predict the recast layer thickness accurately. REFERENCES [1] Chryssolouris, G., Laser Machining: Theory and Practice, (Mechanical Engineering Series). Springer, New York, (1991). [2] Tam, S. C. Yeo, C. Y. Jana, S. Lau, M. W. S., Optimization of Laser Deep-hole Drilling of Inconel 718 Using the Taguchi Method. Journal of Material Processing Technology, 37, (1993), [3] Ng, G. K. L. Li, L. Repeatability Characteristics of Laser Percussion Drilling of Stainless-steel Sheets. Optics and Lasers in Engineering, 39, (2003), [4] Yao, Y. L., Laser Machining Processes. Columbia University, New York, (2002). [5] Yeo, C. Y. Tam, S. C. Jana, S., A Technical Review of the Laser Drilling of Aerospace Materials. Journal of Material Processing Technology, 42, (1994), [6] Dubey, A. K., Yadava, V., Laser Beam Machining A Review. International Journal of Machine Tools & Manufacture, 48, (2008), [7] Chien, W. and Hou, S., Investigating the Recast Layer Formed during the Laser Trepan Drilling of Inconel 718 Using Taguchi method. International Journal of Advance Manufacturing Technology, 33, (2007), [8] Lin, S. C. and Yang, R. J., Force-based Model for Tool Wear Monitoring in Face Milling, International Journal of Machine Tools and Manufacture, 35, (1995), [9] Wang, Z. Lawrenz, W. Rao, R. B. K. N., Featured Filtered Fuzzy Clustering for Condition Monitoring of Tool Wear. Journal of Intelligent Manufacturing, 7, (1996), [10] Susanto, V. and Chen, J. C., Fuzzy Logic Based In-Process Tool-Wear Monitoring System in Face Milling Operations. International Journal of Advance Manufacturing Technology, 3, (2003),