3D Modellemelerin Boyutsal Doğruluk ve Hassasiyet Açısından Bir Karşılaştırma Çalışması

Year 2022, , 24 - 29, 28.06.2022

Mehmet Altuğ

https://doi.org/10.46460/ijiea.1012067

Abstract

Bu çalışmada, Otomatik yüzey modelleme (ASm) ve Parametrik modelleme (Pm) olarak Polyjet ve FDM (Fused Deposition Modeling) yöntemleri ile üretilen 3B modellerin boyutlarındaki farklılıklar araştırılmıştır. Burada amaç modelleme yöntemlerinin boyutsal doğruluk ve hassasiyet üzerindeki etkilerini ortaya koymaktır. Malzeme olarak silindirik, düz ve amorf yüzeylere sahip bir bileşen seçilmiştir. Bu bileşenin nominal verileri kullanılarak 3D yazıcıda Polyjet ve FDM yöntemleri kullanılmıştır. Daha sonra, yukarıda belirtilen parçaları taramak için bir 3D tarayıcı kullanılmıştır. Bu taramalar ASm ve Pm olmak üzere iki farklı modelleme yöntemi ile yeniden modellenmiştir. Modellemeden sonra ölçülen (tarama) veriler nominal verilerle karşılaştırılmıştır. Sonuçların büyüklüğü açısından farklılıklar matematiksel olarak ortaya konmuştur. Elde edilen sonuçlara göre Polyjet'in ASm ve Pm yöntemleriyle ürettiği parçaları karşılaştırdığımızda ASm'nin tüm yüzeyde Pm'ye göre daha iyi sonuçlar verdiğini gözlemliyoruz. Ayrıca ASm'nin tüm yüzeyde Pm'ye göre daha az maksimum hata normuna sahiptir. Öte yandan, FDM tarafından hem ASm hem de Pm kullanılarak üretilen numuneler tüm yüzeyde daha iyi sonuçlar vermiş, ancak ASm, Pm’ye göre nispeten daha az maksimum hata normuna sahip olduğu tespit edilmiştir.

Keywords

3D tarama, 3D yazıcı, Hassasiyet, Otomatik Yüzey modelleme, Parametrik modelleme, Tersine Mühendislik

A Comparison Study in Terms of Dimensional Accuracy and Precision Of 3D Modeling

Abstract

In this study, the differences in the dimensions of the 3D models produced by Polyjet and FDM (Fused Deposition Modeling) methods as Auto surface modeling (ASm) and Parametric modeling (Pm) were investigated. Here, our purpose is to demonstrate the effects of the modeling methods on the dimensional accuracy and precision. A component having cylindrical, plane and amorphous surfaces have been selected as a sample material. Polyjet and FDM methods have been used in 3D printer using the nominal data of this component. Then a 3D scanner have been used to scan those aforementioned parts. These scans have been remodeled with two different modeling methods, namely, ASm and Pm. After modeling, the measured (scan) data has been compared with the nominal data. Differences in terms of the size of the results were revealed mathematically. According to the results obtained, when we compare the parts produced by Polyjet by ASm and Pm methods, we observe that ASm gives better results all over the surface than Pm. Also the former one has less maximum error norm than the later one. On the other hand the samples produced by FDM using both ASm and Pm give better results all over the surface, but the former one has relatively less maximum error norm than the later one.

Keywords

3D scanning, 3D printing, Accuracy, Auto surface modeling, Parametric modeling, Reverse Engineering

