Multi response optimization of injection moulding process parameters of polystyrene and polypropylene to minimize surface roughness and shrinkage’s using integrated approach of S/N ratio and composite desirability function

Jan, Qazi Muhammad Usman; Habib, Tufail; Noor, Sahar; Abas, Muhammad; Azim, Shakir; Yaseen, Qazi Muhammad

doi:10.1080/23311916.2020.1781424

Cited by 28 publications

(14 citation statements)

References 34 publications

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“…The values of the shrinkage of the material without reinforcement were obtained from the bibliography [ 31 , 32 , 33 ], which gave experimental shrinkage of the PP from 1.8% to 4.8%, depending on the direction of the gate and injection process conditions. Uzman Jan et al [ 34 ] studied the influence of the injection parameters on the shrinkage of polypropylene, obtaining, under the other conditions, an optimum temperature of 238° for reducing the shrinkage of the PP material. According to Kosciuszko et al [ 35 ], it should be emphasized that the geometry of the injection moldings made of semicrystalline materials, whose glass transition temperature is lower than the working temperature, is not stable after removing them from the injection mold and cooling to the ambient temperature.…”

Section: Resultsmentioning

confidence: 99%

Tomographic and Tension Analysis of Polypropylene Reinforced with Carbon Fiber Fabric by Injection Molding

Wieczorowski,

Pereira,

Prado

et al. 2023

Materials

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The use of thermoplastic materials has had significant growth in recent years. However, with great mechanical requirements, thermoplastics have limitations to their use. To improve these restrictions, these materials are reinforced to obtain better properties. Polypropylene is one of the most versatile polymers and is used in almost all modern industries. Thus, the aim of this study is to create composite materials that offer performance for various industrial fields using carbon fiber fabric reinforcement, which is an inexpensive material widely used by the aerospace, automotive, and marine industries. The samples are produced by the over-injection molding of polypropylene. The investigation is focused on the impact of two critical control parameters in the injection molding process: temperature and pressure. Twelve experiments have therefore been considered, taking into account the combination of three factors: the presence or absence of carbon fiber fabric reinforcement, three levels of temperature (200 °C, 220 °C, and 240 °C), and two injection pressures (5000 kPa and 10,000 kPa). To evaluate the influence of these factors, three analyses were carried out: first, on the samples’ shrinkage using a portable metrology-grade 3D laser scanner; second, on the internal defects using computed tomography (CT); and third, on the mechanical properties with tensile tests. From the results obtained, it is observed that the mold shrinkage fell slightly when PP samples were reinforced with carbon fiber, with both materials (PP and carbon-fiber-reinforced PP) having linear behavior with temperature. It is also noticed that polypropylene behaves as a crystalline material when processed at higher temperatures and pressures. From tests on the mechanical properties, it is concluded that the mean yield strength of PP-CF for injection temperatures of 220 °C and 240 °C represents an increase of 43% compared to the non-reinforced material.

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Section: Resultsmentioning

confidence: 99%

Tomographic and Tension Analysis of Polypropylene Reinforced with Carbon Fiber Fabric by Injection Molding

Wieczorowski,

Pereira,

Prado

et al. 2023

Materials

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“…The present study integrated the Taguchi design method with Moldex3D simulations [76] to determine optimal injection molding processing conditions in a more efficient and versatile manner, which gave rise to a better aesthetic appearance of the final sintered product and an enhanced mechanical strength [67,77]. Meanwhile, multi-response optimization of PIM process parameters of polystyrene and polypropylene to minimize surface roughness and shrinkage using an integrated approach of S/N ratio and composite desirability function was put forward by Usman et al [78]. Wang et al [39] conducted the orthogonal experiment to analyze the influence of injection process parameters on the evaluation index and employed the Taguchi method to obtain an signal-to-noise ratio.…”

Section: Taguchi Methodsmentioning

confidence: 99%

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

Zhao

Lian

Wang

et al. 2022

Int J Adv Manuf Technol

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The quality control of plastic products is an essential aspect of the plastic injection molding (PIM) process. However, the warpage and shrinkage deformations continue to exist because the PIM process is easily interfered with by several related or independent process parameters. Thus, great efforts have been devoted to optimizing process parameters to minimize the warpage and shrinkage deformations of products during the last decades. In this review, we begin by introducing the manufacturing process in PIM and the cause of warpage and shrinkage deformations, followed by the mechanism about how process parameters, like mold temperature, melt temperature, injection rate, injection pressure, holding pressure, holding and cooling duration, affect those defects. Then, we summarize the recent progress of the design of experiments and four advanced methods (artificial neural networks, genetic algorithm, response surface methodology, and Kriging model) on optimizing process parameters to minimize the warpage and shrinkage deformations. In the end, future perspectives of quality control in injection molding machines are discussed.

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“…However, during injection moulding, due to improper * Author to whom any correspondence should be addressed. parameters, substandard material pretreatment, accidental pollution, and other reasons, some surface defects that affect the function of moulded products inevitably occur [4][5][6][7][8]. Therefore, timely inspection for surface defects of injection moulded products is crucial for controlling product quality, optimising the manufacturing process, and enhancing the benefits of the enterprise.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products

Fan,

Qiu

2024

Meas. Sci. Technol.

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In modern industry, the surface defect inspection of injection moulded products is crucial for controlling product quality and optimizing the manufacturing process. With the development of optical measurement and computer technology, machine vision inspection methods have been widely adopted instead of manual inspection. However, current machine vision inspection methods are difficult to simultaneously ensure the accuracy and efficiency of surface defect inspection of injection moulded products. Considering this problem, a novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products is proposed. To train and evaluate the proposed deep learning algorithm, an image acquisition platform is established and the dataset of surface defects in moulded products is obtained. In the proposed deep learning algorithm, reparameterization-based convolution modules are employed for feature extraction and feature fusion. A median iterative clustering algorithm based on hierarchical clustering initialization is proposed to obtain prior anchors that are highly matched with the actual distribution of defect sizes. A novel Focus-Entire Union over Covering (Focus-EUoC) loss function is utilized for bounding box regression. On these bases, the proposed deep learning algorithm applied to machine vision inspection is evaluated on the dataset of surface defects in moulded products. The experimental results indicate that compared to the traditional inspection algorithms and other deep learning algorithms currently used in machine vision inspection, the proposed deep learning algorithm exhibits superior inspection accuracy and inspection efficiency on the acquired dataset. The inspection precision reaches 0.964, the inspection recall reaches 0.955, and the inference time for each subgraph is only 6.1ms, confirming its effectiveness.

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Multi response optimization of injection moulding process parameters of polystyrene and polypropylene to minimize surface roughness and shrinkage’s using integrated approach of S/N ratio and composite desirability function

Abstract: Yaseen | (2020) Multi response optimization of injection moulding process parameters of polystyrene and polypropylene to minimize surface roughness and shrinkage's using integrated approach of S/N ratio and composite desirability function, Cogent Engineering, 7:1, 1781424,

Cited by 28 publications

References 34 publications

Tomographic and Tension Analysis of Polypropylene Reinforced with Carbon Fiber Fabric by Injection Molding

Tomographic and Tension Analysis of Polypropylene Reinforced with Carbon Fiber Fabric by Injection Molding

Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: a review

A novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products

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