Prediction of polishing roughness of aluminum alloy hard anodized film based on BP and GA-BP neural network

2.1. Test device

The aluminum alloy hard oxide film polishing test was carried out on a CM6140A precision ordinary lathe. The spindle can realize 24-level speed regulation between 10 and 1400 r/min, the power of the spindle motor is 7.5 kW, and the maximum processing diameter×length: is Φ210×900 mm. The polishing test system is shown in Fig. 1. One end of the aluminum alloy hard oxide film polishing test piece is clamped on the chuck of the ordinary lathe through the mandrel, and the other end is clamped by the tip of the tailstock of the ordinary lathe. Mounted on a lathe tool holder. During the test, the rotation of the lathe spindle drives the workpiece to rotate, and the adjustment of the tool holder drives the relative position of the polishing tool and the test piece.

Fig. 1Schematic diagram of polishing test system: 1 – machine chuck; 2 – test piece; 3 – polishing device

The polishing force measurement system includes a hand-held digital liquid crystal meter MCK-HY instrument sensor and a JLBM pull rod capsule-type tension pressure sensor to realize the measurement of polishing force. The size of the aluminum alloy hard oxide film polishing test piece used in the test is: Φ40 mm×350 mm, the substrate is 2D70 aluminum alloy, the surface layer is a hard anodic oxide film, the thickness is (60-70) μm, and its physical and mechanical properties are shown in Table 1.

2.2. Experimental design

The experimental variables are polishing force $F$, polishing times $N$, feed rate $f$, and sandpaper particle size $n$, and the polishing speed $v$ has little influence on the polishing roughness of the hard anodized film in other previous studies, so No longer used as a test factor.

The surface roughness of the polished aluminum alloy hard oxide film was measured by the SJ5730 large-scale roughness profiler produced by Shenzhen Zhongtu Instrument Co., Ltd. The measurement accuracy of the $Ra$ value was 0.001 μm, and the polished area was randomly measured 8 times. Take the average value after the maximum and minimum values as the measurement result. After the aluminum alloy parts were hard anodized, 92 test pieces with a surface initial roughness $Ra$ of around 0.366±0.01 were selected.

2.3. Test results

Some test results are shown in Table 3.

3.1. Prediction model of polished surface roughness of hard anodized film based on BP neural network

The prediction model of polished surface roughness of hard anodized film based on the BP neural network adopts a three-layer network structure, including input layer, hidden layer and output layer. The four parameters of polishing force $F$, polishing times $N$, feed rate $f$, and sandpaper particle size $n$ are used as input samples, so the input layer node is 4, and the hard anodized film polishing surface roughness is used as the output sample, so the output layer node is 1, according to the empirical Eq. (1) and error calculation results, the number of hidden layer nodes is finally determined to be 12, that is, the structure of the BP neural network is 4-12-1:

Fig. 2, $w_{ij}$ is the weight between the input layer and the hidden layer nodes of the BP neural network structure, $w_{jk}$ is the weight between the input layer and the hidden layer nodes, and the transfer function of the hidden layer of the BP neural network structure uses the tanh function $f\left(x\right)=\frac{2}{1+e^{-2x}}-1$. The linear function Pureline is used as the transfer function of the output layer $f\left(x\right)=\frac{1}{1+e^{-x}}$. The trainlm function is used as the training function. The sigmoid function is used as the hidden layer activation function $f\left(x\right)=kx$.

Fig. 2BP neural network structure

The BP neural network prediction model was established using Pycharm software, the training target error was set to 0.001, and the training times were set to 1000. Through the 92 groups of data samples of the aluminum alloy hard oxide film polishing test, 76 groups were selected as training samples, and the remaining 16 groups were used as verification samples, which were normalized according to Eq. (2):

Among them, $X_j$ is the normalized value, $X_i$ is the initial test data, $X_{max}$ is the maximum value in the initial test data, and $X_{min}$ is the minimum value in the initial test data.

3.2. Prediction model of polished surface roughness of hard anodized film based on GA-BP neural network

The GA-BP neural network is composed of three parts. Firstly, the topology structure of the neural network is determined through the polishing parameters of the aluminum alloy hard anodic oxide film; then, the individual is selected through the cross mutation in the genetic algorithm, and the optimized individual is selected; finally Decode the selected individuals to obtain their respective parameters, and use the data to train the BP neural network to obtain a prediction model, as shown in the Fig. 3.

Use Pycharm software to build a GA-BP neural network prediction model, use the newff function to build a GA-BP neural network, initialize the population, and decode to get the initial weight and threshold. The genetic algorithm parameters are set, the individual crossover probability is 0.1, the mutation probability is 0.1, the evolution algebra is 50 generations, and the population size is 150. Use the code function to encode variables to initialize the population, use the test function to determine whether the threshold and weight are out of range, and then use the fun function to calculate the population fitness to obtain the population initialization information. The Select function is used to select, and the optimal individual is obtained through cross-mutation. The optimal individual decoding operation is obtained to obtain the optimal parameters of the aluminum alloy hard anodized film polishing roughness prediction model. The BP network is trained through these parameter data, and finally a relatively accurate prediction result is obtained.

Fig. 3GA-BP neural network flow chart