Optimisation of Injection Moulding Process Parameter Using Taguchi and Desirability Function

2020 ◽  
pp. 247-260
Author(s):  
Vivekanandan Panneerselvam ◽  
Faiz Mohd Turan
Keyword(s):  
Injection Moulding ◽  
Desirability Function ◽  
Process Parameter ◽  
Moulding Process ◽  
Injection Moulding Process

scholarly journals Micro Injection Moulding Process Parameter Tuning

Procedia CIRP ◽  
2015 ◽  
Vol 33 ◽  
pp. 400-405 ◽  
Author(s):  
Michael Packianather ◽  
Christian Griffiths ◽  
Wan Kadir
Keyword(s):  
Injection Moulding ◽  
Parameter Tuning ◽  
Process Parameter ◽  
Moulding Process ◽  
Micro Injection Moulding ◽  
Injection Moulding Process

A Review of the Effect of Process Parameters on the Performance of Plastic Injection Molding Process to Control the Warpage in Plastics

2015 ◽  
Vol 830-831 ◽  
pp. 116-119 ◽  
Author(s):  
Gurjeet Singh ◽  
Mohan Kumar Pradhan ◽  
Ajay Verma
Keyword(s):  
Process Parameters ◽  
Injection Moulding ◽  
Process Parameter ◽  
Coolant Temperature ◽  
Injection Molding Process ◽  
Melt Temperature ◽  
Influence Of Process Parameters ◽  
Moulding Process ◽  
Injection Moulding Process ◽  
Plastic Injection

Quality control is important aspect of manufacturing process. The quality of product in injection moulding is influenced by injection moulding process parameter. A study of the influence of process parameters on the injection moulding process is presented. Statistically based model approach were studied. In this the process parameter that affect the injection moulding process are like injection time, injection pressure, packing pressure, packing time, cooling time, coolant temperature mold temperature, melt temperature are studied and compared. This paper deals with design of experiment approach to fine out optimal parameter setting. The comparison shows effect of parameters on injection moulding process.Key words: Plastic injection moulding, Orthogonal array, Cycle time

Evaluation of Cadmould Software to Simulate the Filling Phase of a Natural Rubber Compound in Injection Moulding Process

2013 ◽  
Vol 747 ◽  
pp. 571-574 ◽  
Author(s):  
Zulkifli Mohamad Ariff ◽  
T.H. Khang
Keyword(s):  
Natural Rubber ◽  
Injection Moulding ◽  
Input Data ◽  
Cure Kinetics ◽  
Rubber Compound ◽  
Moulding Process ◽  
Related Data ◽  
Mould Filling ◽  
Injection Moulding Process ◽  
Material Input

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.

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

2013 ◽  
Vol 554-557 ◽  
pp. 1669-1682 ◽  
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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