# Towards Machine Learning for Error Compensation in Additive Manufacturing
## 1 Introduction
Applying ML in manufacturing allows the system to derive the useful information from existing data sets, which will provide a basis for approximations or predictions to operate machines with future behaviours such  
Figure 1: Prospects of additive manufacturing. as decision-making and automatic system improvement. It is also beneficial to detect certain patterns or explore regularities in a dynamic manufacturing environment [29]. Many authors have invoked the notion of the process to be called a disruptive technology that will inevitably influence the majority of the established production processes [5; 30; 31; 32], and [33]. AM has a potential to reduce production cost by more than USD 170 billion and reduce carbon footprint by 130.5 Mt [34]. It is also estimated that AM could generate up to USD 200-USD 600 billion in transaction by 2025 [35]. General Electric has announced significant investment over a billion dollars in metal AM for jet engine components of aircrafts with one specific advantage being their future 3D printed jet engine fuel nozzle can improve the efficiency of their jet engines by increasing the allowable combustion temperature [36; 37]. Other companies like Airbus are 3D printing bracket for use in aircraft such as a cargo door bracket [38]. The flexibility of 3D printing lets these companies achieve the same function at a lighter weight which in-turn reducing the use of fuel in aircrafts [21]. AM is also used in the entertainment industry to create special effect [39]. In fact, Hollywood was one of the earliest users of MIT's 3D printer for the creation of the replicas of the Aston Martin DB7. On a more personal application in medical devices, a study that was published in the New England Journal of Medicine, where doctors and engineers at the University of Michigan collaborated and 3D printed an implant for an infant with a collapsed airway which was printed using a biocompatible material and was strapped around the infant's airway [40]. There are also great applications tooling wise, where AM is not making parts but instead is improving the performance of tooling, for instance, enabling conformal cooling channels close to the surface of an injection mould that would result in improved part quality by reducing cycle time and minimising shrinking [41; 42]. Table 1 describes the main additive manufacturing types with their respective attributes. In this paper, we aim to provide a review on the prospect of how AI application will provide a sustainable improvement in AM from various aspects and phases of this manufacturing technique. The terms additive manufacturing and 3D printing have been colloquially used interchangeably. Nevertheless, AM is the more broad and all-inclusive term in the industrial or manufacturing field. AM also reflects large-scale production of components while 3D  
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printing is commonly referred to anything that is ornate be it functional or non-functional, though no standards have yet to be established regarding this matter. The structure of this paper is as follows: the overview of different approaches in manufacturing and the advancement in additive manufacturing is presented in Section 2 while Section 3 gives the background of data-driven modelling in additive manufacturing. Section 4 presents the implemented approach of the recent works of error generation and compensation in AM as well as the performance metrics whenever available. Section 5 discussed the deep learning for error compensation as well as the future prospects in the application domain followed by the conclusion in Section 6.