# Towards Machine Learning for Error Compensation in Additive Manufacturing
## 1 Introduction  
In recent years, Additive Manufacturing (AM) is one of the more recent and advanced manufacturing technologies which is expected to escalate the progress for the next-generation manufacturing industry with lean manufacturing being its main principle. It involves the process of layer-basis additive manufacturing of three-dimensional object using 3D Computer Automated/Aided Design (CAD) models opposing the approaches used in subtractive manufacturing like Computer Numerical Control (CNC) machining. In additive manufacturing, the prefabrication stage aims to enhance the reliability and feasibility of the process. A 3D printable model designed either via CAD or directly from 3D scanned object is presented as triangle meshes usually in Stereolithography (STL) format as an example and then converted into sliced layers with Numerical Control (NC) programming language to instruct the fabrication process. Other 3D representation of a model includes voxels, octree point cloud and graph based [1]. The prefabrication of image-based slicing, path arrangement, orientation, support formation, repairing and packaging can significantly speed up the construction time and reduce both cost and material wastes [2]. By definition, additive manufacturing refers to a process by which digital 3D design data is used to build up a component in layers by depositing material [3,4] according to American Society for Testing and Materials (ASTM), an international standards organization. The process typically uses extruder, binder, laser or electron beam to solidify materials such as polymer, metal, and ceramic [5] one layer at a time. Usually, an extruder wheel or a mobile bed can control the flow rate of fused material [6,7]. Throughout its inception, AM has offered an improved mechanical strength throughout the years with new plastics and alloys being tested out to balance both customisability of a design and the required mechanical properties suited for its applications [8,9]. Figure 1 lists the attributes additive manufacturing offers. One of the major benefits AM could utilise is cloud manufacturing. Cloud manufacturing is the concept of distributing manufacturing capabilities and resources on a cloud platform capable of offering intelligent decisions to deliver the most sustainable and robust manufacturing route available [10]. In the additive manufacturing paradigm, Social Manufacturing (SM) is realised by seamless connectivity with the Internet and logistical networks where prosumers can directly participate in the entire cycle of the production process through crowdsourcing. From an idea proposed in [11], a service oriented networked product development model is proposed in which service consumers are enabled to configure, select, and utilise customised product realisation resources and services ranging from expertise support to pay-as-you-go concept. Lately, AM has been widely used in a list of domains including medical [12,13], materials engineering [14, 15, 16, 17], communication [18,19], pre-fabrication construction [20], aeronautic [21,22], power electronic [23, 24, 25], food [26] and even making its way into the space industry [27,28]. However, the limitations hindering the proliferation of AM still exist in the current manufacturing operation. Excessive time consumption, lack of real-time control, potential deviation, and the transition from mass production to mass real-time customisation are principal problems that require being solved by computational aptitude, widely known known as Artificial Intelligence (AI). AI provides various algorithms and methods as well as offers great potential to transform the current manufacturing technique under the situation of ever-increasing data repository. Machine Learning (ML) for example, opens the possibility for machines to learn and improve autonomously with little or no human intervention at all.