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An error identification and compensation method for Cartesian 3D printer based on specially designed test artifact

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Abstract

The printing accuracy is one of the most important metrics to evaluate the additive manufacturing (AM) machine. In this paper, an error identification and compensation method for Cartesian 3D printer is presented based on a specially designed test artifact to improve printing accuracy. The relationship between the geometric errors of the printed object and the kinematic errors of the printer axes is established based on the theory of the multi-body system. A series of formulas are derived to separate the kinematic errors of each axis from the geometric errors. To extract the geometric errors required for the mathematical calculations, an artifact with the special features is proposed and printed. The geometric errors of the characteristic points on the artifact are measured by a coordinate measuring machine (CMM). From the measured geometric errors, kinematic errors of the printer can be identified, and can be further compensated by adjusting the CAD model of the object. Two compensated algorithms are established; one uses the fitted curves of the kinematic errors, and the other uses the average kinematic error values. Printing tests and case studies are performed to verify the effectiveness of the proposed method. The results show that the proposed method can improve printing accuracy of the Cartesian 3D printer.

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Abbreviations

\({}_{b}{}^{a}{T}_{p}\) :

The initial position matrix

\({}_{b}{}^{a}{T}_{pe}\) :

The error matrix of the initial position

\({}_{b}{}^{a}{T}_{k}\) :

The dynamic position matrix

\({}_{b}{}^{a}{T}_{ke}\) :

The error matrix of dynamic position

\({}_{b}{}^{a}{T}_{i}\) :

The ideal position matrix

\({}_{b}{}^{a}{T}_{a}\) :

The actual position matrix

\({E}_{p}\) :

The geometric error of point P

\({E}_{px}\) :

The geometric error of point P in the x-direction

\({E}_{py}\) :

The geometric error of point P in the y-direction

\({E}_{pz}\) :

The geometric error of point P in the z-direction

\({\alpha }_{ab}\) :

The angle around the X-axis between body a and b

\({\beta }_{ab}\) :

The angle around the Y-axis between body a and b

\({\gamma }_{ab}\) :

The angle around the Z-axis between body a and b

\({i}_{ab}\) :

The linear offset between body a and b at the X-direction

\({j}_{ab}\) :

The linear offset between body a and b at the Y-direction

\({k}_{ab}\) :

The linear offset between body a and b at the Z-direction

\({\theta }_{x}\) :

The rotational angle around the X-axis

\({\theta }_{y}\) :

The rotational angle around the Y-axis

\({\theta }_{z}\) :

The rotational angle around the Z-axis

\({x}_{ab}\) :

The linear displacement in the X-axis between body a and b

\({y}_{ab}\) :

The linear displacement in the Y-axis between body a and b

\({z}_{ab}\) :

The linear displacement in the Z-axis between body a and b

\({\delta }_{X\left(x\right)}\) :

The servo error of the X-axis

\({\delta }_{Y\left(x\right)}\) :

The horizontal straightness error of the X-axis

\({\delta }_{Z\left(x\right)}\) :

The vertical straightness error of the X-axis

\({\varepsilon }_{\alpha \left(x\right)}\) :

The roll error of the X-axis

\({\varepsilon }_{\beta \left(x\right)}\) :

The pitch error of the X-axis

\({\varepsilon }_{\gamma \left(x\right)}\) :

The yaw error of the X-axis

\({\delta }_{X\left(y\right)}\) :

The horizontal straightness error of the Y-axis

\({\delta }_{Y\left(y\right)}\) :

The servo error of the Y-axis

\({\delta }_{Z\left(y\right)}\) :

The vertical straightness error of the Y-axis

\({\varepsilon }_{\alpha \left(y\right)}\) :

The roll error of the Y-axis

\({\varepsilon }_{\beta \left(y\right)}\) :

The pitch error of the Y-axis

\({\varepsilon }_{\gamma \left(y\right)}\) :

The yaw error of the Y-axis

\({\delta }_{X\left(z\right)}\) :

The horizontal straightness error of the Z-axis

\({\delta }_{Y\left(z\right)}\) :

The vertical straightness error of the Z-axis

\({\delta }_{Z\left(z\right)}\) :

The servo error of the Z-axis

\({\varepsilon }_{\alpha \left(z\right)}\) :

The roll error of the Z-axis

\({\varepsilon }_{\beta \left(z\right)}\) :

The pitch error of the Z-axis

\({\varepsilon }_{\gamma \left(z\right)}\) :

The yaw error of the Z-axis

\(\Delta {\gamma }_{xy}\) :

