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. 2022 Aug 29;14(17):3551.
doi: 10.3390/polym14173551.

Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts

Saeid Saeidi Aminabadi  1 , Paul Tabatabai  2 , Alexander Steiner  2 , Dieter Paul Gruber  2 , Walter Friesenbichler  1 , Christoph Habersohn  3 , Gerald Berger-Weber  4
Affiliations

Industry 4.0 In-Line AI Quality Control of Plastic Injection Molded Parts

Saeid Saeidi Aminabadi et al. Polymers (Basel). .

Abstract

Automatic in-line process quality control plays a crucial role to enhance production efficiency in the injection molding industry. Industry 4.0 is leading the productivity and efficiency of companies to minimize scrap rates and strive for zero-defect production, especially in the injection molding industry. In this study, a fully automated closed-loop injection molding (IM) setup with a communication platform via OPC UA was built in compliance with Industry 4.0. The setup included fully automated inline measurements, in-line data analysis, and an AI control system to set the new machine parameters via the OPC UA communication protocol. The surface quality of the injection molded parts was rated using the ResNet-18 convolutional neural network, which was trained on data gathered by a heuristic approach. Further, eight different machine learning models for predicting the part quality (weight, surface quality, and dimensional properties) and for predicting sensor data were trained using data from a variety of production information sources, including in-mold sensors, injection molding machine (IMM) sensors, ambient sensors, and inline product quality measurements. These models are the backbone of the AI control system, which is a heuristic model predictive control (MPC) method. This method was applied to find new sets of machine parameters during production to control the specified part quality feature. The control system and predictive models were successfully tested for two groups of quality features: Geometry control and surface quality control. Control parameters were limited to injection speed and holding pressure. Moreover, the geometry control was repeated with mold temperature as an additional control parameter.

Keywords: AI quality control; closed-loop quality control; deep neural network; deep residual learning; dimensional features prediction; in-line quality control; injection molding of plastics; predictive control; surface quality prediction; weight prediction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
OPC UA platform for management of the measurements and the process automation.
Figure 2
Figure 2
OPC UA communications platform to collect data from injection molding machine and peripherals.
Figure 3
Figure 3
OPC UA communication platform for synchronizing the control process.
Figure 4
Figure 4
The scale for measuring the weight, the dimensional measurement system, and the camera for surface quality inspection.
Figure 5
Figure 5
The position of the six scanning lines.
Figure 6
Figure 6
Automation process sequence diagram.
Figure 7
Figure 7
The procedure of handling and measurement of the injection molded part [5].
Figure 8
Figure 8
The general block diagram for an injection molding process.
Figure 9
Figure 9
The block diagram of the control system.
Figure 10
Figure 10
The controller block diagram in detail.
Figure 11
Figure 11
Experimental setup.
Figure 12
Figure 12
The illustration of the measuring sample, (a) Isometric view of the sample, (b) The highly reflective surface of the sample is called a piano-black surface.
Figure 13
Figure 13
The positions of the FOS P/T sensors are marked in red and the position of the Kistler pressure sensor is marked in blue, (a) marked positions on the part, (b) marked positions on the moving side of the mold.
Figure 14
Figure 14
(a) Two ambient sensor packages close to an industrial temperature and humidity sensor for calibration, (b) The ESP8266 module and sensor board.
Figure 15
Figure 15
Main effect diagrams from the analysis of the CCI experimental design.
Figure 16
Figure 16
Experiment results for surface quality control. Orange triangles are cycles with manual interference in the process.
Figure 17
Figure 17
Experiment results for linear dimension control with strategy 1 for controlling parameters. Orange triangles are cycles with manual interference in the process. Arrows show the directions of the change effect.
Figure 18
Figure 18
Experiment results for linear dimension control with strategy 2 for controlling parameters. Orange triangles are cycles with manual interference in the process. Arrows show the directions of the change effect.
Figure 19
Figure 19
Comparing the correlations between surface quality and weight for surface quality control.
Figure 20
Figure 20
Comparing the correlations between linear dimension and weight for surface quality control.
Figure 21
Figure 21
Comparing the correlations between linear dimension and weight for linear dimension control (with strategy 2 for controlling parameters).
See this image and copyright information in PMC

References

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