# Metal Additive Manufacturing Parts Inspection Using Convolutional Neural Network
## 1 Introduction  
Metal additive manufacturing (AM) processes have introduced some capabilities unparalleled by traditional manufacturing, as they realize custom-designed shape, complex features, and low materials consumptions provided by AM [1]. Laser metal deposition (LMD) is a form of AM which accomplishes the layer-by-layer fabrication of near net-shaped components by introducing a powder stream into a high-energy laser beam. During the LMD process, a melt pool is formed by rastering the laser beam across the sample surface, and the powders are injected into the melt pool for each layer deposition. LMD has been explored for various applications, e.g., metallic component repair, surface modification, and layering gradient metal alloy on a dissimilar metal base [2; 3; 4; 5]. Some of the control parameters involved in LMD are the laser power, laser scan speed, powder feed rate, shielding gas flow rate, and the quality of the powder feedstock. The above parameters constantly affect the parts being formed. Some studies have focused on the process parameter selection and optimization of the performance of LMD parts, but the defect presence is still high compared to traditional manufacturing [1; 6; 7; 8]. The common defects present in the LMD process are crack, gas porosity, and lack of fusion (LoF), which have negatively affected the properties of LMD fabricated parts [9]. The direct joining of two dissimilar alloys is usually compromised by cracks, resulting from the residual stress, formation of brittle intermetallic compounds, or differences in thermal expansion coefficient [2; 5; 10]. [PERSON] tried to fabricate gradient components transitioning from AISI 304L stainless steel to Ti-6 Al-4V, but the component was halted due to cracks in the build [3]. A similar phenomenon has been observed by [PERSON] [4] and [PERSON] [5], which the material deposited cracked prior to analysis. Gas porosity, which is caused by the entrapment of gas from the powder feed system or the release of gas present in thepowder particles [8; 11; 12]. This type of porosity could occur in any specific location and is nearly spherical. The gas porosities are highly undesirable because they severely degrade mechanical strength and fatigue resistance. The noticeable effect of porosity was reported by [PERSON], in that 3.3% gas porosity content in the direct energy deposited AISI 4340 steel made the ductility decrease by 92.4% compared to the annealed one [8]. When there is insufficient energy in the melt pool, the resulting inability to melt the powder particles leads to LoF defects. LoF defects are usually found along boundaries between layers and in irregular shapes [6; 7]. A significant LoF defect nucleated the fracture and led to poor elongation of 7% (as compared to 25%-33% elongation without LoF defects) for Ti-6 Al-4V [6]. These defects contribute to the variation in the mechanical properties of each deposit, representing the main barrier to the widespread adoption of LMD technology. Therefore, this creates a need to inspect and evaluate the LMD build parts. Traditionally, the quality of AM build parts has been manually inspected by experienced materials engineers. However, the manual inspection process is very time-consuming and labor-intensive. The machine vision-based inspection method has been investigated in the past decade. This method has been adopted for many years in facility parts identification and classification, glass products, steel strips, metal surface inspection, and agricultural product identification [13; 14; 15; 16]. [PERSON] used the deviation of a melt-pool temperature gradient from a reference defect-free cooling curve to predict gas porosity in the LMD process [15]. The Gaussian pyramid decomposition was applied to low the resolution and center-surround difference operation for steel strip defects detection, which would lose the image information [14]. Vision-based methods had been used the primitive attributes reflected by local anomalies to detect and segment defects.