Achieving better connections between deposited lines in additive manufacturing via machine learning

Jingchao Jiang; Chunling Yu; Xun Xu; Yongsheng Ma; Jikai Liu; Jingchao Jiang; Chunling Yu; Xun Xu; Yongsheng Ma; Jikai Liu

doi:10.3934/mbe.2020191

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 3382-3394. doi: 10.3934/mbe.2020191

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Achieving better connections between deposited lines in additive manufacturing via machine learning

1.
Department of Mechanical Engineering, University of Auckland, Auckland 1142, New Zealand
2.
Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China
3.
Department of Mechanical Engineering, University of Alberta, Edmonton T6G 1H9, Canada
4.
Center for Advanced Jet Engineering Technologies (CaJET), Key Laboratory of High Efficiency and Clean Mechanical Manufacture (Ministry of Education), Department of Mechanical Engineering, Shandong University, Jinan 250100, China
5.
Key National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan 250100, China

Received: 17 March 2020 Accepted: 26 April 2020 Published: 30 April 2020

Additive manufacturing is becoming increasingly popular because of its unique advantages, especially fused deposition modelling (FDM) which has been widely used due to its simplicity and comparatively low price. All the process parameters of FDM can be changed to achieve different goals. For example, lower print speed may lead to higher strength of the fabricated parts. While changing these parameters (e.g. print speed, layer height, filament extrusion speed and path distance in a layer), the connection between paths (lines) in a layer will be changed. To achieve the best connection among paths in a real printing process, how these parameters may result in what kind of connection should be studied. In this paper, a machine learning (deep neural network) model is proposed to predict the connection between paths in different process parameters. Four hundred experiments were conducted on an FDM machine to obtain the corresponding connection status data. Among them, there are 280 groups of data that were used to train the machine learning model, while the rest 120 groups of data were used for testing. The results show that this machine learning model can predict the connection status with the accuracy of around 83%. In the future, this model can be used to select the best process parameters in additive manufacturing processes with corresponding objectives.
- additive manufacturing,
- machine learning,
- deep neural network,
- connection
Citation: Jingchao Jiang, Chunling Yu, Xun Xu, Yongsheng Ma, Jikai Liu. Achieving better connections between deposited lines in additive manufacturing via machine learning[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3382-3394. doi: 10.3934/mbe.2020191

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Abstract

Additive manufacturing is becoming increasingly popular because of its unique advantages, especially fused deposition modelling (FDM) which has been widely used due to its simplicity and comparatively low price. All the process parameters of FDM can be changed to achieve different goals. For example, lower print speed may lead to higher strength of the fabricated parts. While changing these parameters (e.g. print speed, layer height, filament extrusion speed and path distance in a layer), the connection between paths (lines) in a layer will be changed. To achieve the best connection among paths in a real printing process, how these parameters may result in what kind of connection should be studied. In this paper, a machine learning (deep neural network) model is proposed to predict the connection between paths in different process parameters. Four hundred experiments were conducted on an FDM machine to obtain the corresponding connection status data. Among them, there are 280 groups of data that were used to train the machine learning model, while the rest 120 groups of data were used for testing. The results show that this machine learning model can predict the connection status with the accuracy of around 83%. In the future, this model can be used to select the best process parameters in additive manufacturing processes with corresponding objectives.

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