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1.INTRODUCTIONThin-wall metal components as aerospace, medical and health and transportation and other industries of the key slab, with the rapid development of manufacturing and enterprise technology upgrade, rapid prototyping has become the main mode pursued by the market, small batch and quantity of pre-production product specimen demand increases, single-point Incremental forming technology rapidly into people’s attention. However, there are some problems such as excessive thinning rate and uneven thickness of the parts processed by single point Incremental forming technology, so this technology has not been popularized and used. Zhang Litong et al.[1] used the neural network model to effectively predict the relationship between process parameters and thinning rate in two-sided Incremental forming. Shang Miao et al.[2]added static pressure support to the single point Incremental forming and found that the appropriate support pressure is conducive to the thickness strain distribution of the formed parts. Zhao Xinqi et al. [3]proposed a method to change the position of sheet metal in different forming passes, and found that the forming quality of straight wall parts using this method is better than that of traditional multi-pass forming method. Li Yanle et al. [4]predicted and analyzed the maximum thinning rate under different process parameters by establishing a second-order response surface model. Nirala et al. [5] proposed an incremental tool path strategy based on fractal geometry, and through comparative studies, FGBIT tool paths can obtain better formability, stress and thickness distribution. In previous studies, most of the plates were directly formed by single-point Incremental forming technology, which is inefficient for forming multi-feature parts. Therefore, this paper proposed a method of hydraulic expansion of sheet metal first, and then single-point Incremental forming of sheet metal on the basis of expansion. Taguchi method was used to optimize three process parameters that affect the thickness of formed parts. The number of experiments is greatly reduced, which provides a certain reference for the subsequent research. In the existing researches, there are few researches on the wall thickness thinning under the single point Incremental forming process of hydraulic expansion. Therefore, in this paper, 1060 aluminum plate is used to set up the hydraulic expansion single point Incremental forming experiment platform, and the orthogonal experiment is designed by Taguchi method to analyze the influence of different process parameter combinations of tool head diameter, layer spacing and tool head speed on the thinning rate of the workpiece, so as to obtain the optimal parameter combination. 2.HYDRAULIC EXPANSION SINGLE POINT INCREMENTAL FORMING THEORYPoint incremental forming technology, also known as die less Incremental forming, is a forming method that does not require support molds. In this technology, hydraulic expansion is realized by adding hydraulic expansion under the sheet to be formed. As shown in Figure 1, the edge of the plate is rigidly fixed by a professional clamp. The forming tool performs plastic forming on the sheet layer by layer according to the preset forming path, making the thickness of the sheet gradually thin from the initial t0 to tp. The forming Angle between the side wall of the target member and the horizontal line is α. The thickness reduction rate of the plate is used to indicate the degree of change in the thickness of the plate, and the calculation formula is: Where: η is the thinning rate, t0 is the initial plate thickness, t is the plate thickness after forming. 3.NUMERICAL SIMULATION OF INCREMENTAL FORMING3.1Model buildingThe target forming part is a concave and convex pyramid table. 1060AL plate is selected as the part to be formed, and it is formed and fixed by spherical tool head, hydraulic expansion equipment and plate fixture. The spherical tool head and upper and lower splints are set as analytic rigid bodies, and the 1060AL plate is set as three-dimensional deformable shells. The sheet to be formed is a circular sheet with a diameter of 136mm and a thickness of 1mm. The outer ring radius of the upper and lower fixture is 70mm, and the inner ring radius is 50mm. The assembly model is shown in Figure 2. 3.2Verify process feasibilityThe target forming part is a concave and convex pyramid,By selecting the top side length of the forming part is 60mm, the bottom side length is 10mm, the forming depth is 20mm and the forming Angle is 38°. ABAQUS software was used to simulate it, The spherical tool head and upper and lower splints are set as analytic rigid bodies, and the 1060AL plate is set as three-dimensional deformable shells,and the results showed that the plate would not crack during hydraulic expansion and single point Incremental forming, which verified the feasibility of the experiment. The equivalent strain cloud image after simulation expansion was shown in Figure 3, and the cloud image after machining was shown in Figure 4. 4.APPLICATION OF TAGUCHI METHOD IN OPTIMIZING THINNING RATE4.1Introduction to Taguchi methodTaguchi method is a quality engineering method which can reduce the cost by reducing the number of experiments and can quickly find the best parameter combination to improve the efficiency. Orthogonal table and signal-to-noise ratio are the key to Taguchi method. Orthogonal tables are a tool for designing experiments that can be used to look for interactions between different factors. It is characterized by “uniform dispersion, neatly comparable”, which means that the number of different numbers in each column of the orthogonal table is the same, and the number of times each combination appears is equal in any two columns of the horizontal combination of numbers, so that all levels of all different factors have the same probability of appearing in the experiment. Therefore, using this method to design the experiment to make it representative, not only can reflect the influence of all factors and all levels on the object of concern, but also can greatly reduce the number of experiments. Using the signal to noise ratio to measure the robustness of the quality of the parts, through the statistics and analysis of the experimental data of each group, the best parameter combination with strong anti-interference ability, good adjustment and stable performance is found to improve the product quality. 4.2Experimental conditionA machine tool. Qinchuan MVC510 vertical machining center with FANUC-0i system. Hydraulic expansion equipment. Including oil tank, hydraulic pump, pressure gauge, relief valve, check valve, sealing ring. The tool head. Precision K15, machined from WC-Co tungsten steel and coated with ALoCa. The workpiece. Round AL1060 alloy plate, thickness 1mm, radius 68mm. Measuring instruments. Hexagon IRP40.02 infrared photoelectric probe. 4.3.Selective signal factorDr. Taguchi divided the factors that affect part quality in actual production into input variable W, controllable factor X and uncontrollable noise factor Z. When the Taguchi method is used to solve the problem, generally only the controllable factors are designed, and the input variables are not designed. Different factors and different levels are combined to obtain the level combination that can produce the optimal performance and reduce the sensitivity of the product to the noise factor. The three controllable factors selected in this subexperiment are tool head diameter, layer feed and tool head speed. In the process of processing, the degree of change of temperature and humidity can not be predicted, so it also has a certain impact on the forming quality of the parts. These two indicators are regarded as the noise factors in this experiment. In order to reduce the experimental error, each group of experiments designed is repeated twice, and the average thinning rate measured in the two experiments is taken as the final result. The controllable factors and levels are shown in Table 1. Table 1.Controllable factors and levels.
