Shrinkage optimisation on the 3D printed part using Full Factorial Design (FFD) optimisation approach

Haidiezul, A. H. M.; Hazwan, M. H. M.; Lee, Woon Soon; Gunalan,; Najihah, Nur; Fadhli, Izzul

doi:10.1088/1757-899x/932/1/012109

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…[156,157] It may deform the surface from any plane or degree. The execution of the FFD model can be utilized to overcome the intermittently visible defects which helped to figure out the influencing factors such as delayed solidification, inhomogenous shrinkage, etc., [158][159][160] which were resolved by mathematical functions. [161] Among various defects, warpage is a major issue in FDM 3D printing of polyolefins due to their high crystallinity.…”

Section: Warpage Analysismentioning

confidence: 99%

Fused Deposition Modeling of Polyolefins: Challenges and Opportunities

Verma

Awasthi

Gupta

et al. 2022

Macro Materials & Eng

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Polyolefins are the largest class of commercially available synthetic polymers that are extensively used in a variety of applications from commodities to engineering owing to their low cost of production, good physico‐mechanical properties, light weight, good processability, and recyclability. Compared to conventional molding techniques, fused deposition modeling (FDM)‐based 3D printing is a smart manufacturing technology for thermoplastics due to its low cost, ease of production of complex geometrical parts, rapid prototyping, and scalable customization. FDM 3D printing can be an ideal manufacturing technology for polyolefins to manufacture various complex parts. However, FDM 3D‐printing of polyolefins is challenged bycritical printing problems like high warpage, dimensional inaccuracies, poor bed adhesion, and poor layer‐to‐layer adhesion. In this review, a fundamental understanding of polyolefins and their FDM 3D‐printing process is established, and the recent progress of FDM 3D printing of polyolefins is summarized. Furthermore, strategies to overcome warpage and to improve mechanical strength of the 3D‐printed polyolefins are provided. Finally, future prospectives of FDM 3D‐printing of polyolefins are critically discussed to inspire prospective research in this field. It is believed that this review article can be tremendously useful for research work related to FDM of polyolefin‐based materials.

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Section: Warpage Analysismentioning

confidence: 99%

Fused Deposition Modeling of Polyolefins: Challenges and Opportunities

Verma

Awasthi

Gupta

et al. 2022

Macro Materials & Eng

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“…This mechanical property stands as a cornerstone for components manufactured through this method, as their intended applications often demand a notable capacity to withstand such stress. These studies have embraced diverse statistical methodologies, including factorial design, as evidenced in works such as [4,5,6,7,8,9]. Additionally, the utilization of response surface methodology is apparent in studies such as [10,11,12,13,14], while the Taguchi approach has been implemented in investigations like [15,16,17,18,19].…”

Section: Introductionmentioning

confidence: 99%

Exploring the impact of 3D printing parameters on mechanical properties of ABS and PLA materials through machine learning analysis

Cudris Guerrero

2024

Investigación e Innovación en Ingenierías

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Objective: This research presents a comprehensive experimental study on the effect of temperature, material and process parameters related to tensile strength response in 3D printing manufacturing process with ABS material. Methodology or method: A Hyper Latin Square design was chosen for the experimental points’ distribution. Thirteen parameters with multiple levels are considered LAYERHEIGHT, WALLTHICKNESS, TOPBOT-TOMTHICKNESS, TOPBOTTOMLINEDIRECTION1, TOPBOTTOMLINEDIRECTION2, INFILLDENSITY, INFILLLINEDI- RECTION1, INFILLLINEDIRECTION2, PRINTSPEED, EXTRUSIONTEMP, BEDTEMP, WORKSPACETEMP and POSITION. Results: Type IV tensile specimens are fabricated and tested with a universal testing machine. Maximum stress is measured, evaluated and analyzed in three different building positions. Machine learning algorithm with Orange Data mining software are used to study underlying relations between factors and response. Experimental results indicate that INFILLDENSITY, TOPBOTTOMTHICKNESS and INFILLLINEDIRECTION1 has a strong positive correlation with mechanic properties for ABS and PLA. Meanwhile, TOPBOT-TOMLINEDIRECTION1, WORKSPACETEMPERATURE and PRINTSPEED has a negative correlation with tensile strength. Position 1 and 3 with line depositions parallel to Y axis produce the higher tensile strength response. Conclusions: Findings imply that machine algorithms can be used to study multiple parameters at a time.

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“…A large quantity of medical protective equipment, such as medical goggles [1], protective clothing [2,3], and disinfection medicine barrels, is produced to protect people from viruses by this technology during the period of COVID-19. However, the manufacture of these injection products mainly relies on traditional manual operations and trial-and-error methods [4][5][6], which has disadvantages in the part quality [7] and influences the efficiency and energy consumption [8,9] of a company. Therefore, many scientists are devoted to finding a more scientific method in the PIM process.…”

Section: Introductionmentioning

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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Shrinkage optimisation on the 3D printed part using Full Factorial Design (FFD) optimisation approach

Cited by 6 publications

References 3 publications

Fused Deposition Modeling of Polyolefins: Challenges and Opportunities

Fused Deposition Modeling of Polyolefins: Challenges and Opportunities

Exploring the impact of 3D printing parameters on mechanical properties of ABS and PLA materials through machine learning analysis

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

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