The squareness error between the X-axis and Y-axis

\(\Delta {\beta }_{xz}\) :

The squareness error between the X-axis and Z-axis

\(\Delta {\alpha }_{yz}\) :

The squareness error between the Y-axis and the Z-axis

ex1:

The geometric error in the X-direction of the original model

ey1:

The geometric error in the Y-direction of the original model

ez1:

The geometric error in the Z-direction of the original model

ex2:

The geometric error at the X-direction of the compensated model using fitted curves

ey2:

The geometric error at the Y-direction of the compensated model using fitted curves

ez2:

The geometric error at the Z-direction of the compensated model using fitted curves

ex3:

The geometric error at the X-direction of the compensated model using the average value of kinematic error

ey3:

The geometric error at the Y-direction of the compensated model using the average value of kinematic error

ez3:

The geometric error at the Z-direction of the compensated model using the average value of kinematic error

References

  1. Rasiya G, Shukla A, Saran K, Additive manufacturing—a review, Mater Today: Proc. https://doi.org/10.1016/j.matpr.2021.05.181

  2. Vicent AC, Tambuwala MM, Hassan SS, Barh D, Aljabali AA, Birkett M, Arjunan A, Serrano-Aroca A (2021) Fused deposition modelling: current status, methodology, applications and future prospects. Addit Manuf 47:102378. https://doi.org/10.1016/j.addma.2021.102378

    Article  Google Scholar 

  3. Minetola P, Calignano F, Galati M (2020) Comparing geometric tolerance capabilities of additive manufacturing systems for polymers. Addit Manuf 32:101103. https://doi.org/10.1016/j.addma.2020.101103

    Article  Google Scholar 

  4. Decker N, Wang Y, Huang Q (2020) Efficiently registering scan point clouds of 3D printed parts for shape accuracy assessment and modeling. J Manuf Syst 56:587–597. https://doi.org/10.1016/j.jmsy.2020.04.001

    Article  Google Scholar 

  5. Geng Z, Bidanda B (2021) Geometric precision analysis for Additive Manufacturing processes: a comparative study. Precis Eng 69:68–76. https://doi.org/10.1016/j.precisioneng.2020.12.022

    Article  Google Scholar 

  6. Rajan K, Samykano M, Kadirgama K, Harun W, Rahman MM (2022) Fused deposition modeling: process, materials, parameters, properties, and applications. Int J Adv Manuf Technol 120:1531–1570. https://doi.org/10.1007/s00170-022-08860-7

    Article  Google Scholar 

  7. Jafari D, Vaneker T, Gibson I (2021) Wire and arc additive manufacturing: opportunities and challenges to control the quality and accuracy of manufactured parts. Mater Des 202:109471. https://doi.org/10.1016/j.matdes.2021.109471

    Article  Google Scholar 

  8. Zhu Z, Anwer N, Mathieu L, (2018) Shape transformation perspective for geometric deviation modeling in additive manufacturing, 15th CIRP Conference on Computer Aided Tolerancing – CIRP CAT. https://doi.org/10.1016/j.procir.2018.04.038

  9. Pastrea M, Tagneb S, Anwer N (2020) Test artifacts for additive manufacturing: a design methodology review. CIRP J Manuf Sci Technol 31:14–24. https://doi.org/10.1016/j.cirpj.2020.09.008

    Article  Google Scholar 

  10. Moylan S, Slotwinski J, Cooke A, Jurrens K, Donmez M (2014) An additive manufacturing test artifact. J Res Nat Inst Stand Technol 119:429–459. https://doi.org/10.6028/jres.119.017

    Article  Google Scholar 

  11. Yang L, Anam A (2014) An investigation of standard test part design for additive manufacturing, Proceedings of the 25th Annual International Solid Freeform Fabrication Symposium, pp. 901–922. https://utw10945.utweb.utexas.edu/sites/default/files/2014-072-Yang.pdf, 2014/2022.01.22. Accessed 26 Jan 2021

  12. Perez M, Ramos J, Espalin D, Hossain M, Wicker R, (2013) Ranking model for 3D printers, Proceedings of the 2013 Solid Freeform Fabrication Symposium, University of Texas at Austin, TX, Austin, 1048–1065. https://utw10945.utweb.utexas.edu/Manuscripts/2013/2013-83-Perez.pdf, 2013/2022.01.22. Accessed 26 Jan 2021

