scholarly journals Optimization of Injection-Molding Process for Thin-Walled Polypropylene Part Using Artificial Neural Network and Taguchi Techniques

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4158
Author(s):  
Mehdi Moayyedian ◽  
Ali Dinc ◽  
Ali Mamedov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Holding Time ◽  
Injection Molding Process ◽  
Molding Process ◽  
Filling Time ◽  
Thin Walled

Plastics are commonly used engineering materials, and the injection-molding process is well known as an efficient and economic manufacturing technique for producing plastic parts with various shapes and complex geometries. However, there are certain manufacturing defects related to the injection-molding process, such as short shot, shrinkage, and warpage. This research aims to find optimum process parameters for high-quality end products with minimum defect possibility. The Artificial Neural Network and Taguchi Techniques are used to find a set of optimal process parameters. The Analytic Hierarchy Process is used to calculate the weight of each defect in the proposed thin-walled part. The Finite Element Analysis (FEA) using SolidWorks plastics is used to simulate the injection-molding process for polypropylene parts and validate the proposed optimal set of process parameters. Results showed the best end-product quality was achieved at a filling time of 1 s, cooling time of 3 s, pressure-holding time of 3 s, and melt temperature of 230 °C. The end-product quality was mostly influenced by filling time, followed by the pressure-holding time. It was found that the margin of error for the proposed optimization methods was 1.5%, resulting from any uncontrollable parameters affecting the injection-molding process.

scholarly journals Optimization of injection molding process for car fender in consideration of energy efficiency and product quality

2014 ◽  
Vol 1 (4) ◽  
pp. 256-265 ◽  
Author(s):  
Hong Seok Park ◽  
Trung Thanh Nguyen
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Injection Molding ◽  
Product Quality ◽  
Process Parameters ◽  
Optimization Problems ◽  
Computational Cost ◽  
Injection Molding Process ◽  
Molding Process ◽  
Nsga Ii

Abstract Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using nondominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

The Effect of Injection Molding Process Parameters on the Buckling Properties of PBT/Short Glass Fiber Composites

2011 ◽  
Vol 284-286 ◽  
pp. 550-556 ◽  
Author(s):  
Ming Hsiung Ho ◽  
Pin Ning Wang ◽  
Chin Ping Fung
Keyword(s):  
Injection Molding ◽  
Glass Fiber ◽  
Process Parameters ◽  
Injection Molding Process ◽  
Skin Area ◽  
Molding Process ◽  
Melt Temperature ◽  
Short Glass Fiber ◽  
Filling Time ◽  
Short Glass

This study investigates the effect of various injection molding process parameters and fiber amount on buckling properties of Polybutylene Terephthalate (PBT)/short glass fiber composite. The buckling specimens were prepared under injection molding process. These forming parameters about filling time, melt temperature and mold temperature that govern injection molding process are discussed. The buckling properties of neat PBT, 15 wt%, and 30 wt% are obtained using two ends fixed fixture and computerized closed-loop server-hydraulic material testing system. The fracture surfaces are observed by scanning electron microscopy (SEM). The global buckling forces are raised when increased the fiber weight percentage of PBT. Also, the fracture mechanisms in PBT and short glass fiber matrix are fiber pullout in skin area and fiber broken at core area. It is found that the addition of short glass fiber can significantly strengthen neat PBT.

scholarly journals Optimization for Reducing Warpage in Injection Moulding

2018 ◽  
Vol 2 (5) ◽  
pp. 25-31
Author(s):  
Keyword(s):  
Injection Molding ◽  
Process Parameters ◽  
Large Scale ◽  
Simulation Software ◽  
Optimum Process ◽  
Injection Molding Process ◽  
Molding Process ◽  
Melt Temperature ◽  
Product Cycles ◽  
Filling Time

Injection molding is a standout technique utilized for the fabrication of thermoplastic parts in industry due to short product cycles, high part quality, good mechanical properties and low cost for large scale manufacturing. In molded case circuit breaker (MCCB), Trip-bar is one of the most critical components as safety is concerned which is manufactured by injection molding process. To get it manufactured within the specified warpage and deformities free, number of mold flow simulations is carried out using Creo-MoldFlow. The outcomes of the simulation are used to design the mold tool and the process parameters for injection molding are optimized. For process parameter optimization Taguchi based experimental design and ANOVA analysis is done. The objective of this work is to optimize the injection molding process parameters such as filling time, melt temperature and mold temperature to minimize the warpage. CAE flow simulation software is used to simulate the process and Grey Relational Analysis (GRA) is used to find out optimum process parameters.

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