Prediction of weld-line width and sink-mark depth of plastic injection moulded parts using neural networks

Paturi, Uma Maheshwera Reddy; Kolluru, Sai Krishna Prashanth; Kalvakolanu, S.D.S. Abhiram

doi:10.1016/j.matpr.2023.02.295

Cited by 3 publications

(6 citation statements)

References 32 publications

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“…Sometimes flow marks and internal material flow orientations have also come with weld-line which is aesthetically not recommended. 9 Figure 4 shows the aesthetic effect of weld-line on components.…”

Section: Importance Of Weld-line Evaluationmentioning

confidence: 99%

“…In the presence of the weld‐line with black or other color materials, the weld‐lines are making a darker visual appearance on the part. Sometimes flow marks and internal material flow orientations have also come with weld‐line which is aesthetically not recommended 9 . Figure 4 shows the aesthetic effect of weld‐line on components.…”

Section: Importance Of Weld‐line Evaluationmentioning

confidence: 99%

“…However, they have now become as indispensable as the original sources themselves 6–8 . Plastics have achieved this feat due to their unrivaled adaptability and capacity to adjust their qualities, which other materials lack 1,9 . Out of many plastics, the engineering thermoplastics such as polyamide‐66 (PA‐66), polyamide‐6 (PA‐6), polyoxymethylene (POM), polybutylene terephthalate (PBT), polycarbonate (PC), etc.…”

Section: Introductionmentioning

confidence: 99%

“…[6][7][8] Plastics have achieved this feat due to their unrivaled adaptability and capacity to adjust their qualities, which other materials lack. 1,9 Out of many plastics, the engineering thermoplastics such as polyamide-66 (PA-66), polyamide-6 (PA-6), polyoxymethylene (POM), polybutylene terephthalate (PBT), polycarbonate (PC), etc. are commonly used materials in automobile, electrical, electronics and consumer industries.…”

mentioning

confidence: 99%

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Weld‐lines and its strength evaluation in injection molded parts: A review

Jadhav,

Gaval,

Solanke

et al. 2023

Polymer Engineering & Sci

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Injection molding manufacturing is immensely popular nowadays because of its excellent manufacturability for complex designs produced from a wide range of different engineering plastic materials. Weld‐line is a defect that arises in injection molding when two or more flow fronts collide. The weld‐lines are undesirable from both aesthetic and functional points of view and their effect is different for varied materials, process conditions, loadings, forms of weld‐line, etc. The presence of a weld‐line weakens components locally due to insufficient intermolecular diffusion, v‐notch formation, fiber anisotropy, insufficient process conditions, and so on. When a component is under load, crack propagation usually begins with a weak weld‐line area. Therefore, the designer should anticipate the presence of weld‐lines and their strength to avoid failure of components during operation. In this article, the review of published research work on weld‐line formation during injection molding has been summarized. The review focuses on the phenomenon of weld‐line formation, the type of weld‐lines, and the importance of weld‐line evaluations. The theoretical, experimental, and computational approaches to analyzing weld‐line and its strength are covered in detail. At last, methods for reducing weld‐lines and increasing the strength of weld‐line area have been discussed. This review also gathers data on weld‐line strength with respect to different materials.Highlights Details of weld‐line formation and its types. Importance of weld‐line evaluation. Methods of weld‐line strength evaluation. Methods of reduction of the number of weld‐line and its effect. Summary of weld‐line strength for different material.

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Section: Importance Of Weld-line Evaluationmentioning

confidence: 99%

Section: Importance Of Weld‐line Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Weld‐lines and its strength evaluation in injection molded parts: A review

Jadhav,

Gaval,

Solanke

et al. 2023

Polymer Engineering & Sci

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“…Wiangkham et al 24 employed an artificial intelligence approach, combining ANN and adaptive neural fuzzy reasoning system (ANFIS), to predict the mixed mode I/II fracture toughness of PMMA materials. Paturi et al 25 successfully predicted the optimal weld width and indentation depth of PMMA products in the plastic injection molding process using an ANN model. Chen et al 26 forecasted the water absorption of PMMA and its composite materials using a BP neural network.…”

Section: Introductionmentioning

confidence: 99%

Novel Life Prediction Method of PMMA for Cultural Relics Protection Based on the BP Neural Network

Zhang,

Wang,

Peng

et al. 2023

ACS Omega

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Poly(methyl methacrylate) (PMMA) is widely used in the preservation and exhibition of cultural relics in museums. Accurately predicting its service life can help avoid many negative effects caused by PMMA aging. To study the change in the yellowing index of PMMA after aging in a UV light environment, an aging experiment was conducted. A prediction model for the service life of PMMA was established using nonlinear curve fitting and a back propagation (BP) neural network. By comparing the goodness of fit, simulation and modeling capabilities of the initial data, and the predictive ability for new data, it was found that the BP neural network prediction model outperformed the nonlinear curve fitting prediction model. In this study, the service life of newly produced PMMA samples was calculated as 7.83, 8.47, and 8.42 years, based on the yellowing index of retired PMMA as a benchmark and using the output data from the BP neural network prediction model. At this time, the performance and exhibition effect of the PMMA are poor, and the batch of PMMA needs to be updated.

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Weight Optimization of Plastic Injection Moulded Electrical Wire Casing Thermoplastic using Hybrid RSM-Tunicate Swarm Algorithm

Barua,

Jeet,

Mishra

et al. 2023

E3S Web Conf.

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The need for fire-retardant material for electrical wire covers and cases is increasing as the global population continues to expand at an alarming rate. In addition to having good fire and chemical resistance, CPVC (chlorinated polyvinyl chloride) is widely accessible in a assortment of forms and sizes, comprising rods, sheets, and tubes. Plastic injection moulding (PIM) provides a method that allows for the production of CPVC items at a rapid pace and at a low cost. When these mouldings are lightweight, they may reduce the amount of non-biodegradable materials that are used in their construction. The present research gives an insight into the CPVC material moulding for electrical wire casing elbows using an injection moulding machine, which was previously unexplored. Four plastic injection moulding parameters were considered in order to reduce the weight of the elbow, including injection pressure, mould closing speed, mould pressure, and backpressure. The 27 tests were piloted in line with Response Surface Methodbased Box-Behnken Design, and the factors were optimised using Tunicate Swarm Algorithm, which was recently developed. In the case of the plastic injection-moulded item, the analysis of variance revealed that the most significant parameter in the weight reduction was the material used. It has been determined that mould pressure is the most critical factor impacting the weight of the item when it is manufactured. As a result, the optimum manufacture of injection-moulded CPVC components will be facilitated, resulting in optimised weight while also minimising production time and raw material waste for electrical wire casing.

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Prediction of weld-line width and sink-mark depth of plastic injection moulded parts using neural networks

Cited by 3 publications

References 32 publications

Weld‐lines and its strength evaluation in injection molded parts: A review

Weld‐lines and its strength evaluation in injection molded parts: A review

Novel Life Prediction Method of PMMA for Cultural Relics Protection Based on the BP Neural Network

Weight Optimization of Plastic Injection Moulded Electrical Wire Casing Thermoplastic using Hybrid RSM-Tunicate Swarm Algorithm

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