Abstract
Neural networks have aroused a lively interest since 1943 when Warren McCulloch and Walter Pitts proposed a neural network model (a single layer model), that has remained fundamentally structural even today for most neural networks. Problem solving and implicit the study of a system's operating model such as 3D printing involves the association between input data, hypotheses and output data, and neural networks provide the ability to form their own model of solving. The main difference between neural networks and other information processing systems is the ability to learn from interacting with the environment and so improving performance. A correct representation of information, allowing interpretation, prediction, and response to an external stimulus, can allow the network to build a model of the considered process, in the paper case fused deposition modelling (FDM) process.
Highlights
The concept of personalized printing is interesting for almost everybody
The application of artificial neural networks modelling technique allowed the improvement of the process knowledge, and to use this aspect to facilitate process control
The Taguchi method was used for parameters chosen so that to reduce the input parameters number without losing the precision of the experiment results
Summary
The concept of personalized printing is interesting for almost everybody This is a revolutionary method for creating 3D models and is great for making fast prototypes. By using the utility of inkjet technology saves time and it is create a complete model in a single process using 3D printing [1-2]. To solve this problem, present study proposes the artificial neural network (ANN) method for modelling the relationship between temperature, speed and layer thickness. Many studies are made on the influence of various process parameters on mechanical proprieties of the print [5-7]. From these parameters the layer thickness, the raster angle and the air gap influence mostly the elastic performance of the ABS printing
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
