A Novel Setup for Cavity Pressure and Temperature Measurements in Micro-Injection Moulding

2015 ◽  
Author(s):  
G. Trotta ◽  
V. Basile ◽  
I. Fassi
Keyword(s):  
Injection Moulding ◽  
Temperature Measurements ◽  
Cavity Pressure ◽  
Micro Injection Moulding

Influence of Injection and Cavity Pressure on the Demoulding Force in Micro-Injection Moulding

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
C. A. Griffiths ◽  
S. S. Dimov ◽  
S. G. Scholz ◽  
G. Tosello ◽  
A. Rees
Keyword(s):  
Experimental Study ◽  
Process Parameters ◽  
Design Of Experiment ◽  
Injection Moulding ◽  
Processing Conditions ◽  
Cavity Pressure ◽  
Mould Temperature ◽  
Melt Temperature ◽  
Injection Speed ◽  
Micro Injection Moulding

The paper reports an experimental study that investigates part demoulding behavior in micro-injection moulding with a focus on the effects of pressure and temperature on the demoulding forces. In particular, the demoulding performance of a representative microfluidics part was studied as a function of four process parameters, melt temperature, mould temperature, holding pressure, and injection speed, employing the design of experiment approach. In addition, the results obtained using different combinations of process parameters were analyzed to identify the best processing conditions in regards to demoulding behavior of microparts when utilizing a COC polymer to mould them.

scholarly journals A study of micromanufacturing process fingerprints in micro-injection moulding for machine learning and Industry 4.0 applications

2021 ◽  
Author(s):  
Mert Gülçür ◽  
Ben Whiteside
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Injection Moulding ◽  
Industry 4.0 ◽  
Supervised Machine Learning ◽  
Process Conditions ◽  
Learning Models ◽  
Micro Injection Moulding ◽  
Machine Learning Models

AbstractThis paper discusses micromanufacturing process quality proxies called “process fingerprints” in micro-injection moulding for establishing in-line quality assurance and machine learning models for Industry 4.0 applications. Process fingerprints that we present in this study are purely physical proxies of the product quality and need tangible rationale regarding their selection criteria such as sensitivity, cost-effectiveness, and robustness. Proposed methods and selection reasons for process fingerprints are also justified by analysing the temporally collected data with respect to the microreplication efficiency. Extracted process fingerprints were also used in a multiple linear regression scenario where they bring actionable insights for creating traceable and cost-effective supervised machine learning models in challenging micro-injection moulding environments. Multiple linear regression model demonstrated %84 accuracy in predicting the quality of the process, which is significant as far as the extreme process conditions and product features are concerned.

Biopolymers Processing Experimentation by Micro Injection Moulding

2011 ◽  
Author(s):  
Juan J. Marquez ◽  
Jesus Rueda ◽  
Francisco Chinesta ◽  
Yvan Chastel ◽  
Mohamed El Mansori
Keyword(s):  
Injection Moulding ◽  
Micro Injection Moulding

scholarly journals Micro-Injection Moulding In-Line Quality Assurance Based on Product and Process Fingerprints

Micromachines ◽  
2018 ◽  
Vol 9 (6) ◽  
pp. 293 ◽  
Author(s):  
Federico Baruffi ◽  
Matteo Calaon ◽  
Guido Tosello
Keyword(s):  
Quality Assurance ◽  
Injection Moulding ◽  
Micro Injection Moulding ◽  
Product And Process
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