An experimental investigation to optimise injection moulding process parameters for plastic parts by using Taguchi method and multi-objective genetic algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Deepak Kumar ◽  
G.S. Dangayach ◽  
P.N. Rao
Keyword(s):  
Genetic Algorithm ◽  
Experimental Investigation ◽  
Taguchi Method ◽  
Process Parameters ◽  
Injection Moulding ◽  
Moulding Process ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Injection Moulding Process ◽  
Plastic Parts

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

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.

Investigating the Effects of Injection Moulding Process Parameters on Multiple Tensile Characteristics of Plastic Spur Gear via Experimental Approach

2013 ◽  
Vol 748 ◽  
pp. 544-548 ◽  
Author(s):  
Nik Mizamzul Mehat ◽  
Shahrul Kamaruddin ◽  
Abdul Rahim Othman
Keyword(s):  
Process Parameters ◽  
Injection Moulding ◽  
Experimental Approach ◽  
Low Cost ◽  
Spur Gear ◽  
Melt Temperature ◽  
Moulding Process ◽  
Packing Pressure ◽  
Injection Moulding Process ◽  
State Of Tension

This paper presents the original development of an experimental approach in studying the multiple tensile characterizations as key quality characteristics for several different plastic gear materials related to various parameters in injection moulding process. In this study, emphases are given on a new low-cost mechanism for the testing of the injection moulded plastic spur gear specimens with various teeth module. The testing fixture are developed and validated to provide uniform state of tension with series of plastic gear specimens produced in accordance with the systematically designed of experiment. The effects of changes in the process parameters including melt temperature, packing pressure, packing time and cooling time at three different levels on the elongation at break and ultimate strength of plastic gear is evaluated and studied through the proposed experimental approach.

Optimization of Injection Moulding Process Parameters for Manufacturing Plastic Components (PBT) Using Taguchi Method (TM)

2019 ◽  
Vol 969 ◽  
pp. 775-780
Author(s):  
Rajendra Khavekar ◽  
Hari Vasudevan ◽  
Gosar Vimal
Keyword(s):  
Taguchi Method ◽  
Process Parameters ◽  
Injection Moulding ◽  
Injection Pressure ◽  
Injection Time ◽  
Time Zone ◽  
Polybutylene Terephthalate ◽  
Influence Of Process Parameters ◽  
Pressure Injection ◽  
Injection Moulding Process

In this Paper, the application of Taguchi Method (TM) on the process parameters of Injection Moulding of Polybutylene Terephthalate (PBT) is presented. The influence of process parameters, such as Injection Pressure, Suckback Pressure, Injection Time, Cooling Time, Zone 1 Temperature & Zone 2 Temperature (Barrel Temperatures) on Dark Spots and Short Shots (defects) were investigated using the Orthogonal Array L16 of Taguchi Method for 6 factors at 2 levels each with the response being percent defectives. It was found that Injection Pressure, Injection Time & Zone 1 Temperature had a major effect on the response. After the application of Taguchi Method, the rejection rate dropped down to 5.84% from 11.33%, which is a 48.45% reduction.

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