Design of Experiments for Injection Molding

Kulkarni, Suhas

doi:10.3139/9783446433427.008

Cited by 2 publications

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“…The molder will perform a first article inspection to fully characterize the quality of the moldings. Process capability studies are often performed to optimize the molding process, perhaps with the use of scientific molding techniques and design of experiments [1]. Engineering change orders for the mold may be requested to remedy defects in the mold design or workmanship, increase the molded product quality, or otherwise improve molding productivity.…”

Section: 13develop Mold Operation and Maintenance Plansmentioning

confidence: 99%

Mold Commissioning

Kazmer¹

2016

Injection Mold Design Engineering

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Section: 13develop Mold Operation and Maintenance Plansmentioning

confidence: 99%

Mold Commissioning

Kazmer¹

2016

Injection Mold Design Engineering

View full text Add to dashboard Cite

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

et al. 2022

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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.

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Design of Experiments for Injection Molding

Cited by 2 publications

References 0 publications

Mold Commissioning

Mold Commissioning

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

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