# Design of an In-Process Quality Monitoring Strategy for FDM-Type 3D Printer Using Deep Learning
## 1 Introduction  
The development of specific components utilizing filaments or materials like plastic or even metal and resins can now be done using the help of 3D printers. The method is utilized for faster creation of prototypes and end-use products, since producing molds, plastic, or metal components involves the use and application of industrial equipment [1]. Although using industrial-grade machinery and equipment is suitable and reasonable for its price during mass production, it is time-consuming and quite expensive for prototype development. The mass production method is not viable for prototype development because it typically needs numerous design revisions and iterations. For the development of a single piece, the procedure is rather affordable, but not for mass productions. In using 3D printers for printing materials, the process of fabrication requires a length of time to finish. However, in that duration there might be errors during printing [2, 3]. Printing mistakes are frequently expected, and output is typically rendered useless. Because the procedure could take awhile, operators typically do not monitor the printer as it runs. The 3D printer is not intended to handle faults; hence, it normally keeps printing even if one occurs. Proceeding with the printing process could harm the 3D printer and even waste materials or the output itself [4]. One of the prominent printing error is nozzle clogging [5]. There are numerous efforts in the part of the researchers to address these issue. The researchers proposed in-process monitoring, which is a critical quality control factor that is often overlooked [6]. The in-process quality control is based on continuously monitoring the 3D printing using a visual inspection or acquiring sensor data during the manufacturing processes [7, 8, 9]. These method commonly uses a collaborative sensors for an improved quality prediction and defect detection. In paper presented by [10] used an acoustic emission (AE) to monitorand identify the occurrence of filament breakage using statistical analysis to detect the event, and when it happen during the printing process, it causes the nozzle to clog and malfunction. Likewise, in [11] in preventing the nozzle clogging, the current flowing on the extruder motor is monitored. The increase of amperes readings implicates a rise in extruder motor temperature, leading to a clogged nozzle tip; to stabilize the nozzle temperature, a proportional integral derivative (PID) controls the cooling fan to prevent the nozzle clogging that leads to warping on the final product, and a physics-based model is constructed to further validate their result. Aside from affecting the rheological properties of the material, this printing parameter--nozzle temperature--also influences the filament extrusion velocity, a critical factor attributing on nozzle clogging that can cause a printing failure [12; 13]. Recently, the authors applied machine learning algorithms to the in-process monitoring systems to perform smart monitoring scheme. Machine learning algorithms are used to classify, predict, and prevent the malfunctions during the printing process [14; 15]. The authors in [16; 17] acquire the data on a fused deposition model (FDM) machine with an AE technique. These signals are processed using the hidden semi-Markov model and support vector machine (SVM) to classify the normal and nozzle clogged state of the FDM printer. The approach in [18] utilized heterogenous sensors for quality monitoring. The data from thermocouples, accelerometers, and infrared sensors are analyzed in different machine learning algorithms like probabilistic neural networks, naive Bayesian clustering, and SVM to predict the anomaly during the printing processes. In this paper, a deep learning-based data-driven monitoring system is proposed to enable an in-process quality monitoring scheme for additive manufacturing process. The implementation of a 3D printer monitoring device is proposed to solve the need to continuously monitor the production and status of the 3D printer.