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Method for 3D Printing Mechanism for Real Time Design Pattern Prediction Analysis

IP.com Disclosure Number: IPCOM000249600D
Publication Date: 2017-Mar-07
Document File: 2 page(s) / 35K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is a predictive mechanism for training a three-dimensional (3D) printer to identify potential design failures. An integrated processor with enabled sensing technology (i.e., Wi-Fi and ultrasonic sensor) embedded in the 3D printer next to the main processor inspects and detects the failures encountered in the design via one-to-one mapping with the software model.

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Method for 3D Printing Mechanism for Real Time Design Pattern Prediction Analysis

Many users of modelling software do not know how to use it to create a design that is stable enough for three-dimensional (3D) printing. Currently, a 3D printing machine has no predictive mechanism installed that enables it to learn and extract the design features and detect design failures.

A mechanism is needed that enables a 3D printer to automatically detect and learn from design faults in order to foresee the faults in other designs and advise the user. The mechanism must be able to predict the failures, and then work on remedies, as soon as the user develops the design in the appropriate Graphical User Interface (GUI) modeling software, before sending the command to print to the 3D printer connected to the mapping software.

The novel contribution is a predictive mechanism for training the 3D printer to identify potential design failures. The mechanism considers the design pattern and features extracted from the associated design. An integrated processor with enabled sensing technology (i.e., Wi-Fi and ultrasonic sensor) embedded in the 3D printer next to the main processor inspects and detects the failures encountered in the design via one-to-one mapping with the software model.

The components and process for implementing the mechanism in a preferred embodiment follow: 1. An additional camera scanner (one on top and on the side) is embedded with the multimedia processor of the 3D

printer. Considering the cost in mind, the 3D internal model developed in the software can also be used for feature extraction from software perspective (and compared with the hardware).

2. The visual scan extracts the features of the design which the user has already printed in order to train the 3D printer to detect the design features associated with the design pattern

3. The key features of designs printed in the past can include contours designing, layer-by-layer addition of critical portions, and the thickness of the material employed on a specific portion of the design pattern from hardware scanning mechanism.

4. From a software monitoring perspective, the features can literally be the pixels. (This is how image classification works. Each second pixel is converted to a feature, and then a neural network learns which combination of features indicates a given class of image). This can compensate for cost.

5. In such a way, sensory mechanisms such as a 3D scanner along with an ultr...