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<p>To rapidly prototype the user’s final design, an effective additive manufacturing (AM) system is introduced. In recent years, the AM technique of fused deposition modeling (FDM) has gained considerable attention due to its lower computational cost and the ability to produce complex parts. Additionally, the performance of the AM machine improves with the initiation of printing connection lines and patches in the 3D printing strategy. In the past, the prediction methods did not provide the exact filter patches line prediction output because they lacked a decision function module. Therefore, a decision-based Chimp estimation model was proposed to predict the patches’ extracted location of the printed object and AM. The 3D-printed spectrometer is taken as the testing material for this study. Here, the filtering patches in the 3D-printed spectrometer are measured, and the Chimp fitness solution predicts the extracted filtering patches. After estimating the extracted filtering patches, the FDM is activated to fill the patches in the extracted regions. In the end, the robustness of the designed model was evaluated based on its accuracy, thermal conductivity, surface roughness, and error rate. In addition, the results obtained were compared with those of other existing models.</p>

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An optimized estimation in a 3D printed model spectrometer for predicting patches and the location of the printed object

  • 期刊论文

  • 发表日期：

    2026年2月9日

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  • Putta Nageswara Rao,
  • Lakshmi Vinitha,
  • Siva Sankara Babu Chinka,
  • Ramesh Lanka,
  • Vasanth Kumar

摘要

To rapidly prototype the user’s final design, an effective additive manufacturing (AM) system is introduced. In recent years, the AM technique of fused deposition modeling (FDM) has gained considerable attention due to its lower computational cost and the ability to produce complex parts. Additionally, the performance of the AM machine improves with the initiation of printing connection lines and patches in the 3D printing strategy. In the past, the prediction methods did not provide the exact filter patches line prediction output because they lacked a decision function module. Therefore, a decision-based Chimp estimation model was proposed to predict the patches’ extracted location of the printed object and AM. The 3D-printed spectrometer is taken as the testing material for this study. Here, the filtering patches in the 3D-printed spectrometer are measured, and the Chimp fitness solution predicts the extracted filtering patches. After estimating the extracted filtering patches, the FDM is activated to fill the patches in the extracted regions. In the end, the robustness of the designed model was evaluated based on its accuracy, thermal conductivity, surface roughness, and error rate. In addition, the results obtained were compared with those of other existing models.