# Using Intelligent Edge Devices for Predictive Maintenance on Injection Molds +
## 1 Introduction  
One-third of the global plastics production is processed by injection molding [1], making injection molding machines (IMMs) widely used industrial assets. Figure 1 depicts the main components of a typical IMM. During the injection process, plastic pallets stored in a feed hopper are melted along the barrel by heating bands and injected via pressure into a mold cavity filled with plastic material. After solidifying, the material is ejected from the mold cavity, producing a three-dimensional part. The complexity of the injection molding process makes the maintenance of IMMs and their components a challenge [2], which has particular importance when it is acknowledged that expenses related to maintenance are typically a considerable part of an enterprises' total expenses [3]. For this reason, adopting sensors to monitor IMMs and their components is very common, especially concerning injection molding [4, 5], as they are a key component of the injection process [6]. Injection molds are subjected to degradation due to repeated use, high temperatures, pressure, and contact with melted materials, which may cause issues including such as surface degradation, dimensional changes, or misalignment. These issues may be time-consuming, leading to high periods of unavailability, or may be easily solved without a major intervention. Scheduling maintenance actions for these components is challenging,as the time between two failures does not follow a pattern, and requires balancing the mold's performance with the expected maintenance costs. Furthermore, keeping detailed records of mold maintenance activities, including repairs, modifications, and maintenance history is essential for maintenance planning; however, this can be challenging in large-scale manufacturing. There are several strategies to handle maintenance actions, such as corrective maintenance (CM), preventive maintenance, condition-based maintenance (CBM) [7], and predictive maintenance (PdM). CM assumes that only corrective actions are taken, i.e., maintenance is carried out only when there is a failure that forces the equipment to stop and it cannot work anymore without an intervention. This strategy may be useful when it is used in components that are easily and quickly replaceable, and when failure does not cause other malfunctions in the system. Otherwise, severe failures may occur, which can be costly and cause long periods of unavailability. PM intends to schedule proactive maintenance actions, usually in equally spaced intervals of time. However, this strategy may encounter two issues: (1) the asset's lifetime may be under-exploited if the time between interventions is too short; and (2) if this time is too long, the same issues are encountered as in the CM case. When there are components in the system that need to be replaced due to wear and for which the lifetime follows a well-defined pattern, PM may be successfully implemented; however, this scenario only occurs in controlled environments where the components subjected to wear spend their entire working lifecycles under the same working conditions, which is not the typical scenario in real industrial environments. According to [8], more than 30% of maintenance costs are derived from poor maintenance scheduling. For this reason, PdM, and CBM strategies have received increasing attention, as they take into account the actual degradation state of the assets. While CBM usually relies on monitoring and analysis of the relevant parameters of an industrial process, PdM additionally exploits analytics techniques to improve maintenance decisions. The adoption of optimal maintenance strategies is crucial for enterprises, and proactive actions may not be the most suitable strategy for all scenarios; when a given failure is very simple to solve, spending time and resources on proactive actions may be unnecessary. Reliability-centered maintenance (RCM) is considered a type of PM [9].