Compensation for Geometrical Deviations in Additive Manufacturing

The design of additive manufacturing processes, especially for batch production in industrial practice, is of high importance for the propagation of new additive manufacturing technology. Manual redesign procedures of the additive manufactured parts based on discrete measurement data or numerical meshes are error prone and hardly automatable. To achieve the required final accuracy of the parts, often, various iterations are necessary. To address these issues, a data-driven geometrical compensation approach is proposed that adapts concepts from forming technology. The measurement information of a first calibration cycle of manufactured parts is the basis of the approach. Through non-rigid transformations of the part geometry, a new shape for the subsequent additive manufacturing process was derived in a systematic way. Based on a purely geometrical approach, the systematic portion of part deviations can be compensated. The proposed concept is presented first and was applied to a sample fin-shaped part. The deviation data of three manufacturing cycles was utilised for validation and verification.

[1]  Lihua Zhao,et al.  Influence of process parameters on part shrinkage in SLS , 2007 .

[2]  Maarten Moesen,et al.  Robust beam compensation for laser-based additive manufacturing , 2011, Comput. Aided Des..

[3]  Kevin McAlea,et al.  Improvements in SLS Part Accuracy , 1995 .

[4]  Konda Gokuldoss Prashanth,et al.  Additive Manufacturing: Reproducibility of Metallic Parts , 2017 .

[5]  Georges M. Fadel,et al.  Efficient slicing for layered manufacturing , 1998 .

[6]  Duc Truong Pham,et al.  Selective laser sintering: Applications and technological capabilities , 1999 .

[7]  A. Gebhardt Additive Fertigungsverfahren: Additive Manufacturing und 3D-Drucken für Prototyping – Tooling – Produktion , 2013 .

[8]  Jean-Loup Chenot,et al.  OPTIMAL DESIGN FOR NON‐STEADY‐STATE METAL FORMING PROCESSES—I. SHAPE OPTIMIZATION METHOD , 1996 .

[9]  Han Tong Loh,et al.  An approach to minimize build errors in direct metal laser sintering , 2006, IEEE Transactions on Automation Science and Engineering.

[10]  Michael F. Zaeh,et al.  Pre-compensation of Warpage for Additive Manufacturing , 2016 .

[11]  Michael F. Zaeh,et al.  Improving cost effectiveness in additive manufacturing – Increasing dimensional accuracy in laser beam melting by means of a simulationsupported process chain , 2015 .

[12]  Anas Yaghi,et al.  Distortion prediction and compensation in selective laser melting , 2017 .

[13]  Seok-Hee Lee,et al.  A study on shrinkage compensation of the SLS process by using the Taguchi method , 2002 .

[14]  Christian Markus Seidel Finite-Elemente-Simulation des Aufbauprozesses beim Laserstrahlschmelzen , 2016 .

[15]  Joshua M. Pearce,et al.  Distributed Manufacturing of Flexible Products: Technical Feasibility and Economic Viability , 2017, Technologies.

[16]  Jean-Pierre Kruth,et al.  Direct Selective Laser Sintering of Hard Metal Powders: Experimental Study and Simulation , 2002 .

[17]  Rémy Glardon,et al.  Finite element and neural network models for process optimization in selective laser sintering , 2004 .

[18]  Tirthankar Dasgupta,et al.  Optimal offline compensation of shape shrinkage for three-dimensional printing processes , 2015 .

[19]  Weiyin Ma,et al.  NURBS-based adaptive slicing for efficient rapid prototyping , 2004, Comput. Aided Des..

[20]  Amar M. Kamat,et al.  An analytical method to predict and compensate for residual stress-induced deformation in overhanging regions of internal channels fabricated using powder bed fusion , 2019, Additive Manufacturing.

[21]  Paolo Maggiore,et al.  Special Issue on “Additive Manufacturing Technologies and Applications” , 2017 .

[22]  M. Eder,et al.  Process-integrated Compensation of Geometrical Deviations for Bulk Forming , 2017 .

[23]  Nabil Anwer,et al.  Geometric deviation modeling with Statistical Shape Analysis in Design for Additive Manufacturing , 2019 .

[24]  Qiang Huang,et al.  An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deformation in Additive Manufacturing , 2016 .

[25]  Ian Gibson,et al.  On the role of different annealing heat treatments on mechanical properties and microstructure of selective laser melted and conventional wrought Ti-6Al-4V , 2017 .

[26]  J. Ponthot,et al.  A cascade optimization methodology for automatic parameter identification and shape/process optimization in metal forming simulation , 2006 .

[27]  K. Manetsberger,et al.  Compensation of Non-Linear Shrinkage of Polymer Materials in Selective Laser Sintering , 2001 .

[28]  Nabil Anwer,et al.  Deviation Modeling and Shape Transformation in Design for Additive Manufacturing , 2017 .

[29]  Syed H. Masood,et al.  A generic algorithm for a best part orientation system for complex parts in rapid prototyping , 2003 .

[30]  Nabil Anwer,et al.  Review of Shape Deviation Modeling for Additive Manufacturing , 2017 .