Skip to Main Content
 

Global Search Box

 
 
 
 

Files

ETD Abstract Container

Abstract Header

Hierarchical Data Structures for Optimization of Additive Manufacturing Processes

Panhalkar, Neeraj

Abstract Details

2015, PhD, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Additive Manufacturing (AM) is a Layered Manufacturing (LM) process where part is fabricated using materials such as metallic powder, plastics etc. with a layer by layer approach. Direct Metal Laser Sintering (DMLS) is capable of fabricating parts from a wide variety of metals and metallic alloys such as titanium, aluminum, steel, cobalt-chromium, etc. However, the major drawbacks associated with the DMLS process are poor part quality due to inherent staircase effect and thermal distortion along with material shrinkage and associated large build time. This dissertation seeks to reduce or eliminate these disadvantages of the AM process by developing an adaptive slicing approach that will also satisfy the Geometric Dimensioning and Tolerances (GD&T) callouts on the part. A virtual manufacturing model is developed for simulating the effect of input process parameters such as slice thickness, part orientation on the part quality manufactured using DMLS process. Even though in most of the research approaches, STL is used as an input file, an effort is made to include JT file format for virtually building parts using AM processes. A k-d tree based adaptive slicing approach has been developed and verified by comparing it with the uniform slicing approach. As existing AM hardware is not capable of handling adaptive slicing as an input parameter, a clustered slicing approach has been developed and presented in the dissertation. A new approach is developed that will calculate the adaptive slices based on the GD&T callouts information available on the part blueprint. To overcome the effect of material shrinkage and thermal deformations error to some extent, it is proposed to modify the STL/CAD file before feeding it to the machine so as to minimize tolerance errors on the design part. Another area explored in this dissertation is Additive Manufacturing (AM) based Printed Electronics (PE). It is an emerging technique where electronic components and interconnects are printed directly on substrates using a layered technique. The direct printing of the electronic components allows large scale and ultra-thin components to be printed on a wide variety of substrates including glass, silicon and plastic. Currently this technology is a labor intensive and manual process with the machine operator using his experience and judgment to slice the CAD file of the part to create 2D layers at different levels. A new file format based on the Constructive Solid Geometry (CSG) technique is proposed in this research. This file format data will not only include CAD data in the form of CSG primitives and Boolean representation but will also include manufacturing information such as toolpath, component material, deposition pattern etc. Finally, a component shape library is proposed for PE components such as resistors, capacitors etc. in AM based PE. Multiple shapes have been proposed for electronic components that can be used in place of conventional component designs having same electrical functionality but occupy less space on the substrate. A toolpath optimization approach for PE has been proposed to minimize the overall time consumed for material deposition.
Sundararaman Anand, Ph.D. (Committee Chair)
Mohsen Rezayat, Ph.D. (Committee Member)
Thomas Richard Huston, Ph.D. (Committee Member)
Sundaram Murali Meenakshi, Ph.D. (Committee Member)
David Thompson, Ph.D. (Committee Member)
130 p.

Recommended Citations

Citations

  • Panhalkar, N. (2015). Hierarchical Data Structures for Optimization of Additive Manufacturing Processes [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310812

    APA Style (7th edition)

  • Panhalkar, Neeraj. Hierarchical Data Structures for Optimization of Additive Manufacturing Processes. 2015. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310812.

    MLA Style (8th edition)

  • Panhalkar, Neeraj. "Hierarchical Data Structures for Optimization of Additive Manufacturing Processes." Doctoral dissertation, University of Cincinnati, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310812

    Chicago Manual of Style (17th edition)