Part Mass Estimation Strategy for Injection Molding Machines

Maderthaner, Jakob; Kugi, Andreas; Kemmetmüller, Wolfgang

doi:10.1016/j.ifacol.2020.12.2775

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Common setting parameters consist of shooting seat position, shooting speed, V/P switching, material temperature, molding position limit switch, mold clamping force, etc. (Maderthaner, Kugi, and Kemmetmüller 2020).…”

Section: Injection Molding Machinementioning

confidence: 99%

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Huang,

Chen,

Kanga

et al. 2024

Preprint

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In the past, most of traditional master craftsmen always adopted the acoustic actions to recognition the situation of machine. Along with the development of time and technology, the mode of industry has changed with the Fourth Industrial Revolution (Industry 4.0). The long been known for the mother of industry, mold industry, has been inevitably impacted by Industry 4.0. This research stems from the structure of the six-level IoT model, through Internet connecting sensors, data collection, and the appropriate implementation of human and machine interface to intellectualize the injection molding machine. This research has collected 130 times of audio frequency, and there were 53 effective data sets, in sum there were 34,030,640 datasets. There were 5 manufacturing actions of petroleum molding machines that were successfully identified. Due to the low accuracy of one of the manufacturing actions, the training of audio frequency is based on the other four. In the end, there are 93.64% of accurate AI intelligent identifying models. Concurrently, through labeling the audio characteristics of different manufacturing parameters, the model recognizing audio characteristics from injection molding machines under different injection speed and rotation speed parameters is successfully trained. It is expected that in the future, other researchers can use this research as a reference to further strengthen the correlation between audio characteristics and injection molding machines to engage a more in-depth and diverse application of this topic. JEL Classification: C80, C88, C90.

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Section: Injection Molding Machinementioning

confidence: 99%

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Huang,

Chen,

Kanga

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The limit of experimental based models compared to theoretical models based on, e.g., physics is that it is only valid in the tested region. However, in this case, the experimental approach is preferred due to the simplicity of the test making it beneficial, compared to more complex models such as [16,20,21,34,[36][37][38]. Nine experiments are conducted, where the five ABS materials are moulded at three dif-ferent holding pressures.…”

Section: Process Modelmentioning

confidence: 99%

Cycle-Based Control of Injection Moulding Process in Presence of Material Dual Sourcing Using Mass Feedback

Hertz,

Therkelsen,

Kristiansen

et al. 2024

Polymers

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The low cost and precise tolerances of plastic injection moulded products are a major reason for the popularity of the manufacturing method. The tolerances are greatly influenced by the equipment, raw material and moulding process. One challenge is the raw material variation. This paper presents a production process using cycle based feedback of cycle mass, for control of part properties in the presence of material variation from dual sourcing. The part properties considered are part mass and outer dimensions. The process uses direct cycle mass feedback without additional process measurements in the proposed controller structure. The designed controller structure is tested in a multi-cavity mould while using raw materials from multiple vendors, encompassing five different grades. The results show a total decrease in part mass variance of approximately 50% and a decrease of length and width variance of approximately 40% compared to a moulding process with fixed settings.

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“…Consequently, monitoring pressure in the cavity and runner system is an excellent solution to collect real-time data, control the quality of injection molded parts [ 19 ], as well as test the injection mold itself [ 21 , 22 ]. Kemmetmuller et al [ 23 ] proposed a method to estimate part mass using pressure and temperature sensors installed in the cavity of an S-shaped test mold.…”

Section: Introductionmentioning

confidence: 99%

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

Párizs

Török

Ageyeva

et al. 2023

Sensors

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The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements.

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Part Mass Estimation Strategy for Injection Molding Machines

Cited by 4 publications

References 13 publications

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

AI-Enhanced Audio-Based Predictive Control for Injection Molding Machines in the Era of IoT

Cycle-Based Control of Injection Moulding Process in Presence of Material Dual Sourcing Using Mass Feedback

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding

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