  13. Lopes A, Perez M, Espalin D, Wicker R (2020) Comparison of ranking models to evaluate desktop 3D printing in a growing market. Addit Manuf 35:101291. https://doi.org/10.1016/j.addma.2020.101291

    Article  Google Scholar 

  14. Yap Y, Wang C, Sing S, Dikshit V, Yeong W, Wei J (2017) Material jetting additive manufacturing: an experimental study using designed metrological benchmarks. Precis Eng 50:275–285. https://doi.org/10.1016/j.precisioneng.2017.05.015

    Article  Google Scholar 

  15. Santos V, Thompson A, Waterhouse D, Maskery I, Woolliams P, Leach R (2020) Design and characterisation of an additive manufacturing benchmarking artifact following a design-for-metrology approach. Addit Manuf 32:100964. https://doi.org/10.1016/j.addma.2019.100964

    Article  Google Scholar 

  16. Toguem S, Souzani C, Nouira H, Anwer N (2020) Axiomatic design of customised additive manufacturing artifacts. Procedia CIRP 91:899–904. https://doi.org/10.1016/j.procir.2020.02.246

    Article  Google Scholar 

  17. Taylor H, Garibay E, Wicker R (2021) Toward a common laser powder bed fusion qualification test artifact. Addit Manuf 39:101803. https://doi.org/10.1016/j.addma.2020.101803

    Article  Google Scholar 

  18. Bracken J, Pomorski T, Armstrong C, Prabhu R, Simpson T, Jablokow K, Cleary W, Meisel N (2020) Design for metal powder bed fusion: the geometry for additive part selection (GAPS) worksheet. Additive Manufacturing 35:101163. https://doi.org/10.1016/j.addma.2020.101163

    Article  Google Scholar 

  19. Berez J, Praniewicz M, Saldana C (2021) Assessing laser powder bed fusion system geometric errors through artifact-based methods. Procedia Manufacturing 53:395–406. https://doi.org/10.1016/j.promfg.2021.06.042

    Article  Google Scholar 

  20. Veetil J, Khorasani M, Ghasemi A, Rolfe B, Vrooijink I, Beurden K, Moes S, Gibson I (2021) Build position-based dimensional deviations of laser powder-bed fusion of stainless steel 316L. Precis Eng 67:58–68. https://doi.org/10.1016/j.precisioneng.2020.09.024

    Article  Google Scholar 

  21. Rupal B, Ahmad R, Qureshi A (2018) Feature-based methodology for design of geometric benchmark test artifacts for additive manufacturing processes. Procedia CIRP 70:84–89. https://doi.org/10.1016/j.procir.2018.02.012

    Article  Google Scholar 

  22. Vorkapic N, Pjevic M, Popovic M, Slavkovic N, Zivanovic S (2020) An additive manufacturing benchmark artifact and deviation measurement method. J Mech Sci Technol 34(7):3015–3026. https://doi.org/10.1007/s12206-020-06

    Article  Google Scholar 

  23. Li Z, Yang J, Fan K, Zhang Y (2015) Integrated geometric and thermal error modeling and compensation for vertical machining centers. Int J Adv Manuf Technol 76:1139–1150. https://doi.org/10.1007/s00170-014-6336-z

    Article  Google Scholar 

  24. Ruan D, Mao J, Liu G, Ma L (2021) Synchronous motion error identification method of dual-five-axis CNC machine tool based on R-test. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-021-07665-4

  25. Bochmann L, Bayley C, Helu M, Transchel R, Wegener K, Dornfeld D (2015) Understanding error generation in fused deposition modeling. Surf Topogr Metrol Propert 3:014002. https://doi.org/10.1088/2051-672X/3/1/014002

    Article  Google Scholar 

  26. Cajal C, Santolaria J, Samper D, Velazquez J (2016) Efficient volumetric error compensation technique for additive manufacturing machines. Rapid Prototyp J 22:2–19. https://doi.org/10.1108/RPJ-05-2014-0061

    Article  Google Scholar 

  27. Lyu J, Manoochehri S (2019) Error modeling and compensation for FDM machines. Rapid Prototyp J 25:1565–1574. https://doi.org/10.1108/RPJ-04-2017-0068

    Article  Google Scholar 

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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Tianjian Li and Jungang Li. The first draft of the manuscript was written by Jungang Li and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tianjian Li.

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Li, T., Li, J., Ding, X. et al. An error identification and compensation method for Cartesian 3D printer based on specially designed test artifact. Int J Adv Manuf Technol 125, 4185–4199 (2023). https://doi.org/10.1007/s00170-023-10858-8

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