In situ monitoring of FDM machine condition via acoustic emission

Hai-xi, WU; Wang, Yan; Yu, Zhonghua

doi:10.1007/s00170-015-7809-4

Cited by 84 publications

(61 citation statements)

References 61 publications

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“…If the material flow is interrupted only briefly the object might be printed completely with only minor defects as layers can compensate for missing lower layer parts to a small degree. In [13] this error is detected using acoustic emissions from the printer. In figure 2 this error type is depicted.…”

Section: Detachmentmentioning

confidence: 99%

Vision based error detection for 3D printing processes

Baumann

Roller

2016

MATEC Web of Conferences

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Abstract. 3D printers became more popular in the last decade, partly because of the expiration of key patents and the supply of affordable machines. The origin is located in rapid prototyping. With Additive Manufacturing (AM) it is possible to create physical objects from 3D model data by layer wise addition of material. Besides professional use for prototyping and low volume manufacturing they are becoming widespread amongst end users starting with the so called Maker Movement. The most prevalent type of consumer grade 3D printers is Fused Deposition Modelling (FDM, also Fused Filament Fabrication FFF). This work focuses on FDM machinery because of their widespread occurrence and large number of open problems like precision and failure. These 3D printers can fail to print objects at a statistical rate depending on the manufacturer and model of the printer. Failures can occur due to misalignment of the print-bed, the print-head, slippage of the motors, warping of the printed material, lack of adhesion or other reasons. The goal of this research is to provide an environment in which these failures can be detected automatically. Direct supervision is inhibited by the recommended placement of FDM printers in separate rooms away from the user due to ventilation issues. The inability to oversee the printing process leads to late or omitted detection of failures. Rejects effect material waste and wasted time thus lowering the utilization of printing resources. Our approach consists of a camera based error detection mechanism that provides a web based interface for remote supervision and early failure detection. Early failure detection can lead to reduced time spent on broken prints, less material wasted and in some cases salvaged objects.

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Section: Detachmentmentioning

confidence: 99%

Vision based error detection for 3D printing processes

Baumann

Roller

2016

MATEC Web of Conferences

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“…Therefore, defect detection and quality control of the fused deposition modeling process is necessary. Wu et al (2016) used acoustic emission techniques to monitor the FDM process, and effectively identified the failure mode of material breakage or depletion and extruder clogging. Yoon et al (2014) and He et al (2017) developed a prognostics and health management approach to the 3D printer health monitoring, using acoustic emission sensor and piezoelectric strain sensor.…”

Section: Introductionmentioning

confidence: 99%

Profile monitoring based quality control method for fused deposition modeling process

Zhang

Hong

2018

J Intell Manuf

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In order to monitor the quality of parts in printing, the methodology to monitor the geometric quality of the printed parts in fused deposition modeling process is researched. A non-contact measurement method based on machine vision technology is adopted to obtain the precise complete geometric information. An image acquisition system is established to capture the image of each layer of the part in building and image processing technology is used to obtain the geometric profile information. With the above information, statistical process control method is applied to monitor the geometric quality of the parts during the printing process. Firstly, a border signature method is applied to transform complex geometry into a simple distance-angle function to get the profile deviation data. Secondly, monitoring of the profile deviation data based on profile monitoring method is studied and applied to achieve the goal of layer-to-layer monitoring. In the research, quantile-quantile plot method is used to transform the profile deviation point cloud data monitoring problem into a linear profile relationship monitoring problem and EWMA control charts are established to monitor the parameters of the linear relationship to detect shifts occurred in the Fused Deposition Modeling process. Finally, laboratory experiments are conducted to demonstrate the effectiveness of the proposed approach.

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“…However, due to the limitations such as immaturity of the technology, there are still many problems to be resolved in the process of FDM in production. Many products built by FDM have the defects such as insufficient precision, warping, short filling [2][3][4], string shearing, or nozzle failure [5][6][7], which seriously restrict the application of FDM. For print failure often occurs in the building process, the improvement of the intelligent monitoring of the FDM building process is particularly important.…”

Section: Introductionmentioning

confidence: 99%

Approach to Online Defect Monitoring in Fused Deposition Modeling Based on the Variation of the Temperature Field

Wang

2018

Complexity

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According to the surface state of the product, a process quality monitoring technology based on a Qualitative Trend Analysis method is proposed. The construction of the product surface defect knowledge base provides a basis for online monitoring of the nozzle status of the fused deposition modeling (FDM) machine. Ten workpieces are designed in all, and each one conducts two prints which are normal and abnormal, respectively. An infrared thermal imager is used to obtain the temperature image of each layer of the workpiece surface; the feature values are extracted, and the difference between the feature values in the two kinds of printing is calculated. The Qualitative Trend Analysis method is used to analyze the difference in order to obtain the corresponding sensitive feature parameters corresponding to the starting points in different phases of the product printing. The change rules of the sensitive feature values are summarized, and the threshold of each starting point is solved. Base on that, the knowledge rules are defined and the defect knowledge base is constructed. Finally, a new experiment is designed to obtain the theoretical starting point of each state of the product surface in the building process. By comparing the theoretical starting point with the actual starting point, the feasibility of the knowledge rules in monitoring the nozzle working state is verified.

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In situ monitoring of FDM machine condition via acoustic emission

Cited by 84 publications

References 61 publications

Vision based error detection for 3D printing processes

Vision based error detection for 3D printing processes

Profile monitoring based quality control method for fused deposition modeling process

Approach to Online Defect Monitoring in Fused Deposition Modeling Based on the Variation of the Temperature Field

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