References

• [1] Boschetto, A., Giordano,V., & Veniali ,F. (2013). 3D roughness profile model in fused deposition modelling. Rapid Prototyping Journal, 19(4), 240-252.
• [2] Dietricha, C.A., Enderb ,A., Baumgartner, S., & Mehld, A. (2017). A validation study of reconstructed rapid prototyping models produced by two Technologies. Angle Orthodontist, 87(5), 782-786.
• [3] Mallepree,T., & Bergers ,D. (2009). Accuracy of medical RP models. Rapid Prototyping Journal, 15(5), 325-332.
• [4] Johnson,W.M., Rowell ,M., Deason ,B., & Eubanks ,M. (2014). Comparative evaluation of an open-source FDM system. Rapid Prototyping Journal, 20(3), 205–214.
• [5] Armillotta, A., Bianchi,S., Cavallaro,M., & Minnella, S. (2017). Edge quality in fused deposition modeling: II. experimental verification. Rapid Prototyping Journal, 23(4), 686-695.
• [6] Cheng, L., Zhang, P., Biyikli, E., Bai, J., Robbins, J., & To, A. (2017). Efficient design optimization of variable-density cellular structures for additive manufacturing: theory and experimental validation. Rapid Prototyping Journal, 23(4), 660-677.
• [7] Anwer,N., & Mathieu, L. (2016). From reverse engineering to shape engineering in mechanical design. CIRP Annals-Manufacturing Technology, 65(1), 165–168.
• [8] Van Eijnatten, M., Berger, F.H., De Graaf, P., Koivisto, J., Forouzanfar, T., & Wolff, J. (2017). Influence of CT parameters on STL model accuracy. Rapid Prototyping Journal, 23(4), 678-685.
• [9] Chiu, S.-H., Chen, K.-T., Wicaksono ,S.T., Tsai ,J.-R., & Pong ,S.-H. (2015). Process parameters optimization for area-forming rapid prototyping system. Rapid Prototyping Journal, 21(1), 70-78.
• [10] Aroca ,R.V., Ventura,C.E.H., De Mello, I., & Pazelli, T.F.P.A.T. (2017). Sequential additive manufacturing: automatic manipulation of 3D printed parts. Rapid Prototyping Journal, 23(4), 653-659.
• [11] Anwer, N., Schleich, B., Mathieu, L., & Wartzack ,S. (2014). From solid modelling to skin model shapes: Shifting paradigms in computer-aided tolerancing. CIRP Annals-Manufacturing Technology, 63(1), 137-140.
• [12] De Villiers, H., Van Zijl, L., & Niesler, T. (2015). High-level Rapid Prototyping of Graphical Models, Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, (pp.130-135).
• [13] Doubenskaia, M., Grigoriev ,S., Zhirnov ,I., & Smurov, I. (2016). Parametric analysis of SLM using comprehensive optical monitoring. Rapid Prototyping Journal, 22(1), 1-20.
• [14] Wang, J., & Yu, Z. (2011). Quadratic curve and surface fitting via squared distance minimization. Computers & Graphics, 35(6), 1035-1050.
• [15] Schleicha, B., Anwer, N., Mathieu, L., & Wartzacka, S. (2014). Skin Model Shapes: A new paradigm shift for geometric variations modelling in mechanical engineering. Computer-Aided Design, 50, 1-15.
• [16] Pattnaik, S., Jha, P.K., & Karunakar, D.B. (2013). A review of rapid prototyping integrated investment casting processes. Proc IMechE Part L: J Materials: Design and Applications, 228(4), 249-277.
• [17] Salmi, M., Paloheimo, K.S., Tuomi, J., Wolff, J., & Mäkitie, A. (2013). Accuracy of medical models made by additive manufacturing. Journal of Cranio-Maxillo-Facial Surgery, 41(7), 603-609.
• [18] Majstorovic,V., Trajanovic, M., Vitkovic,N., & Stojkovic, M. (2013). Reverse engineering of human bones by using method of anatomical features. CIRP Annals-Manufacturing Technology, 62(1), 167-170.
• [19] Stark, R., Grosser, & H., Müller, P. (2013). Product analysis automation for digital MRO based on intelligent 3D data acquisition. CIRP Annals-Manufacturing Technology, 62(1), 123-126.
• [20] Isa, M.A., & Lazoglu, I. (2017). Design and analysis of a 3D laser scanner. Measurement, 111, 122-133.
• [21] Molleda, J., Usamentiaga, R., Garcı´a, D.F., Bulnes, F.G., Espina, A., Dieye, B., & Smith, L.N. (2013). An improved 3D imaging system for dimensional quality inspection of rolled products in the metal industry. Computers in Industry, 64(9), 1186-1200.
• [22] Popov, I., Onuh, S., & Dotchev, K. (2010). Dimensional error analysis in point cloud-based inspection using a non-contact method for data acquisition. Measurement Science and Technology, 21(7), 1-8.
• [23] Isheil, A., Gonnet,J.P., Joannic,D., & Fontaine, J.F. (2011). Systematic error correction of a 3D laser scanning measurement device. Optics and Lasers in Engineering, 49(1), 16-24.
• [24] Xi, F., Liu, Y., & Feng, H.Y. (2001). Error Compensation for Three-Dimensional Line Laser Scanning Data. International Journal Advanced Manufacturing Technology, 18(3), 211-216.
• [25] Wang, Y., & Feng, H.Y. (2014). Modeling outlier formation in scanning reflective surfaces using a laser stripe scanner. Measurement, 57, 108-121.
• [26] Wang, Y., & Feng, H.Y. (2016). Effects of scanning orientation on outlier formation in 3D laser scanning of reflective surfaces. Optics and Lasers in Engineering, 81, 35-45.
• [27] Besic, I., Van Gestel, N., Kruth, J.P., Bleys, P., & Hodolic, J. (2011). Accuracy improvement of laser line scanning for feature measurements on CMM. Optics and Lasers in Engineering, 49(11), 1274-1280.
• [28] Iuliano, L., Minetola, P., & Salmi, A. (2010). Proposal of an innovative benchmark for comparison of the performance of contactless digitizers. Measurement Science and Technology, 21(10), 1-13.
• [29] Feng, H.-Y., Liu, Y., & Xi, F. (2001). Analysis of digitizing errors of a laser scanning system. Precision Engineering Journal of the International Societies for Precision Engineering and Nanotechnology, 25(3), 185-191.
• [30] Zhang,Y., Zhong, D., Wu, B., Guan, T., Yue, P., & Wu, H. (2018). 3D Parametric Modeling of Complex Geological Structures for Geotechnical Engineering of Dam Foundation Based on T-Splines. Computer-Aided Civil and Infrastructure Engineering, 33(7), 545-570.
• [31] Borrmann, A., Kolbe, T.H., Donaubauer, A., Steuer, H., Jubierre, J.R. (2015). Multi‐Scale Geometric ‐ Semantic Modeling of Shield Tunnels for GIS and BIM Applications. Computer-Aided Civil and Infrastructure Engineering, 30(4), 263-281.
• [32] Buonamici, F., Carfagni, M., Furferi, R., Governi, L., Lapini, A., Volpe, Y. (2018). Reverse Engineering of Mechanical Parts: A template-based Approach. Journal of Computational Design and Engineering, 5(2), 145-159.