Correlating nano-scale surface replication accuracy and cavity temperature in micro-injection moulding using in-line process control and high-speed thermal imaging

Baruffi, Federico; Gülçür, Mert; Calaon, Matteo; Romano, Jean–Michel; Penchev, Pavel; Dimov, Stefan Simeonov; Whiteside, Ben; Tosello, Guido

doi:10.1016/j.jmapro.2019.08.017

Cited by 31 publications

(18 citation statements)

References 37 publications

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“…In the injection molding process, one of the main operating parameters that affect the properties of the final object is the mold temperature [17,18]. Surface finishing, morphology distribution, and possibility to produce micro and nano-structures on the molded surface strongly depend on the temperature adopted in the mold and, in particular, on the cavity surface [19][20][21][22][23]. The mechanical properties have been found to be dependent on the mold temperature; in particular, the tensile strength of moldings made of polyether-ether-ketone increases with the mold temperature [24].…”

Section: Introductionmentioning

confidence: 99%

Effect of Rapid Mold Heating on the Structure and Performance of Injection-Molded Polypropylene

et al. 2020

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The tailoring by the process of the properties developed in the plastic objects is the more effective way to improve the sustainability of the plastic objects. The possibility to tailor to the final use the properties developed within the molded object requires further understanding of the relationship between the properties of the plastic objects and the process conduction. One of the main process parameters that allow adjusting the properties of molded objects is the mold temperature. In this work, a thin electrical heater was located below the cavity surface in order to obtain rapid and localized surface heating/cooling cycles during the injection molding process. An isotactic polypropylene was adopted for the molding tests, during which surface temperature was modulated in terms of values and heating times. The modulation of the cavity temperature was found able to control the distribution of relevant morphological characteristics, thus, properties along the sample thickness. In particular, lamellar thickness, crystallinity distribution, and orientation were analyzed by synchrotron X-ray experiments, and the morphology and elastic modulus were characterized by atomic force microscopy acquisitions carried out with a tool for the simultaneous nanomechanical characterization. The crystalline degree slightly increased with the cavity temperature, and this induced an increase in the elastic modulus when high temperatures were adopted for the cavity surface. The cavity temperature strongly influenced the orientation distribution that, on its turn, determined the highest values of the elastic modulus found in the shear layer. Furthermore, although the sample core, not experiencing a strong flow field, was not characterized by high levels of orientation, it might show high values of the elastic modulus if temperature and time during crystallization were sufficient. In particular, if the macromolecules spent adequate time at temperatures close to the crystallization temperature, they could achieve high levels of structuring and, thus, high values of elastic modulus.

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Section: Introductionmentioning

confidence: 99%

Effect of Rapid Mold Heating on the Structure and Performance of Injection-Molded Polypropylene

et al. 2020

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“…Linear regression is a statistical method that has been widely used for modelling process behaviours, product quality, and predictions based on dependant and independent variables [2,10,26]. More variables can be added to the linear regression models for training the predictive model towards a more accurate machine learning approach.…”

Section: Multiple Linear Regression (Mlr) Modelmentioning

confidence: 99%

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

Gülçür

Whiteside

2021

Int J Adv Manuf Technol

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This 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.

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“…3c). Flow visualisation has been a powerful method for studying flow behaviour, shrinkage, internal stresses and cooling behaviour of polymer melts in micromoulding research [2,21,26,27]. In order to carry out thermo-optical visualisation of the polymer flow during the filling stage of ultrasonic micromoulding, a sapphire window has been flush-mounted on the top mould (Fig.…”

Section: Tool Design For Ultrasonic Micromouldingmentioning

confidence: 99%

“…Micro-injection moulding or micromoulding technology has evolved into an established and highly-repeatable manufacturing process over the last two decades for producing miniature products with intricate features and micro-nano textured surfaces [1][2][3][4][5][6]. Despite this, some inherent aspects of this technique can prove problematic for certain applications.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic micromoulding: Process characterisation using extensive in-line monitoring for micro-scaled products

Gülçür

Brown

Gough

et al. 2020

Journal of Manufacturing Processes

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Correlating nano-scale surface replication accuracy and cavity temperature in micro-injection moulding using in-line process control and high-speed thermal imaging

Abstract: CitationBaruffi F, Gülçür M, Calaon M et al (2019) Correlating nanoscale surface replication accuracy and cavity temperature in micro-injection moulding using in-line process control and highspeed thermal imaging. Journal of Manufacturing Processes. 47: 367-381.Rights

Cited by 31 publications

References 37 publications

Effect of Rapid Mold Heating on the Structure and Performance of Injection-Molded Polypropylene

Effect of Rapid Mold Heating on the Structure and Performance of Injection-Molded Polypropylene

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

Ultrasonic micromoulding: Process characterisation using extensive in-line monitoring for micro-scaled products

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