Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation

Hentati, Fatma; Hadriche, Ismail; Masmoudi, Nesrine; Bradaï, Chedly

doi:10.1007/s00170-019-04283-z

Cited by 38 publications

(22 citation statements)

References 22 publications

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“…In general, the Taguchi method [ 39 ] has three quality characteristics, i.e. the-bigger-the-better, the-nominal-the-best, and the-smaller-the-better.…”

Section: Resultsmentioning

confidence: 99%

Optimization of Process Parameters for Fabricating Polylactic Acid Filaments Using Design of Experiments Approach

2021

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The amount of wasted polylactic acid (PLA) is increasing because 3D printing services are an increasingly popular offering in many fields. The PLA is widely employed in the fused deposition modeling (FDM) since it is an environmentally friendly polymer. However, failed prototypes or physical models can generate substantial waste. In this study, the feasibility of recycling PLA waste plastic and re-extruded it into new PLA filaments was investigated. An automatic PLA filament extruder was first developed for fabricating new PLA filaments. This paper also discusses the process, challenges, and benefits of recycling PLA waste plastic in an effort to fabricate new PLA filaments more sustainable. It was found that it was possible to fabricate PLA filament using recycled PLA waste plastic. The production cost is only 60% of the commercially available PLA filament. The tensile strength of the developed PLA filament is approximately 1.1 times that of the commercially available PLA filament. The design of experiments approach was employed to investigate the optimal process parameters for fabricating PLA filaments. The most important control factor affecting the diameter of PLA filament is the barrel temperature, followed by recycled material addition ratio, extrusion speed, and cooling distance. The optimal process parameters for fabricating PLA filament with a diameter of 1.7 mm include the barrel temperature of 184 °C, extrusion speed of 490 mm/min, cooling distance of 57.5 mm, and recycled material addition ratio of 40%.

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“…In general, the Taguchi method [ 39 ] has three quality characteristics, i.e. the-bigger-the-better, the-nominal-the-best, and the-smaller-the-better.…”

Section: Resultsmentioning

confidence: 99%

Optimization of Process Parameters for Fabricating Polylactic Acid Filaments Using Design of Experiments Approach

2021

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“…SKG Company recognized by the injection and metallization of the PC/ABS automotive parts, discovered the occurrence of some defects in the surfaces of the injected parts. These defects caused a high rejection rate [17].…”

Section: Industrial Contextmentioning

confidence: 99%

“…The commercial PC/ABS blend used in this investigation was the T45 PG. This blend was proximately oven dried at 120°C for 4 h [17]. Its mechanical and thermal properties are exhibited in Table 1.…”

Section: Materials Elaborationmentioning

confidence: 99%

Influence of Injection Molding Parameters on the Mechanical Properties of Injected PC/ABS Parts

Hentati¹,

Masmoudi²

2021

Quality Control - Intelligent Manufacturing, Robust Design and Charts

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This study optimized the influence of process parameters on the mechanical properties during injection molding (IM) of PC/ABS blend. The Taguchi method of design of experiments (DOE) was employed to optimize the process parameters and to increase the tensile strength and the elasticity module. Taguchi’s L9 (34) orthogonal array design was employed for the experimental plan. Process parameters of the injection molding such as material temperature, injection pressure, holding time, and mold temperature were studied with three levels. The Signal to noise (S/N) ratio for mechanical properties of PC/ABS blend using the Taguchi method was calculated. Taguchi’s results proposed two sets of optimal injection parameters conditions to achieve the best mechanical characteristics (σ, E). The (S/N) ratio results proved that the injection pressure was the more prominent than the other IM process parameters for the tensile strength, and the material temperature was the more prominent for the elasticity module.

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“…more energy during (mass) production. While arbitrary optimization by expert knowledge [11,47], i.e., by trial-and-error, or processoriented optimization by simulation [6,10,34] is widespread, objective optimization can be achieved with unbiased mathematical approaches such as artificial neural networks (ANNs). ANNs are vastly audited methods to model the correspondence between IMmachine and process parameters as input and part quality parameters as output [15,75,83,90,92].…”

Section: Transfer Learning For Injection Moldingmentioning

confidence: 99%

Privacy-Preserving Production Process Parameter Exchange

Pennekamp

Buchholz

Lockner

et al. 2020

Annual Computer Security Applications Conference

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Optimization of the injection molding process for the PC/ABS parts by integrating Taguchi approach and CAE simulation

Cited by 38 publications

References 22 publications

Optimization of Process Parameters for Fabricating Polylactic Acid Filaments Using Design of Experiments Approach

Optimization of Process Parameters for Fabricating Polylactic Acid Filaments Using Design of Experiments Approach

Influence of Injection Molding Parameters on the Mechanical Properties of Injected PC/ABS Parts

Privacy-Preserving Production Process Parameter Exchange

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