Smart AI makes all kinds of shapes on its own

Date:
August 18, 2020
Source:
Pohang University of Science & Technology (POSTECH)
Summary:
POSTECH research team develops an artificial neural network system
that recommends plastic molding process conditions.



FULL STORY ========================================================================== Plastic is light, cheap, and can be made into any shape if heated,
making it a "gift from the 20th-century god." The key is to maintain
its uniform quality but its sensitivity to process conditions makes
processing autonomy difficult.

It also takes long to change the process once it is set and real-time optimization is deemed impossible due to the difference in actual
outcomes.


==========================================================================
A research team consisting of Professor Junsuk Rho and doctoral student
Chihun Lee of POSTECH's departments of mechanical and chemical engineering
and Professor Seungchul Lee, Juwon Na in the MS-PhD integrated program
with Professor Seongjin Park in the Department of Mechanical Engineering
have together developed a system that recommends process conditions for injection molding by combining artificial neural network (Artificial
Neural Network) and a random search. Various shapes can be obtained in
real time through using this new system. These research findings were
recently published in the journal Advanced Intelligent Systems.

The team trained the relationship between process conditions and final
products using artificial intelligence to find the conditions that
satisfy the target quality. 3,600 simulations and 476 experiments from
36 different molds were obtained and learned. As a result, the team
confirmed that each datum had 15 shapes and five processes as input
value and the final weight of the product as the output value.

Based on the weight prediction model trained through transfer learning,
a recommender system was developed to find the optimal process conditions
by random search. By applying the conditions recommended by the AI model,
the average relative error of 0.66% was achieved.

Finally, a GUI (graphical user interface) was developed for the actual injection machines. This allows even non-experts to enter the shape
information for any product to establish a process condition that has
an error within 1% of the target product weight.

Conventional research predicted the quality of the target product by only changing the process conditions for one specified product. However, this
study collected information on the results (weight) of 36 differently
shaped products while changing both quantified shapes and process
conditions. Therefore, even if a new product is molded, the process
conditions can be controlled without having to predict the results or to generate learning data by simply entering the shape of the product. In addition, transfer learning was introduced to obtain both simulation
data and the accuracy of experimental data.

Using this newly developed artificial neural network system, even
non-experts can obtain uniform results by simply entering the shape and
the weight of the final product desired. It is anticipated that such
system will enable the implementation of 'unmanned smart factory' in
various manufacturing industries by allowing plastic injection processes, machining, 3D printers, and casting, which were previously challenging.


========================================================================== Story Source: Materials provided by Pohang_University_of_Science_&_Technology_(POSTECH).

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Chihun Lee, Juwon Na, Kyongho Park, Hyeonjae Yu, Jongsun Kim, Kwonil
Choi, Dongyong Park, Seongjin Park, Junsuk Rho, Seungchul Lee.

Development of Artificial Neural Network System to Recommend Process
Conditions of Injection Molding for Various Geometries. Advanced
Intelligent Systems, 2020; 2000037 DOI: 10.1002/aisy.202000037 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200818094051.htm

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