# Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products
## 2 Literature Review  
Given that 3D printing is still a relatively new technology in the manufacturing sector, there is limited literature addressing quality issues with 3D printing. [PERSON] et al. have stated the different errors that occurs, such as clogging of the nozzle, adhesion problem, vibration or shocks, misalignment of the print platform, etc., which causes loss of material, time, and money [8]. [PERSON] and [PERSON] stated the severity of warpage \begin{table}
\begin{tabular}{c c c c} \hline
**Sr No.** & **Defect** & **Cause** & **Effect** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Warping [5]} & Improper cooling of the printed component or due to materials used in the process & The printed part swells upward, resulting in a change in the shape of the component. \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{Blistering} & \multirow{2}{*}{Due to improper cooling of lower layers.} & A lower layer of printed component \\  & & & swells outward due to the weights of upper layers. \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{Porosity [6]} & Improper printing process or material used in the process & Very small air bubbles or cavities get a form in the printed component during the printing process \\ \hline \multirow{4}{*}{4} & \multirow{2}{*}{Cracking [6]} & Due to small cavities leading to the formation of cracks, stress formation, or uneven heating or cooling of a particular area & Cracks are formed in the component, which can result in the failure of a printed part. \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{Residual stresses [7]} & Stress is induced due to rapid heating or cooling of the material, leading to expansion or contraction. & When residual stress exceeds the limit of tensile strength, then it can lead to the formation of cracks or defects such as warpage \\ \hline \multirow{2}{*}{6} & \multirow{2}{*}{Poor surface finish [8]} & Printing technique and material used in the process & Parts produced often require post-processing \\ \hline \multirow{2}{*}{7} & \multirow{2}{*}{Stringing [9]} & Due to material property and printing properties. & Parts produced often have strings of extra material attached to them. \\ \hline \multirow{2}{*}{8} & \multirow{2}{*}{Material shrinkage [10]} & \multirow{2}{*}{Materials used in 3D printing have a certain degree of shrinkage.} & If material shrinkage is too large, residual stress may occur, which can cause deformation of the part or crack generation. \\ \hline \end{tabular}
\end{table} 
Table 1: Defect analysis. problems, which are caused due to improper cooling [5]. Apart from machine errors, printing components are subjected to errors due to structural and material shortcomings such as porosity, cracking, residual stresses, etc. [7]. [PERSON] emphasizes the solidification defects in printed components that affect the overall strength of the printed part [6]. During the manufacturing process, the ongoing process or system component often interacts with the environment, humans, and various parameters that affect the physical element. An important factor for monitoring a system is the availability of built-in sensors, but current FDM printers lack these built-in sensors. Therefore, it is a very difficult task for sensing the real-time system state, which is vital for fault detection. Many studies are performed for anomaly detection using a sensor-based approach such as [PERSON] and [PERSON], who used temperature sensors and thermocouples to generate temperature profiles for fault detection [11]. [PERSON] et al. provided a sensor-based model for surface anomaly detection [12]. Many works are done using a sensor-based model for fault detection. However, almost all FDM machines currently have few sensing capabilities that are either inaccessible to users or are not equipped with feedback measurement systems for process correction [13]. For diagnosing a single defect, sensor-based monitoring systems need several sensors. In contrast, only a few sensors can precisely track and recognize product quality during the actual process.