Chapter 32
Prediction of Width and Thickness
of Injection Molded Parts Using Machine
Learning Methods
Olga Ogorodnyk, Ole Vidar Lyngstad, Mats Larsen, and Kristian Martinsen
Abstract Injection molding is one of the major processes applied for production of
thermoplastic products. Thermoplastic materials are used in manufacturing of dozens
of products seen in everyday life, such as: car bumpers, children toys, bodies of electronic devices, etc. At the same time, plastic pollution is a well-known problem.
One of the sources of this pollution is plastic scrap, which might appear because of
using faulty process parameters during production process. To decrease amount of
scrap, injection molding needs to include better process and quality control routines.
Quality of a product can be defined in different ways and product dimensions can be
one of criteria for accepting or declining a product. The following paper applies
machine learning (ML) methods to predict width and thickness of the injection
molded HDPE dogbone specimens with 4 mm thickness based on process parameter values used to produce the parts. Data used for creation of regression models
with help of ML methods was acquired during an experiment, which included 160
machine runs during which 47 machine and process parameters were logged. Application of ML methods for training of product dimensions prediction models will
increase overall intelligence level of injection molding machines and their compliance with Industry 4.0 standards. Beforehand prediction of product’s dimensions will
allow to decrease amount of scrap and energy consumption. This will contribute to
more environmentally conscious use of thermoplastic materials and more sustainable
design of manufacturing systems.
Keywords Machine learning · Artificial neural networks · k-nearest neighbors ·
Decision trees · Injection molding · Quality prediction
O. Ogorodnyk (B) · K. Martinsen
Department of Manufacturing and Civil Engineering, Norwegian University of Science and
Technology (NTNU), Gjøvik, Norway
e-mail: olga.ogorodnyk@ntnu.no
O. V. Lyngstad
Department of Materials Technology, SINTEF Manufacturing, Raufoss, Norway
M. Larsen
Department of Production Technology, SINTEF Manufacturing, Raufoss, Norway
© Springer Nature Singapore Pte Ltd. 2021
Y. Kishita et al. (eds.), EcoDesign and Sustainability I, Sustainable Production, Life Cycle
Engineering and Management, https://doi.org/10.1007/978-981-15-6779-7_32
455
Prediction of Width and Thickness
of Injection Molded Parts Using Machine
Learning Methods
Olga Ogorodnyk, Ole Vidar Lyngstad, Mats Larsen, and Kristian Martinsen
Abstract Injection molding is one of the major processes applied for production of
thermoplastic products. Thermoplastic materials are used in manufacturing of dozens
of products seen in everyday life, such as: car bumpers, children toys, bodies of electronic devices, etc. At the same time, plastic pollution is a well-known problem.
One of the sources of this pollution is plastic scrap, which might appear because of
using faulty process parameters during production process. To decrease amount of
scrap, injection molding needs to include better process and quality control routines.
Quality of a product can be defined in different ways and product dimensions can be
one of criteria for accepting or declining a product. The following paper applies
machine learning (ML) methods to predict width and thickness of the injection
molded HDPE dogbone specimens with 4 mm thickness based on process parameter values used to produce the parts. Data used for creation of regression models
with help of ML methods was acquired during an experiment, which included 160
machine runs during which 47 machine and process parameters were logged. Application of ML methods for training of product dimensions prediction models will
increase overall intelligence level of injection molding machines and their compliance with Industry 4.0 standards. Beforehand prediction of product’s dimensions will
allow to decrease amount of scrap and energy consumption. This will contribute to
more environmentally conscious use of thermoplastic materials and more sustainable
design of manufacturing systems.
Keywords Machine learning · Artificial neural networks · k-nearest neighbors ·
Decision trees · Injection molding · Quality prediction
O. Ogorodnyk (B) · K. Martinsen
Department of Manufacturing and Civil Engineering, Norwegian University of Science and
Technology (NTNU), Gjøvik, Norway
e-mail: olga.ogorodnyk@ntnu.no
O. V. Lyngstad
Department of Materials Technology, SINTEF Manufacturing, Raufoss, Norway
M. Larsen
Department of Production Technology, SINTEF Manufacturing, Raufoss, Norway
© Springer Nature Singapore Pte Ltd. 2021
Y. Kishita et al. (eds.), EcoDesign and Sustainability I, Sustainable Production, Life Cycle
Engineering and Management, https://doi.org/10.1007/978-981-15-6779-7_32
455