Part quality investigation in fused deposition modelling using machine learning classifiers

Potnis, Mihir S.; Singh, Aayushi; Jatti, Vijaykumar S.; Sapre, Mandar S.; Pathak, Shreyansh; Joshi, Shrey; Jatti, Ashwini V.

doi:10.1007/s12008-023-01493-4

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…The Fused Deposition Modeling method, uses thermoplastic polymers such as polylactic acid to build three-dimensional objects layer by layer. This process involves preparing a computer-aided FIGURE 1 (A) FDM printing setup (Potnis et al, 2023). (B) Schematic sketch of Tensile Specimen (Jatti et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

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Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study

Jatti,

Tamboli,

Shaikh

et al. 2024

Front. Mater.

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This research focuses on the relationship between the tensile strength of PLA material and several 3D printing parameters, such as infill density, layer height, print speed, and extrusion temperature, utilizing the Fused Deposition Modeling (FDM) method of Additive Manufacturing (AM). Tensile strength of the samples was determined in compliance with ASTM D638 standard, and the experiments were carried out according to a planned arrangement. Six distinct methods were used to optimize the tensile strength: Particle Swarm Optimization (PSO), Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Simulated Annealing (SA), and Cohort Intelligence (CI). Several runs of the optimization methods demonstrated their consistency in producing the same values of tensile strength, indicating their reliability. The optimization results showed that JAYA performed better than the other algorithms, resulting in a material with the maximum tensile strength of 55.475 N/mm2. Validation experiments were carried out to confirm the efficacy of these algorithms. The results showed that the ideal input parameters produced tensile strength values that closely matched the anticipated values with a low percentage error. The benefits of applying these algorithms to improve the tensile strength of PLA materials for 3D printing are demonstrated by this study, which also offers insightful information about how to optimize FDM procedures.

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Section: Methodsmentioning

confidence: 99%

“…(B) Schematic sketch of Tensile Specimen (Jatti et al, 2022). (C) 3D printed tensile specimen (Potnis et al, 2023). (D) Tensile test setup.…”

Section: Methodsmentioning

confidence: 99%

Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study

Jatti,

Tamboli,

Shaikh

et al. 2024

Front. Mater.

Self Cite

View full text Add to dashboard Cite

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A review of artificial intelligent methods for machined surface roughness prediction

Yang,

Zheng,

Zhang

2024

Tribology International

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Enhancing of Surface Quality of FDM Moulded Materials through Hybrid Techniques

Jabłońska,

Łastowska

2024

Materials

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With the rapid advancement of 3D-printing technology, additive manufacturing using FDM extrusion has emerged as a prominent method in manufacturing. However, it encounters certain limitations, notably in surface quality and dimensional accuracy. Addressing issues related to stability and surface roughness necessitates the integration of 3D-printing technology with traditional machining, a strategy known as the hybrid technique. This paper presents a study of the surface geometric parameters and microstructure of plastic parts produced by FDM. Sleeve-shaped samples were 3D-printed from polyethylene terephthalate glycol material using variable layer heights of 0.1 mm and 0.2 mm and then subjected to the turning process with PVD-coated DCMT11T304 turning inserts using variable cutting parameters. The cutting depth was constant at 0.82 mm. Surface roughness values were correlated with the cutting tool feed rate and the printing layer height applied. The selected specimen’s microstructure was studied with a Zeiss EVO MA 15 scanning electron microscope. The roundness was measured with a Keyence VR-6200 3D optical profilometer. The research results confirmed that the additional application of turning, combined with a reduction in the feed rate (0.0506 mm/rev) and the height of the printed layer (0.1 mm), reduced the surface roughness of the sleeve (Ra = 1.94 μm) and increased its geometric accuracy.

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Part quality investigation in fused deposition modelling using machine learning classifiers

Cited by 3 publications

References 29 publications

Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study

Optimization of tensile strength in 3D printed PLA parts via meta-heuristic approaches: a comparative study

A review of artificial intelligent methods for machined surface roughness prediction

Enhancing of Surface Quality of FDM Moulded Materials through Hybrid Techniques

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