scholarly journals Automated Testing and Characterization of Additive Manufacturing (ATCAM)

2021 ◽  
Author(s):  
Arash Alex Mazhari ◽  
Randall Ticknor ◽  
Sean Swei ◽  
Stanley Krzesniak ◽  
Mircea Teodorescu
Keyword(s):  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Cell System ◽  
Automated Testing ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Machine Calibration

AbstractThe sensitivity of additive manufacturing (AM) to the variability of feedstock quality, machine calibration, and accuracy drives the need for frequent characterization of fabricated objects for a robust material process. The constant testing is fiscally and logistically intensive, often requiring coupons that are manufactured and tested in independent facilities. As a step toward integrating testing and characterization into the AM process while reducing cost, we propose the automated testing and characterization of AM (ATCAM). ATCAM is configured for fused deposition modeling (FDM) and introduces the concept of dynamic coupons to generate large quantities of basic AM samples. An in situ actuator is printed on the build surface to deploy coupons through impact, which is sensed by a load cell system utilizing machine learning (ML) to correlate AM data. We test ATCAM’s ability to distinguish the quality of three PLA feedstock at differing price points by generating and comparing 3000 dynamic coupons in 10 repetitions of 100 coupon cycles per material. ATCAM correlated the quality of each feedstock and visualized fatigue of in situ actuators over each testing cycle. Three ML algorithms were then compared, with Gradient Boost regression demonstrating a 71% correlation of dynamic coupons to their parent feedstock and provided confidence for the quality of AM data ATCAM generates.

Investigation of the Functional Properties of Additively-Fabricated Triply Periodic Minimal Surface-Based Bone Scaffolds for the Treatment of Osseous Fractures

2021 ◽  
Author(s):  
Abigail Chaffins ◽  
Mohan Yu ◽  
Pier Paolo Claudio ◽  
James B. Day ◽  
Roozbeh (Ross) Salary
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Fused Deposition ◽  
Bone Scaffolds ◽  
Periodic Minimal Surface ◽  
Triply Periodic ◽  
Triply Periodic Minimal Surface ◽  
Deposition Modeling

Abstract Fused deposition modeling (FDM), is a direct-write material extrusion additive manufacturing process, which has emerged as a method of choice for the fabrication of a wide range of biological tissues and structures. FDM allows for non-contact, multi-material deposition of a broad spectrum of functional materials for tissue engineering applications. However, the FDM process is intrinsically complex, consisting of a multitude of parameters as well as material-machine interactions, which may adversely influence the mechanical properties, the surface morphology, and ultimately the functional integrity of fabricated bone scaffolds. Hence, process optimization in addition to physics-based characterization of the FDM process would be inevitably a need. The overarching goal of this research work is to fabricate biocompatible, porous bone scaffolds, incorporating autologous human bone marrow mesenchymal stem cells (hBMSCs), for the treatment of osseous fractures, defects, and eventually diseases. The objective of this work is to investigate the mechanical properties of several triply periodic minimal surface (TPMS) bone scaffolds, fabricated using fused deposition modeling (FDM) additive manufacturing process. In this study, biocompatible TPMS bone scaffolds were FDM-deposited, based on a medical-grade polymer composite, composed of polyamide, polyolefin, and cellulose fibers (named PAPC-II). In addition, the experimental characterization of the TPMS bone scaffolds was on the basis of a single factor experiment. The compression properties of the fabricated bone scaffolds were measured using a compression testing machine. Furthermore, a digital image processing program was developed in the MATLAB environment to characterize the morphological properties of the fabricated bone scaffolds.

Surface Characterization in Fused Deposition Modeling

3D Printing ◽  
2017 ◽  
pp. 22-47 ◽  
Author(s):  
Alberto Boschetto ◽  
Luana Bottini
Keyword(s):  
Surface Characterization ◽  
Fused Deposition Modeling ◽  
Prediction Models ◽  
Great Effort ◽  
Roughness Parameters ◽  
Fused Deposition ◽  
Design Specifications ◽  
Deposition Modeling

Fused deposition modeling is a proven technology, widely diffused in industry, born for the fabrication of aesthetic and functional prototypes. Recently used for small and medium series of parts and for tooling, it received particular attention in order to integrate prototyping systems within production. A limiting aspect of this technology is the obtainable roughness and above all its prediction: no machine software and Computer-Aided Manufacturing implements a relationship between process parameters and surface quality of components. The prediction of the surface properties is an essential tool that allows it to comply with design specifications and, in process planning, to determine manufacturing strategies. Recently, great effort has been spent to develop a characterization of such surfaces. In this chapter, prediction models are presented and a new characterization approach is detailed. It is based on the theoretical prediction of the geometrical roughness profile, thus allowing it to obtain, in advance, all roughness parameters.

