## 5 Conclusions and Discussion  
Although the rain flow extrapolation method can extrapolate the load frequency, it does not complete the two-way extrapolation of load and frequency and does not accurately predict or even ignore the extreme load that may have a great impact on the fatigue life in the whole life cycle of 5 MN metal extruder. At the same time, due to the randomness of the test load, the load data measured in each test are different. For irregular load information, simply using the basic distribution function to describe its characteristics must have errors. Therefore, we must try to fit with more distributions or mixed distributions in the extrapolation process. The closer the degree of fitting is, the more accurate the extrapolation result will be, but it will also face serious parameter estimation and calculation problems and strong human factor constraints.  
LSTM model essentially belongs to the category of time domain extrapolation. With the increase of the number of products, the frequent change of working objects and the renewal of design and manufacturing technology, the load information is also very complex. Time domain extrapolation directly extrapolates the tested load sequence without a series of transformations, so the generated cyclic sequence is more real. The method reduces the error accumulation caused by the calculation of multiple links, and can generate new extreme value data to better predict the service life of the system or parts. The establishment of time domain extrapolation method makes up for the uncertainty of extrapolation distribution function fitting in the past and avoids the existence of subjectivity.  
Another advantage of time domain extrapolation is that the result retains the sequence of load cycles, and the extrapolated load of any mileage can also be obtained. The time domain sequence can be obtained only by applying Markov chain model transformation to the extrapolation result of rain flow.  
Load extrapolation is a key step in the process of compiling the load spectrum, and the accuracy of the extrapolated data directly determines the validity of the load spectrum. This paper proposes the use of LSTM method to extrapolate the load spectrum of the 5 MN metal extruder, and uses the advantage of LSTM to learn the long-distance time series dependence to predict the trend of the load data. The model was verified with actual load data, and compared the short-term load spectrum compiled by the forecast data and the rain flow extrapolation method. The results show that the proposed method improves the reliability of the load spectrum and has great potential in engineering applications. In the  
Figure 10: Eight-level load spectrum frequency distribution.  
following research, the LSTM model can be improved to further improve the prediction accuracy of the model.  
Writing--original draft preparation, T.H.; methodology, T.H.; software, T.H. and X.Z.; writing--review and editing, P.Y.; data curation, X.C.; resources, X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the State Key Laboratory of Metal Extrusion and Forging Equipment Technology of China.  
Not applicable.  
The data provided by this research can be obtained from T.H.  
The authors declare no conflict of interest.