Prediction of tensile strength in fused deposition modeling process using artificial neural network technique

2020 ◽  
Author(s):  
Karthic Manoharan ◽  
K. Chockalingam ◽  
S. Shankar Ram
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tensile Strength ◽  
Fused Deposition Modeling ◽  
Fused Deposition ◽  
Modeling Process ◽  
Deposition Modeling ◽  
Artificial Neural

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.

Knowledge-Based Optimization of Artificial Neural Network Topology for Process Modeling of Fused Deposition Modeling

2018 ◽  
Author(s):  
Hari P. N. Nagarajan ◽  
Hesam Jafarian ◽  
Azarakhsh Hamedi ◽  
Hossein Mokhtarian ◽  
Romaric Prod'hon ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fused Deposition Modeling ◽  
Performance Metrics ◽  
Expert Knowledge ◽  
Production Quality ◽  
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 the influence of process variables on the production quality in AM can be highly beneficial in creating useful knowledge of the process and product. An approach combining dimensional analysis conceptual modeling, mutual information based analysis, experimental sampling, factors selection, and modeling based on Knowledge-Based Artificial Neural Network (KB-ANN) is proposed for Fused Deposition Modeling (FDM) process. KB-ANN reduces the excessive amount of training samples required in traditional neural networks. The developed KB-ANN’s topology for FDM, integrates existing literature and expert knowledge of the process. The KB-ANN is compared to conventional ANN using prescribed performance metrics. This research presents a methodology to concurrently perform experiments, classify influential factors, limit the effect of noise in the modeled system, and model using KB-ANN. This research can contribute to the qualification efforts of AM technologies.

Feature Extraction and Optimum Part Deposition Orientation for FDM

2015 ◽  
Vol 793 ◽  
pp. 642-646 ◽  
Author(s):  
Khairul Fauzi Karim ◽  
D. Hazry ◽  
A.H. Zulkifli ◽  
S. Faiz Ahmed ◽  
Zuradzman Mohamad Razlan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Fused Deposition Modeling ◽  
Support Structure ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Selection Of

Support generation is an essential for Fused Deposition Modeling (FDM) process which is dependent on part deposition orientation. Various part deposition orientation result in formation of different support and non-support features. Present work focuses on extracting the support features containing Externally-Supported Features (ESF) which are able to determine the volume and number of support structure. The methodology proposed in this work uses these information as an input for Artificial Neural Network (ANN) in order to automate the selection of optimum part deposition orientation. The results produced in present methodology can be predicted and are in agreement with the results published earlier.

Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 891-897
Author(s):  
G.Q. Shang ◽  
C.H. Sun ◽  
X.F. Chen ◽  
J.H. Du
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fused Deposition Modeling ◽  
Rapid Prototype ◽  
Fused Deposition ◽  
Novel Approach ◽  
Deposition Modeling ◽  
Hidden Layer ◽  
Parts Manufacturing ◽  
Bp Neural Network Model

Fused deposition modeling (FDM) has been widely applied in complex parts manufacturing and rapid tooling and so on. The precision of prototype was affected by many factors during FDM, so it is difficult to depict the process using a precise mathematical model. A novel approach for establishing a BP neural network model to predict FDM prototype precision was proposed in this paper. Firstly, based on analyzing effect of each factor on prototyping precision, some key parameters were confirmed to be feature parameters of BP neural networks. Then, the dimensional numbers of input layer and middle hidden layer were confirmed according to practical conditions, and therefore the model structure was fixed. Finally, the structure was trained by a great lot of experimental data, a model of BP neural network to predict precision of FDM prototype was constituted. The results show that the error can be controlled within 10%, which possesses excellent capability of predicting precision.

scholarly journals Numerical Simulation of the Fused Deposition Modeling for the Manufacturing of Parts with both High Geometric Fidelity and Mechanical Quality

2021 ◽  
Vol 14 (6) ◽  
pp. 712-725
Author(s):  
Holm Altenbach ◽  
◽  
G´abor Janiga ◽  
Rene Androsch ◽  
Mario Beiner ◽  
...  
Keyword(s):  
Fused Deposition Modeling ◽  
Two Dimensions ◽  
Volume Of Fluid Method ◽  
Ansys Fluent ◽  
Mechanical Parts ◽  
Fused Deposition ◽  
Modeling Process ◽  
Mechanical Quality ◽  
Deposition Modeling ◽  
Manufacturing Methods

With increasing usage of additive manufacturing methods for mechanical parts the need for precise and reliable simulations of the manufacturing process increases as well. In this paper various com- putations suited for simulating the fused deposition modeling process are considered in two dimensions. In fused deposition modeling a molten polymer is laid down on a prescribed path before the cooling of the melt begins. The occuring flows are treated as multiphase flows. To model the deposition of the filament, methods of computational fluid dynamics are used in ANSYS-Fluent, namely the volume of fluid method (VOF). Different numerical experiments are simulated

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