Surface Characterization in Fused Deposition Modeling

2014 ◽  
pp. 249-280 ◽  
Author(s):  
Alberto Boschetto ◽  
Luana Bottini
Keyword(s):  
Surface Characterization ◽  
Fused Deposition Modeling ◽  
Prediction Models ◽  
Great Effort ◽  
Roughness Parameters ◽  
Fused Deposition ◽  
Design Specifications ◽  
Deposition Modeling

Fused deposition modeling is a proven technology, widely diffused in industry, born for the fabrication of aesthetic and functional prototypes. Recently used for small and medium series of parts and for tooling, it received particular attention in order to integrate prototyping systems within production. A limiting aspect of this technology is the obtainable roughness and above all its prediction: no machine software and Computer-Aided Manufacturing implements a relationship between process parameters and surface quality of components. The prediction of the surface properties is an essential tool that allows it to comply with design specifications and, in process planning, to determine manufacturing strategies. Recently, great effort has been spent to develop a characterization of such surfaces. In this chapter, prediction models are presented and a new characterization approach is detailed. It is based on the theoretical prediction of the geometrical roughness profile, thus allowing it to obtain, in advance, all roughness parameters.

Nano-CT Characterization of Structural Voids and Air Bubbles in Fused Deposition Modeling for Additive Manufacturing

2015 ◽  
Author(s):  
Roland K. Chen ◽  
Terris T. Lo ◽  
Lei Chen ◽  
Albert J. Shih
Keyword(s):  
Fused Deposition Modeling ◽  
Three Dimensional ◽  
Material Testing ◽  
Air Bubbles ◽  
Part Quality ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Non Destructive

The build quality of fused deposition modeling (FDM) parts depends on many build parameters, such as toolpath and temperature. Destructive material testing methods are widely used to examine FDM parts with different build parameters. The optimization of build parameters relies on methods of experimental design and extensive material testing. However, this approach mainly considers the bulk properties of the FDM part, without fully understanding the effect of each parameter on the build quality. This study presents a method to investigate the integrity of FDM parts using nano-focus computed tomography (NanoCT). A solid filled ULTEM sample was built and underwent NanoCT scan. The three dimensional geometry of this sample was reconstructed. Structural voids and bubbles inside the sample were also identified and quantified. The volume of this solid filled sample consists of 11.9% structural voids and bubbles. Air bubbles are further categorized into internal bubbles (bubbles inside the deposited fibers) and necking bubbles (bubbles at the bonding region of two adjacent fibers). While structural voids can be predicted according to toolpath, layer thickness, and extruder diameter, the occurrence of air bubbles are unexpected and can compromise the integrity of the built parts. NanoCT offers a non-destructive way to inspect the integrity of FDM parts. NanoCT can also be used to study three dimensional meso-structure and correlate that with build parameters. This will provide insightful information for further studying the FDM process and help to predict material strengths and to improve the part quality.

scholarly journals Design and Characterization of a Screw Extrusion Hot-End for Fused Deposition Modeling

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 590
Author(s):  
Tim Feuerbach ◽  
Markus Thommes
Keyword(s):  
Vinyl Acetate ◽  
Fused Deposition Modeling ◽  
Ethylene Vinyl Acetate ◽  
Direct Printing ◽  
Fused Deposition ◽  
Screw Extrusion ◽  
Deposition Modeling ◽  
Feedstock Material ◽  
Material Form

