Commercially available fused deposition modeling (FDM) printers have yet to bridge the gap between printing soft, flexible materials and printing hard, rigid materials. This work presents a custom printer solution, based on open-source hardware and software, which allows a user to print both flexible and rigid polymer materials. The materials printed include NinjaFlex, SemiFlex, acrylonitrile-butadiene-styrene (ABS), Nylon, and Polycarbonate. In order to print rigid materials, a custom, high-temperature heated bed was designed to act as a print stage. Additionally, high temperature extruders were included in the design to accommodate the printing requirements of both flexible and rigid filaments. Across 25 equally spaced points on the print plate, the maximum temperature difference between any two points on the heated bed was found to be ∼9°C for a target temperature of 170°C. With a uniform temperature profile across the plate, functional prints were achieved in each material. The print quality varied, dependent on material; however, the standard deviation of layer thicknesses and size measurements of the parts were comparable to those produced on a Zortrax M200 printer. After calibration and further process development, the custom printer will be integrated into the NEXUS system — a multiscale additive manufacturing instrument with integrated 3D printing and robotic assembly (NSF Award #1828355).
more »
« less
Profile monitoring based quality control method for fused deposition modeling process
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 andEWMAcontrol 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.
more »
« less
- Award ID(s):
- 1634867
- NSF-PAR ID:
- 10073124
- Date Published:
- Journal Name:
- Journal of Intelligent Manufacturing
- ISSN:
- 0956-5515
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.more » « less
-
Manoj Gupta (Ed.)more » « less
Three-dimensional (3D) printing with continuous carbon-fiber-reinforced polymer (C-CFRP) composites is under increasing development, as it offers more versatility than traditional molding processes, such as the out-of-autoclave-vacuum bag only (OOA-VBO) process. However, due to the layer-by-layer deposition of materials, voids can form between the layers and weaken some of the parts’ properties, such as the interlaminar shear strength (ILSS). In this paper, a novel mold-less magnetic compaction force-assisted additive manufacturing (MCFA-AM) method was used to print carbon nanofiber (CNF) z-threaded CFRP (ZT-CFRP) laminates with significantly improved ILSS and reduced void content compared to traditional C-CFRP laminates, which are printed using a no-pressure 3D-printing process (similar to the fused-deposition-modeling process). The radial flow alignment (RFA) and resin-blending techniques were utilized to manufacture a printing-compatible fast-curing ZT-CFRP prepreg tape to act as the feedstock for a MCFA-AM printhead, which was mounted on a robotic arm. In terms of the ILSS, the MCFA-AM method coupled with ZT-CFRP nanomaterial technology significantly outperformed the C-CFRP made with both the traditional no-pressure 3D-printing process and the OOA-VBO molding process. Furthermore, the mold-less MCFA-AM process more than doubled the production speed of the OOA-VBO molding process. This demonstrates that through the integration of new nanomaterials and 3D-printing techniques, a paradigm shift in C-CFRP manufacturing with significantly better performance, versatility, agility, efficiency, and lower cost is achievable.
-
Abstract The process uncertainty induced quality issue remains the major challenge that hinders the wider adoption of additive manufacturing (AM) technology. The defects occurred significantly compromise structural integrity and mechanical properties of fabricated parts. Therefore, there is an urgent need in fast, yet reliable AM component certification. Most finite element analysis related methods characterize defects based on the thermomechanical relationships, which are computationally inefficient and cannot capture process uncertainty. In addition, there is a growing trend in data-driven approaches on characterizing the empirical relationships between thermal history and anomaly occurrences, which focus on modeling an individual image basis to identify local defects. Despite their effectiveness in local anomaly detection, these methods are quite cumbersome when applied to layer-wise anomaly detection. This paper proposes a novel in situ layer-wise anomaly detection method by analyzing the layer-by-layer morphological dynamics of melt pools and heat affected zones (HAZs). Specifically, the thermal images are first preprocessed based on the g-code to assure unified orientation. Subsequently, the melt pool and HAZ are segmented, and the global and morphological transition metrics are developed to characterize the morphological dynamics. New layer-wise features are extracted, and supervised machine learning methods are applied for layer-wise anomaly detection. The proposed method is validated using the directed energy deposition (DED) process, which demonstrates superior performance comparing with the benchmark methods. The average computational time is significantly shorter than the average build time, enabling in situ layer-wise certification and real-time process control.more » « less
-
more » « less
Abstract Wire arc additive manufacturing (WAAM) has gained attention as a feasible process in large-scale metal additive manufacturing due to its high deposition rate, cost efficiency, and material diversity. However, WAAM induces a degree of uncertainty in the process stability and the part quality owing to its non-equilibrium thermal cycles and layer-by-layer stacking mechanism. Anomaly detection is therefore necessary for the quality monitoring of the parts. Most relevant studies have applied machine learning to derive data-driven models that detect defects through feature and pattern learning. However, acquiring sufficient data is time- and/or resource-intensive, which introduces a challenge to applying machine learning-based anomaly detection. This study proposes a multisource transfer learning method that generates anomaly detection models for balling defect detection, thus ensuring quality monitoring in WAAM. The proposed method uses convolutional neural network models to extract sufficient image features from multisource materials, then transfers and fine-tunes the models for anomaly detection in the target material. Stepwise learning is applied to extract image features sequentially from individual source materials, and composite learning is employed to assign the optimal frozen ratio for converging transferred and present features. Experiments were performed using a gas tungsten arc welding-based WAAM process to validate the classification accuracy of the models using low-carbon steel, stainless steel, and Inconel.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s10845-018-1424-9
