# EMP: Extended Kalman Filter Based Self-Adaptive Mold Protection Method on a Toggle Mechanism
## 1 Introduction  
Concepts of intelligent manufacturing [1], cyber-physical systems [2], etc., are widespread. Large equipment (e.g., CNC machining centers and injection molding machines) has crucial roles in these concepts, claiming many researchers' interests [3; 4; 5] because of the equipment's systematic complexity and costliness. The injection molding machine (\(IMM\)) is a typical piece of large equipment, and the importance of \(IMM\)s has been ever-growing. \(IMM\)s produce plastic products occupying the main plastic market. Nowadays, \(IMM\)s tend toward high speed, precise control, and low energy consumption and noise. Many studies primarily focus on the control of robustness [6; 7; 8; 9; 10], trajectory planning [11; 12; 13], time optimization [14], and energy consumption [15; 16; 17]. Hence, the performance of \(IMM\)s has been enormously improved. Meanwhile, the mold protection (\(MP\)) issue is also serious. Figure 1 provides an example, where the toggle mechanism (\(TM\)) closes with the mold in less than two seconds and under high pressure. If there are some foreign bodies in the mold without a protection mechanism, the mold will be damaged directly. This problem always frustrates operators because of the high maintenance price and time-consuming maintenance process. At present, mold protection has attracted many researchers. One research direction is based on machine vision through comparing safe and unsafe situations to detect the foreign bodies. [PERSON] et al. [18] presented a method that applied technologies, such as near infrared illumination technology, the background updating algorithm, and the region of interest, to fulfill \(MP\). [PERSON] et al. [19] applied key technologies, including the methods for pretreatment of the cavity image, template matching of the cavity image, and cross-correlation of an abnormal cavity situation, into \(MP\). [PERSON] et al. [20] proposed a system that was able to correctly detect the unsafe in-mold status of the stuck ejectors or pieces by the two approaches of offset correction and dynamic safe range learning. However, the above methods need to add a special vision system. In some high speed cases, processing speed also becomes the issue, and for getting a good effect from image processing, much effort should also be made to implement a customized visual program. Other improving methods are to avoid failed production based on processing data and mathematical models to reduce the possibility of the entrance of foreign bodies. Based on the monitoring data, [PERSON] et al. [21] proposed an approach for fault detection in a thermoplastic injection molding process to assure a high level of quality control over the molded parts using neural networks. The properties in injection molding can also be controlled by neural networks in [22]. A study in [23] monitored the quality of molded parts in a process from the design and processing points of view. [PERSON] et al. [24] also proposed an approach of support vector machines for quality monitoring in a plastic injection molding process to monitor in-process data and thereby the unexpected process interferences could be responded quickly. These methods are good auxiliary but not the thorough ones for \(MP\). IMM controller venders (e.g., Technation and KEBA) also have their own \(MP\) methods. Those methods just compare electric current (\(EC\)) profiles simply, which need to set many parameters (e.g., \(MP\) distance, upper deviation, lower deviation, left deviation, and right deviation). Using those methods, operators always take a long time adjusting parameters, and it is still possible that the mold will be damaged because of misjudgment. To address the issue, we propose an extended Kalman filter based self-adaptive method. The contributions of this work are as follows:  
1. The mathematical model of the \(TM\) driven by servo systems (\(SS\)s) is built used for the process analysis and further researches. 2.