Path planning method based on discontinuous grid partition algorithm of point cloud for in situ printing

Purpose Path planning is an important part of three-dimensional (3D) printing data processing technology. This study aims to propose a new path planning method based on a discontinuous grid partition algorithm of point cloud for in situ printing. Design/methodology/approach Three types of parameters (i.e. structural, process and path interruption parameters) were designed to establish the algorithm model with the path error and the computation amount as the dependent variables. The path error (i.e. boundary error and internal error) was further studied and the influence of each parameter on the path point density was analyzed quantitatively. The feasibility of this method was verified by skin in situ printing experiments. Findings Path point density was positively correlated with Grid_size and negatively related to other parameters. Point_space, Sparsity and Line_space had greater influence on path point density than Indentation and Grid_size. In skin in situ printing experiment, two layers of orthogonal printing path were generated, and the material was printed accurately in the defect, which proved the feasibility of this method. Originality/value This study proposed a new path planning method that converted 3D point cloud data to a printing path directly, providing a new path planning solution for in situ printing. The discontinuous grid partition algorithm achieved controllability of the path planning accuracy and computation amount that could be applied to different processes.

[1]  Yusheng Shi,et al.  RESEARCH AND IMPLEMENT OF A NEW KIND OF SCANNING MODE FOR SELECTIVE LASER SINTERING , 2002 .

[2]  P. Wright,et al.  Anisotropic material properties of fused deposition modeling ABS , 2002 .

[3]  N. Venkata Reddy,et al.  Slicing procedures in layered manufacturing: a review , 2003 .

[4]  Yongnian Yan,et al.  Biomaterial forming research using RP technology , 2003 .

[5]  Radovan Kovacevic,et al.  Automated torch path planning using polygon subdivision for solid freeform fabrication based on welding , 2004 .

[6]  Lun Li,et al.  Research on a New Kind of Adaptive Parallel Scan Method in Laser Metal Deposition Shaping , 2008, 2008 International Conference on Computer Science and Software Engineering.

[7]  Dorothea Wagner,et al.  Algorithmics of Large and Complex Networks - Design, Analysis, and Simulation [DFG priority program 1126] , 2009, Algorithmics of Large and Complex Networks.

[8]  Hod Lipson,et al.  Additive manufacturing for in situ repair of osteochondral defects , 2010, Biofabrication.

[9]  Anthony Atala,et al.  In situ bioprinting of the skin for burns , 2010 .

[10]  Fabien Guillemot,et al.  In vivo bioprinting for computer- and robotic-assisted medical intervention: preliminary study in mice , 2010, Biofabrication.

[11]  Yu Ta,et al.  Summary of Path Planning Algorithm and its Application , 2011 .

[12]  D. D’Lima,et al.  Direct human cartilage repair using three-dimensional bioprinting technology. , 2012, Tissue engineering. Part A.

[13]  Shaojie Tang,et al.  Alignment, segmentation and 3-D reconstruction of serial sections based on automated algorithm , 2012 .

[14]  James J. Yoo,et al.  Bioprinted Amniotic Fluid‐Derived Stem Cells Accelerate Healing of Large Skin Wounds , 2012, Stem cells translational medicine.

[15]  Garry E Gold,et al.  Human Cartilage Repair with a Photoreactive Adhesive-Hydrogel Composite , 2013, Science Translational Medicine.

[16]  W. D. Li,et al.  An adaptive process planning approach of rapid prototyping and manufacturing , 2013 .

[17]  Zhongmin Jin,et al.  [Research status and future of in situ three-dimensional printing technique]. , 2014, Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery.

[18]  Ibrahim T. Ozbolat,et al.  Bioprinting scale-up tissue and organ constructs for transplantation. , 2015, Trends in biotechnology.

[19]  Jun Dong,et al.  Multi-frequency color-marked fringe projection profilometry for fast 3D shape measurement of complex objects. , 2015, Optics express.

[20]  Qin Lian,et al.  The application of multi-frequency fringe projection profilometry on the measurement of biological tissues. , 2015, Bio-medical materials and engineering.

[21]  Quan Wang,et al.  An Adaptive Slicing Thickness Adjustment Method Based on Cloud Point in 3D Printing , 2016, 2016 13th International Conference on Embedded Software and Systems (ICESS).

[22]  Jun Dong,et al.  Multi-frequency fringe projection profilometry based on wavelet transform. , 2016, Optics express.

[23]  Wei Sun,et al.  Evaluating fabrication feasibility and biomedical application potential of in situ 3D printing technology , 2016 .

[24]  Ibrahim T. Ozbolat,et al.  Current advances and future perspectives in extrusion-based bioprinting. , 2016, Biomaterials.

[25]  Fabien Guillemot,et al.  In situ printing of mesenchymal stromal cells, by laser-assisted bioprinting, for in vivo bone regeneration applications , 2017, Scientific Reports.

[26]  Xiao Li,et al.  Development of a Robotic Arm Based Hydrogel Additive Manufacturing System for In-Situ Printing , 2017 .

[27]  S. Soker,et al.  A tunable hydrogel system for long-term release of cell-secreted cytokines and bioprinted in situ wound cell delivery. , 2017, Journal of biomedical materials research. Part B, Applied biomaterials.

[28]  Wei Mao,et al.  3D Printing Process for Hydro Gel with the Three-dimensional Micro Tubes to Mimic Vascular Network , 2017 .

[29]  Gershon Elber,et al.  Volumetric covering print-paths for additive manufacturing of 3D models , 2018, Comput. Aided Des..

[30]  Nhayoung Hong,et al.  3D bioprinting and its in vivo applications. , 2018, Journal of biomedical materials research. Part B, Applied biomaterials.

[31]  Peter Pivonka,et al.  In-situ handheld 3D Bioprinting for cartilage regeneration , 2018 .