Optimizing the High-Performance Milling of Thin Aluminum Alloy Plates Using the Taguchi Method

Abstract

Most aerospace parts are thin walled and made of aluminum or titanium alloy that is machined to the required shape and dimensions. Deformation is a common issue. Although the reduced cutting forces used in high-speed milling generate low residual stress, the problem of deformation cannot be completely resolved. In this work, we emphasized that choosing the correct cutting parameters and machining techniques could increase the cutting performance and surface quality and reduce the deformation of thin plates. In this study, a part made of a thin 6061 aluminum alloy plate was machined by high-speed milling (HSM), and a Taguchi L16 orthogonal array was used to optimize the following parameters: linear velocity, feed per tooth, cutting depth, cutting width, and toolpath. The impact of cutting parameters on the degree of deformation, surface roughness, as well as the cutting force on the thin plate were all investigated. The results showed that the experimental parameters for the optimal degree of deformation were A1 (linear velocity 450 mm/min), B1 (feed per tooth 0.06 mm/tooth), C1 (cutting depth 0.3 mm), D4 (cutting width 70%), and E4 (rough zigzag). Feed per tooth was the most significant control factor, with a contribution as high as 63.5%. It should also be mentioned that, according to the factor response of deformation, there was a lower value of feed per tooth and less deformation. Furthermore, the feed per tooth and the cutting depth decreased and the surface roughness increased. The cutting force rose or fell with an increase or decrease of cutting depth.

Single neuron PID based method for deformation suppression during CNC machining of parts

“…In the process of CNC machine tool machining, the cutting process of thin‐walled parts is relatively complex, and the vibration in the cutting process has a great impact on their surface quality and machining efficiency, which will directly damage the machine tool and cause irreparable economic losses in serious cases. However, with the increasing demand for the processing technology of such parts, the optimization can be transformed into multi‐objective optimization based on processing quality and efficiency 19 . The single neuron algorithm of fuzzy control is to adjust the parameters in PID, while the NC machining process of parts is uncertain and random.…”

“…However, with the increasing demand for the processing technology of such parts, the optimization can be transformed into multi-objective optimization based on processing quality and efficiency. 19 The single neuron algorithm of fuzzy control is to adjust the parameters in PID, while the NC machining process of parts is uncertain and random. By optimizing the parameters of thin-wall machining process, the deformation of thin-walled parts in NC machining can be restrained.…”

Numerical simulation and tool parameters optimization of aluminum alloy transmission intermediate shell

“…MATLAB software was used to carry out dual-objective optimization, firefly algorithm in the algorithm toolbox was called, and various parameters were set 44 . In order to avoid overtraining in the algorithm iteration process, the number of iterations is set to 2000, the initial population number is 200, and the constraint conditions for tool parameters optimization are as follows: b 01 = [0.05,0.20], r 01 = [-20,-5], γ 0 = [6,15], α 0 = [15.,24]. Typical data selection is shown in Fig.…”

“…It has the advantages listed above as well as good formability, weldability, and excellent machinability 5 . It is widely used in the middle shell of the transmission with specific precision requirements and high corrosion resistance 6 . 6061 Aluminum alloy is a common material that is challenging to manufacture.…”

A Comparison of the Geometrical Accuracy of Thin-Walled Elements Made of Different Aluminum Alloys

“…The value of the cutting force also depends on the properties of the workpiece material, as described in this study, using selected aluminum alloys as an example. In the case of using HSC and HPC technologies for the production of pocket thin-walled elements from monolithic rolled plates, the chips sometimes constitute even more than 95% of the semifinished product weight [ 37 , 38 , 39 ]. The optimalization of machining process in aspect of its efficiency depends on the use of machining fluids.…”