Deep Learning in Product Manufacturing Record System

Since the 21st century, the new generation of industrial revolution, mainly characterized by intelligence, information technology and automation, namely Industrial Revolution 4.0, has emerged and opened the curtain of the “Industry 4.0” era [1]. With the improvement of product quality and safety in the modern industrial production process, increasing amounts of attention is being paid to the quality control of the whole process of product production. In order to improve product quality and production efficiency, enterprises trace the quality of their products, pay attention to the use of products, and improve and control the existing product quality [2]. In recent years, with the development of manufacturing technology and increased supervision, the probability of accidents in industrial products is also decreasing year by year, and the overall safety level is gradually increasing [3-4].

The product production record system records production record data from processing to packaging, provides comprehensive analysis of product production data, and establishes a production record system that focuses on the production process. In the intelligent manufacturing mode, the operator uses the code scanning gun to enter the relevant data of the product. It greatly improves the efficiency of production data collection and facilitates the improvement of product production efficiency. Deep learning has taken off in recent years, making major breakthroughs not only in medical research, energy consumption in life, finance and communications. Features such as face recognition, speech processing and video object detection are derived based on algorithms and extensive training in deep learning. The learned features extracted through different deep networks perform well in prediction. Therefore, learning deep features for prediction is becoming Getting more and more popular. In this paper, inspection records in product manufacturing system are used as training samples, CNN, STACK LSTM, GRU, INCEPTION, ConvLSTM and CasualLSTM techniques are used to design network models and to study the processing of temporal data, after comparing and selecting a comprehensive and high network model to predict the results of the product to be inspected. According to the prediction results, the model can correct the operation of the workshop technicians who do not regulate the debugging of products in advance, and largely improve the efficiency of product testing at the early stage of production.

A wide variety of time series data exists for various industries, financial time series, electricity consumption time series, coal price time series. It is of great value and significance to spend effort on time series data to study and analyze them. Lots of work has been done by domestic and foreign scholars on time series data analysis and forecasting.

RNN is a traditional recurrent neural network [5], which is a model for processing sequential data. The traditional RNN in solving the association between long sequences, through practice, proved that the classical RNN performs poorly, the reason is that when backpropagation is performed, too long sequences lead to abnormal computation of gradients, and gradient disappearance or explosion occurs. LSTM [6] long short-term memory model is a special kind of RNN neural network and effectively deal with long-term dependence problem, it is compared with RNN in two aspects to do improvement The LSTM gating mechanism has three main gates which are forgetting gate, input gate and output gate respectively. The equation of LSTM network structure at moment t is shown below, f_t, i_t, o_t, C_t are forgetting gate, input gate, output gate and cell state respectively, W_t is each gating weight parameter, b_t is each gating bias parameter respectively, σ are sigmoid activation function, and tanh is the hyperbolic tangent activation function.

(1) { f t = σ ( W f [ h t − 1 , x t ] + b f ) i t = σ ( W i [ h t − 1 , x t ] + b i ) C ~ t = tanh ⁡ ( W c [ h t − 1 , x t ] + b c ) o t = σ ( W o [ h t − 1 , x t ] + b o ) h t = o t ⋅ tanh ⁡ ( C t ) C t = f t ⋅ C t − 1 + i t ⋅ C ~

LSTM has the dominant advantage for the prediction of temporal data, but it is not effective in predicting data in spatio-temporal sequences, does not consider spatial correlation and carries redundancy, and cannot portray local features due to the strong local characteristics of spatial data. The ConvLSTM network structure was first proposed in 2015 for precipitation proximity prediction [7], and the experimental results found that ConvLSTM grasps the data spatio-temporal structure, and also proved that ConvLSTM works better than LSTM in obtaining spatio-temporal relationships. The core of ConvLSTM network structure is consistent with LSTM, which also takes the output information of the previous layer as the input information of the next layer. The difference is that the W-weight full link operation is changed to convolution operation, which not only can get the spatio-temporal relationships, but also can extract spatial features like convolution layer thus being able to obtain spatio-temporal features.

GRU [8] is a variant of LSTM network, it inherits the advantages of LSTM network and has a simple structure, LSTM has three gates are forget gate, input gate and output gate, while GRU has only two gates are update gate and reset gate, for memory information transfer, LSTM is passed to the next unit through the output gate, GRU is directly transferred to the next unit without control. t moment GRU unit structure of the equation is shown below, r_t, z_t, h^~ _t are reset gate, update gate and memory transfer information respectively.

(2) { r t = σ ( W r ⋅ [ h t − 1 , x t ] ) z t = σ ( W z ⋅ [ h t − 1 , x t ] ) h ~ t = tanh ⁡ ( W ⋅ [ r t ∗ h t − 1 , x t ] ) h t = ( 1 − z t ) ∗ h t − 1 + z t ∗ h ~ t

In conclusion, this article propose a research method based on deep learning for product inspection record prediction and design three network models to predict the dataset, each model will have different advantages between both the results and the process, and INCEPTION-GRU network model is considered comprehensively from all aspects as a product inspection record prediction model to provide technical support for new product detection. After studying the three network models, it is found that ConvLSTM is not as stable and efficient as LSTM and GRU in processing temporal data. Future work will focus on improving the GRU model to further improve the accuracy of product inspection pass rate.