4.4.Main experiment4.4.1Select orthogonal tables and experimentsOrthonormal table was selected as the experimental scheme, as shown in Table 2 Table 2.Experimental Design.
4.4.2Calculate the thinning rate and S/NDr. Taguchi uses the energy ratio of signal to noise, known as the signal-to-noise ratio (S/N), as an indicator to measure the degree of noise interference in the production process. The larger the ratio, the lower the noise interference of the product, and thus the higher the quality of the product. In the evaluation of basic quantitative qualities, characteristics are usually divided into static and dynamic categories. Static characteristics are further subdivided into high and low expectations And visual characteristics. This paper focuses on the thinning rate and adopts the static hope property as the evaluation criterion, that is, the lower the thinning rate, the better the quality. The mass loss function of the desired characteristic is shown in Equation 2. The S/N of a small feature is shown in Formula 3. Where: K is the mass loss coefficient, K≥1; n is the total number of measurements and yi is the reduction rate measured at the i time. 4.5Result analysisEach group of experiments was performed twice, and the average of the data obtained from these two experiments was taken as the final result. In this paper, the thickness of the plate was measured at five points 41, 44, 47, 50 and 56mm away from the edge of the plate along the X-axis of the center of the plate and the thinning rate was calculated. The SNR is calculated by Minitab22 software. The results of the mean thinning rate and signal-to-noise ratio of the forming area are shown in Table 3. Table 3.Results of Mean Residual Stress and SNR Calculation.
This time, we only pay attention to the main effects of three process parameters, namely tool head diameter, layer feed and tool head speed, on the response, excluding the interaction between them. The response table of various factors obtained by Minitab22 to the mean value and SNR is shown in Table 4 Table 4.Thinning Rate Mean and SNR Response Table.
The rank of S/N can be used to determine the process parameters that affect the radial residual compressive stress, that is, the tool head has the greatest influence on the response, followed by the layer feed and the tool head speed. The response graphs of various factors obtained by Minitab22 to the mean thinning rate and the mean signal-to-noise ratio are shown in FIG. 5 and 6 respectively. The average main effect plot reveals the influence trend of forming parameters on the result variables. If the slope of the line in the main effect plot is close to zero, this indicates that the parameter has little effect on the resulting variable; A non-zero slope means that the parameter has a significant effect on the resulting variable. According to the chart analysis, we can see that with the increase of the feed rate of each layer and the speed of the tool head, the thickness thinning first decreases and then increases, while with the increase of the diameter of the tool head, the thinning rate shows a downward trend. The chart clearly shows how different parameter Settings affect the signal-to-noise ratio (S/N). By taking into account the high signal-to-noise ratio parameter Settings and the low expected value of the resulting variable, we can determine the optimal combination of parameters to optimize the residual compressive stress of the workpiece: using a tool head with a diameter of 14mm, a feed rate of 1.5mm per layer, and a speed of 1500 rpm. 5.CONCLUSIONBased on hydraulic expansion single point Incremental forming technology, Taguchi method was used to study the effect of tool head diameter, layer feed rate and tool head speed on the thinning rate of formed parts. In the range of factors and parameters set in this paper, the main order of influence on the thinning rate of formed parts is tool head diameter D, layer feed rate Z and tool head speed R. The optimal parameter combination is D=14mm, Z=1.5mm, R=1500r/min. The diameter of the tool head is significant to the thinning rate of the formed part. In the subsequent study, other factors (such as plate thickness, forming trajectory, hydraulic support, forming Angle) can also be considered to optimize the thinning rate. 6.ACKNOWLEDGMENTThis work is supported by the National Natural Science Foundation of China under Grant No. 52075437, China. REFERENCESZhang L T and Yu G P,
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