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<p>Additive manufacturing (AM) outperforms the traditional subtractive techniques, offering advantages across applications. Among AM methods, fused deposition modeling (FDM) 3D printing stands out for its affordability and precision. This study focuses on the design, manufacturing, and evaluation of a cost-effective FDM 3D printer with enhanced quality and efficiency, utilizing a Cartesian mechanical system modeled and tested in SolidWorks with a factor of safety between 1.6 and 2. An open-source computer-aided design and manufacturing software package is used for object model preparation and machine control. To evaluate performance, 27 samples were printed, varying printing temperature (T), layer height (LH), nozzle size (NS), print speed (S), and infill density (ID). The Taguchi L27 orthogonal array was used for the Design of Experiments. Performance metrics included printing time (t), surface roughness (SR), and geometric errors: flatness (F), roundness (R), parallelism (P), and dimensional accuracy (A%). The results revealed that single-response analysis revealed LH as the most influential parameter for SR, F, P, and t. T had the greatest impact on R, while NS primarily affected A%. But the multi-response analysis identified NS as the dominant factor for optimizing SR, P, F, R, t, and A%. The optimal setting for the responses are as follows: SR: LH at level 1, NS at level 2, ID at level 3, S at level 1, and T at level 3; F: LH at level 3, S at level 1, T at level 1, ID at level 2, and NS at level 2; R: T at level 1, NS at level 3, S at level 1, LH at level 1, and ID at level 2; P: LH at level 2, ID at level 2, T at level 1, S at level 2, and NS at level 1; A%: LH at level 2, ID at level 2, T at level 1, S at level 2, and NS at level 1; and t: LH at level 3, NS at level 3, ID at level 1, S at level 2, and T at level 2. For achieving a higher MRPI, the optimal settings are as follows: NS at level 3, T at level 2, ID at level 1, LH at level 2, and S at level 3. Empirical models and regression equations were derived, demonstrating good predictive accuracy.</p>

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Design and performance optimization of a cost-effective FDM 3D printer based on surface roughness and geometric errors

  • 期刊论文

  • 发表日期：

    2025年5月28日

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  • Sabreen Abdallah Abdelwahab,
  • Hassan Ahmed Hassan,
  • Sayed Abdelrahman,
  • Ahmed I. Ali,
  • Dongwhi Choi,
  • Hamdy Abo El daheb

摘要

Additive manufacturing (AM) outperforms the traditional subtractive techniques, offering advantages across applications. Among AM methods, fused deposition modeling (FDM) 3D printing stands out for its affordability and precision. This study focuses on the design, manufacturing, and evaluation of a cost-effective FDM 3D printer with enhanced quality and efficiency, utilizing a Cartesian mechanical system modeled and tested in SolidWorks with a factor of safety between 1.6 and 2. An open-source computer-aided design and manufacturing software package is used for object model preparation and machine control. To evaluate performance, 27 samples were printed, varying printing temperature (T), layer height (LH), nozzle size (NS), print speed (S), and infill density (ID). The Taguchi L27 orthogonal array was used for the Design of Experiments. Performance metrics included printing time (t), surface roughness (SR), and geometric errors: flatness (F), roundness (R), parallelism (P), and dimensional accuracy (A%). The results revealed that single-response analysis revealed LH as the most influential parameter for SR, F, P, and t. T had the greatest impact on R, while NS primarily affected A%. But the multi-response analysis identified NS as the dominant factor for optimizing SR, P, F, R, t, and A%. The optimal setting for the responses are as follows: SR: LH at level 1, NS at level 2, ID at level 3, S at level 1, and T at level 3; F: LH at level 3, S at level 1, T at level 1, ID at level 2, and NS at level 2; R: T at level 1, NS at level 3, S at level 1, LH at level 1, and ID at level 2; P: LH at level 2, ID at level 2, T at level 1, S at level 2, and NS at level 1; A%: LH at level 2, ID at level 2, T at level 1, S at level 2, and NS at level 1; and t: LH at level 3, NS at level 3, ID at level 1, S at level 2, and T at level 2. For achieving a higher MRPI, the optimal settings are as follows: NS at level 3, T at level 2, ID at level 1, LH at level 2, and S at level 3. Empirical models and regression equations were derived, demonstrating good predictive accuracy.