Warpage optimisation in injection moulding process using response surface methodology (RSM)

The possibility of using Cadmould software to simulate the filling behaviour of a natural rubber compound during an injection moulding process was investigated. For the simulation process, the determination of required material input data involving the rheological and cure kinetics data of the designed rubber compound were conducted. It was discovered that the acquired data were able to function as reliable material input data as they were comparable with related data available in the Cadmould software materials database. Verification of the simulated filling profiles by experimental short shots specimens showed that the Cadmould Rubber Package was able to predict the realistic filling behaviour of the formulated natural rubber compound inside the mould cavity when the measured material data were utilized. Whereas, the usage of available material database from the software failed to model the mould filling progression of the intended natural rubber compound.

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Rule-based explanations based on ensemble machine learning for detecting sink mark defects in the injection moulding process

Journal of Manufacturing Systems ◽

10.1016/j.jmsy.2021.07.001 ◽

2021 ◽

Vol 60 ◽

pp. 392-405

Author(s):

Josue Obregon ◽

Jihoon Hong ◽

Jae-Yoon Jung

Keyword(s):

Machine Learning ◽

Injection Moulding ◽

Rule Based ◽

Moulding Process ◽

Ensemble Machine Learning ◽

Injection Moulding Process ◽

Sink Mark

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Multi-Response Parameters Optimisation for Energy-Efficient Injection Moulding Process via Dynamic Shainin DOE Method

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.554-557.1669 ◽

2013 ◽

Vol 554-557 ◽

pp. 1669-1682 ◽

Cited By ~ 1

Author(s):

Kam Hoe Yin ◽

Hui Leng Choo ◽

Dunant Halim ◽

Chris Rudd

Keyword(s):

Energy Consumption ◽

Process Parameters ◽

Injection Moulding ◽

Shop Floor ◽

Operational Experience ◽

Moulding Process ◽

Part Quality ◽

Control Factors ◽

Injection Moulding Process ◽

Signal Response

Process parameters optimisation has been identified as a potential approach to realise a greener injection moulding process. However, reduction in the process energy consumption does not necessarily imply a good part quality. An effective multi-response optimisation process can be demanding and often relies on extensive operational experience from human operators. Therefore, this research focuses on an attempt to develop a more user-friendly approach which could simultaneously deal with the requirements of energy efficiency and part quality. This research proposes a novel approach using a dynamic Shainin Design of Experiment (DOE) methodology to determine an optimal combination of process parameters used in the injection moulding process. The Shainin DOE method is adopted to pinpoint the most important factors on energy consumption and the targeted part quality whereas the ‘dynamic’ term refers to the signal-response system. The effectiveness of the proposed approach was illustrated by investigating the influence of various dominant parameters on the specific energy consumption (SEC) and the Charpy impact strength (CIS) of polypropylene (PP) material after being injection-moulded into impact test specimens. From the experimental results, barrel temperature was identified as the signal factor while mould temperature and cooling time were used as control factors in the full factorial experiments. Then, response function modelling (RFM) was built to characterise the signal-response relationship as a function of the control factors. Finally, RFM led to a trade-off solution where reducing part-to-part variation for CIS resulted in an increase of SEC. Therefore, the research outcomes have demonstrated that the proposed methodology can be practically applied at the factory shop floor to achieve different performance output targets specified by the customer or the manufacturer’s intent.

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