Vibration from an erroneous disturbance harms the manufactured components and lowers the output quality of an FDM printer. For moving machinery, vibration analysis and control are crucial. Additive manufacturing is the basis of 3D printing, which utilizes mechanical movement of the extruder to fabricate objects, and faults occur due to unwanted vibrations. Therefore, it is vital to examine the vibration patterns of a 3D printer. In this work, we observe these parameters of an FDM printer, exemplified by the MakerBot Method X. To analyze the system, it is necessary to understand the motion it generates and select appropriate sensors to detect those motions. The sensor measurement values can be used to determine the condition of the printer. We used an accelerometer and an acoustic sensor to measure the vibration and sound produced by the printer. The outputs from these sensors were examined individually. The findings show that vibration occurs at relatively low levels during continuous motion because it mainly appears at component transition edges. Due to abrupt acceleration and deceleration during zigzag motion, vibration reaches its peak.
To ensure the quality and consistency of parts produced by the MakerBot Method X, it is essential to have an effective quality control system in place. One of the most promising approaches for quality control in FDM is condition monitoring. Condition monitoring involves the continuous monitoring of key parameters during the printing process to detect anomalies and identify potential defects before they become critical. Many studies have already been done on this topic [1][2][3][4][5][6][7][8] This technique can provide valuable insights into the quality of printed parts, enabling manufacturers to take corrective action and optimize their production processes.
In this paper, we will review the current state-of-the-art in-situ condition monitoring for FDM printers, examine the benefits and challenges associated with this technique, propose future research directions to enhance the effectiveness of condition monitoring in this printer, and present experimental findings to contribute valuable insights to the field.
FDM is a prevalent additive manufacturing method that involves melting material, extruding it through a nozzle, depositing it on a bed, and layering it to form parts. FDM offers several benefits, including low cost, simple operation, and the ability to manufacture complex shapes efficiently and environmentally friendly. We are going to differentiate between various modes of failure within the 3d printer caused by the motions. We will also analyze the acoustic patterns of the 3d printer during motion.
Despite technological advancements, FDM printing can still result in defects, necessitating extensive research into monitoring the printing process and product quality to enhance success rates and efficiency. [1] By doing a comparison and analysis of vibration signal generated by extruder head movements in a FDM printer we can further understand the root cause of failure, in FDM printer the xaxis, y-axis, and z-axis motors work together to control the movement of the extruder head and the build platform in three-dimensional space.
The print head moves in 2 axis ‘x’ and ‘y’, The print bed moves in ‘z’ direction, The x-axis motor is responsible for moving the print head horizontally along the x-axis, while the y-axis motor controls the movement of the build platform along the y-axis. The z-axis motor, on the other hand, controls the vertical movement of the print head and the build platform along the z-axis. Also three different motions, including point-to-point, zigzag, and continuous motion are produced by extruder which result in sometimes unwanted vibration. Fig 1 .1. shows the location of X and Y axis motor in the 3D printer. For this research, we will be using The MakerBot Method X, it is a high-performance 3D printer that uses Fused Deposition Modeling (FDM) technology to produce high-quality parts. This printer is designed for industrial applications and is capable of printing with a wide range of materials, including ABS, Nylon, and Carbon Fiber.
PRINTER HEALTH
In the context of 3D printing, in situ monitoring (ISM) refers to the use of automated processes to facilitate real-time quality monitoring during the printing process. This type of monitoring is essential for detecting product quality defects and ensuring the overall health of the 3D printing process [9] [10]. Research and studies have explored the use of accelerometers and acoustic sensors for in situ monitoring of 3D printers, particularly in the context of fused filament fabrication (FFF) processes. Accelerometers have been utilized to analyze and control the vibration of 3D printers, with the extracted acceleration data being used to identify various states of the FFF machine and predict product quality [14] [15].
Additionally, the installation of accelerometers on 3D printers has been presented in experimental setups for nozzle condition monitoring, demonstrating their potential in this application [16].
Acoustic sensors have also been considered for in situ monitoring during the FFF process. Some studies have used time-domain features of acoustic emission data to identify abnormal conditions, such as material runout and blocked extruders, showcasing the potential of acoustic sensors in monitoring the 3D printing process [16]. Various techniques, such as in-situ print characterization, defect monitoring via conductive filament and Ohm’s Law, and digital image correlation (DIC)-based monitoring methods, have been developed for in situ monitoring of additive manufacturing [11] [12]. ASTM International has released a report on in-situ monitoring for 3D printing, which aims to provide a comprehensive assessment of the technology readiness and to identify gaps and challenges within the field of in-situ technology [19]. The report is the result of a workshop series organized by ASTM International, NASA, and America Makes, and it serves as a valuable resource for the 3D printing community [10] [13].
A general idea of main components
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