Papers by Author: Xiao Jiang Cai

Abstract: Carbon fiber reinforced plastics (CFRP) has increasing applications in aerospace and other fields, due to low density, high strength, high stiffness, great resistance to corrosion, etc. Although, delamination damages in drilling holes for assembly influence the final characteristics of CFRP components. This paper presents an experimental investigation to analyze delamination damage, in which acoustic emission and thrust force are monitored during drilling CFRP laminates to clarify the relationship between AE signals and delamination damages. The results show that delamination damage has close correlation with thrust force and acoustic emission energy. AE root mean square (rms) is recommended to be selected as AE signal parameter. Abrupt peak feature of AE rms can be used as a dependable trigger for delamination monitoring. The number of abrupt pulses of AE rms can be counted online to predict the degree of delamination damages, based on which delaminations can be monitored and controlled online.

Abstract: High-strength carbon fiber reinforced plastic (CFRP) T800S/250F is used as the large commercial aircraft material for manufacturing the main load-bearing structural components. Drilling is the mostly used in final machining process of CFRP laminates, while the delamination and burrs occur frequently at the drill exit in the CFRP laminate. In this paper, the machinability of T800S/250F was investigated in term of drilling force and hole quality by using a twist drill and a dagger drill. The experimental results indicated that high spindle speed and low feed rate favor the reduction of thrust force for both drill bits. High spindle speed is a preference to gain the good hole quality at drill exit especially for the dagger drill, which also shows excellent drilling performance than the twist drill and was more suitable for drilling of T800S/250F CFRP laminate.

Abstract: Since metal matrix composites (MMCs) have increasing applications in industries, this paper presents an experimental investigation on machinability of SiC_p reinforced aluminium metal matrix composites. 14 wt.% of SiC_p reinforcement addition composite was studied in end milling using CVD coated carbide tools under different cutting parameters. By experimental results, the relationships of cutting force and surface roughness with cutting speed and feed were discussed. Some defects concerning surface topography such as ploughed furrow, pits, matrix tearing, etc. were examined by SEM.

Abstract: nconel 718 is a typical difficult-to-machine material, and its high speed end milling process has wide applications in manufacturing parts for aerospace and power industry. Surface integrity of these parts greatly influences the final characteristics. This paper presents an experimental investigation to evaluate surface integrity behaviors of Inconel 718 with finishing cutting parameters in terms of surface topography, surface roughness Ra, residual stresses and subsurface microstructure and microhardness. The results show that high cutting speed is advisable to get better surface topography and roughness. Residual stresses and subsurface microhardness barely increase after 80m/min. Microstructure in surface layer has only slight deformation after high speed milling.

Abstract: As two kinds of advanced titanium alloys, TC18 and TA19 were introduced in this paper. The machinabilities of TC18 and TA19 alloys were described in the grinding process. Grinding experiments were completed using green silicon carbide grinding wheel with the coarser 100 grit. Grinding forces and specific energy in surface grinding were investigated. And then, for studying the grinding characteristic, SEM images of the workpiece material were obtained. The results indicated that specific chip formation had the great effect on the mechanism of grinding TC18 and TA19 alloys, and the scratch was the main characteristic of surface grinding. TC18 alloy had the poor grinding performance compared to TA19 alloy.

Abstract: Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.