# Research on Temperature Compensation Method in Crankshaft Online Measurement System
## 1 Introduction  
Measurement is the important link in the manufacturing process. The automobile manufacturing industry produces tens of thousands of shaft parts every year, so the measurement task is heavy. The shaft parts can only be sampled in the measurement room, and the offline measurement efficiency is low. To ensure the quality of parts, a full inspection must be achieved. Online measurement is an effective solution to achieve full inspection of parts [1, 2, 3]. While ensuring the measurement speed, it also guarantees the measurement accuracy. High-precision and high-quality demand are increasing in industry, so that accurate measurements are required. During the measurement of the crankshaft, the temperature changes will cause the crankshaft to expand and contract, will also affect the measuring precision of the sensor, so that a great error is brought to the measurement results. Therefore, it is necessary to perform error analysis and optimization on the sensor and the crankshaft measuring machine, to achieve the temperature compensation of measurement. At present, a temperature compensation method is introduced into Italian MARPOSS crankshaft final inspection machine (online measuring machine). [PERSON] [4] analyzes temperature compensation of a MARPOSS crankshaft terminal inspection machine by a security source, the standard temperature in the qualification judgment criterion of the crankshaft journal is not introduced into the temperature error compensation method, the size of a workpiece at the standard temperature cannot be directly obtained, and size conversion is still needed after compensation. When the temperature compensation coefficient is obtained, the temperature curve is approximately corrected to be a straight line, the crankshaft journal is a cylinder, the thermal expansion coefficient of the diameter parameter of the crankshaft journal is not linear, and the correction method is easy to introduce errors. And the variation range of the temperature of the workpiece and the environment is limited, and the error compensation under various temperature conditions can not be realized. [PERSON] [5] developes a compensation algorithm, taking fluctuation of the ambient temperature into measurement system. The errors caused by the temperature of workbench and workpiece to the measurement results are respectively analyzed. But temperature drift nature of displacement sensor is ignored. [PERSON] [6] put forward a concept of temperature compensation factor to compensate measurement error caused by the difference between calibration temperature and environment temperature. However, this compensation method is applied in rail gauge measurement, does not consider the influence of temperature on other factors. Regarding the influence of temperature on the sensor, different studies have carried out to enhance the capabilities of displacement sensor to obtain more accurate measurement results [7; 8; 9]. For the temperature compensation of the displacement sensor, there are circuit hardware compensation and software algorithm compensation. Artificial neural network (ANN) is widely used because of its self-learning function [10]. [PERSON] [11] proposed a modified BP neural network and a new method of grouping temperature variables based on fuzzy cluster theory. Many researchers combine genetic algorithms, particle swarm algorithms, and difference algorithms to optimize BP to speed up convergence and improve robustness [12; 13; 14]. Dragonfly Algorithm (DA) is proposed by [PERSON] [15] in 2016.The results of DA compared with PSO and GA has been verified by [PERSON]. High exploitation assists the DA algorithm to rapidly converge towards the global optimum and exploit it accurately. [PERSON] [16] propose the use of DA as an optimization algorithm for the weights of each connection between the ANN neurons in a model called ANN-DA, which compared with ANN-ABC, proved its high efficiency and robustness.