The filament is the most widespread feedstock material form used for fused deposition modeling printers. Filaments must be manufactured with tight dimensional tolerances, both to be processable in the hot-end and to obtain printed objects of high quality. The ability to successfully feed the filament into the printer is also related to the mechanical properties of the filament, which are often insufficient for pharmaceutically relevant excipients. In the scope of this work, an 8 mm single screw hot-end was designed and characterized, which allows direct printing of materials from their powder form and does not require an intermediate filament. The capability of the hot-end to increase the range of applicable excipients to fused deposition modeling was demonstrated by processing and printing several excipients that are not suitable for fused deposition modeling in their filament forms, such as ethylene vinyl acetate and poly(1-vinylpyrrolidone-co-vinyl acetate). The conveying characteristic of the screw was investigated experimentally with all materials and was in agreement with an established model from literature. The complete design information, such as the screw geometry and the hot-end dimensions, is provided in this work.

Knowledge-Based Design of Artificial Neural Network Topology for Additive Manufacturing Process Modeling: A New Approach and Case Study for Fused Deposition Modeling

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hossein Mokhtarian ◽  
Hesam Jafarian ◽  
Saoussen Dimassi ◽  
Shahriar Bakrani-Balani ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Additive Manufacturing ◽  
Fused Deposition Modeling ◽  
Expert Knowledge ◽  
Fused Deposition ◽  
Knowledge Based ◽  
Deposition Modeling ◽  
Artificial Neural

Additive manufacturing (AM) continues to rise in popularity due to its various advantages over traditional manufacturing processes. AM interests industry, but achieving repeatable production quality remains problematic for many AM technologies. Thus, modeling different process variables in AM using machine learning can be highly beneficial in creating useful knowledge of the process. Such developed artificial neural network (ANN) models would aid designers and manufacturers to make informed decisions about their products and processes. However, it is challenging to define an appropriate ANN topology that captures the AM system behavior. Toward that goal, an approach combining dimensional analysis conceptual modeling (DACM) and classical ANNs is proposed to create a new type of knowledge-based ANN (KB-ANN). This approach integrates existing literature and expert knowledge of the AM process to define a topology for the KB-ANN model. The proposed KB-ANN is a hybrid learning network that encompasses topological zones derived from knowledge of the process and other zones where missing knowledge is modeled using classical ANNs. The usefulness of the method is demonstrated using a case study to model wall thickness, part height, and total part mass in a fused deposition modeling (FDM) process. The KB-ANN-based model for FDM has the same performance with better generalization capabilities using fewer weights trained, when compared to a classical ANN.

Carbon Nanotube Reinforced Fused Deposition Modeling Using Microwave Irradiation

2016 ◽  
Author(s):  
Meng Zhang ◽  
Xiaoxu Song ◽  
Weston Grove ◽  
Emmett Hull ◽  
Z. J. Pei ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Carbon Nanotube ◽  
Additive Manufacturing ◽  
Microwave Irradiation ◽  
Computer Aided Design ◽  
Fused Deposition Modeling ◽  
Layer By Layer ◽  
Sem Images ◽  
Fused Deposition ◽  
Deposition Modeling

Additive manufacturing (AM) is a class of manufacturing processes where material is deposited in a layer-by-layer fashion to fabricate a three-dimensional part directly from a computer-aided design model. With a current market share of 44%, thermoplastic-based additive manufacturing such as fused deposition modeling (FDM) is a prevailing technology. A key challenge for AM parts (especially for parts made by FDM) in engineering applications is the weak inter-layer adhesion. The lack of bonding between filaments usually results in delamination and mechanical failure. To address this challenge, this study embedded carbon nanotubes into acrylonitrile butadiene styrene (ABS) thermoplastics via a filament extrusion process. The vigorous response of carbon nanotubes to microwave irradiation, leading to the release of a large amount of heat, is used to melt the ABS thermoplastic matrix adjacent to carbon nanotubes within a very short time period. This treatment is found to enhance the inter-layer adhesion without bulk heating to deform the 3D printed parts. Tensile and flexural tests were performed to evaluation the effects of microwave irradiation on mechanical properties of the specimens made by FDM. Scanning electron microscopic (SEM) images were taken to characterize the fracture surfaces of tensile test specimens. The actual carbon nanotube contents in the filaments were measured by conducting thermogravimetric analysis (TGA). The effects of microwave irradiation on the electrical resistivity of the filament were also reported.

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