The SLS-Generated Soft Robotic Hand - An Integrated Approach Using Additive Manufacturing and Reinforcement Learning

Author(s):  
Arne Rost ◽  
Stephan Schadle
2011 ◽  
Vol 2011 (1) ◽  
pp. 001021-001027 ◽  
Author(s):  
Cassie Gutierrez ◽  
Rudy Salas ◽  
Gustavo Hernandez ◽  
Dan Muse ◽  
Richard Olivas ◽  
...  

Fabricating entire systems with both electrical and mechanical content through on-demand 3D printing is the future for high value manufacturing. In this new paradigm, conformal and complex shapes with a diversity of materials in spatial gradients can be built layer-by-layer using hybrid Additive Manufacturing (AM). A design can be conceived in Computer Aided Design (CAD) and printed on-demand. This new integrated approach enables the fabrication of sophisticated electronics in mechanical structures by avoiding the restrictions of traditional fabrication techniques, which result in stiff, two dimensional printed circuit boards (PCB) fabricated using many disparate and wasteful processes. The integration of Additive Manufacturing (AM) combined with Direct Print (DP) micro-dispensing and robotic pick-and-place for component placement can 1) provide the capability to print-on-demand fabrication, 2) enable the use of micron-resolution cavities for press fitting electronic components and 3) integrate conductive traces for electrical interconnect between components. The fabrication freedom introduced by AM techniques such as stereolithography (SL), ultrasonic consolidation (UC), and fused deposition modeling (FDM) have only recently been explored in the context of electronics integration and 3D packaging. This paper describes a process that provides a novel approach for the fabrication of stiff conformal structures with integrated electronics and describes a prototype demonstration: a volumetrically-efficient sensor and microcontroller subsystem scheduled to launch in a CubeSat designed with the CubeFlow methodology.


Author(s):  
Edwin Valarezo Añazco ◽  
Patricio Rivera Lopez ◽  
Nahyeon Park ◽  
Jiheon Oh ◽  
Gahyeon Ryu ◽  
...  

Author(s):  
Deepankar Pal ◽  
Nachiket Patil ◽  
Kai Zeng ◽  
Brent Stucker

The complexity of local and dynamic thermal transformations in additive manufacturing (AM) processes makes it difficult to track in situ thermomechanical changes at different length scales within a part using experimental process monitoring equipment. In addition, in situ process monitoring is limited to providing information only at the exposed surface of a layer being built. As a result, an understanding of the bulk microstructural transformations and the resulting behavior of a part requires rigorous postprocess microscopy and mechanical testing. In order to circumvent the limited feedback obtained from in situ experiments and to better understand material response, a novel 3D dislocation density based thermomechanical finite element framework has been developed. This framework solves for the in situ response much faster than currently used state-of-the-art modeling software since it has been specifically designed for AM platforms. This modeling infrastructure can predict the anisotropic performance of AM-produced components before they are built, can serve as a method to enable in situ closed-loop process control and as a method to predict residual stress and distortion in parts and thus enable support structure optimization. This manuscript provides an overview of these software modules which together form a robust and reliable AM software suite to address future needs for machine development, material development, and geometric optimization.


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