Localized electrodeposition (LED) was explored as an additive manufacturing technique with high control over process parameters and output geometry. The effect of variation o f process parameters and changing boundary conditions during the deposition process on the output geometry was observed through simulation and experimentation. Trends were found between specific process parameters and output geometries in the simula tions; trends varied between linear and nonlinear, and certain process parameters such as voltage and interelectrode gap were found to have a greater influence on the output than others. The simulations were able to predict the output width of deposition of experiments in an error of 8-30%. The information gained from this research allows for greater understanding of LED output, so that it can potentially be applied as an additive manufacturing technique of complex three-dimensional (3D) parts on the microscale.

Cloud-based computing holds enormous potential for collaboration, cost-saving, streamlining, and versatility of manufacturing. Additive manufacturing, being a computer-based system that can save point-by-point data of parts to be manufactured, can be easily integrated into the cloud. Preliminary work was done to test the cloud-based application of an in-house micro metal additive manufacturing by electrochemical deposition process. The system was linked to commercial cloud and email access for constant real-time communication from any user with a phone, tablet, or personal computer. The process could be started, stopped, altered, and queried remotely via the cloud. Input parameters (ie: geometry, tool size) and preliminary design rules (ie: current feedback threshold value) were specified. Plots of output performance, time, and current information were communicated back to the user ondemand, as well as stored on the cloud long-term. The cloud could then link input parameters to the history of system performance on such input parameters in a cloud-stored database. An experiment was set up to optimize horizontal deposition parameters based on deposition resolution, and save these values into the cloud for future use, The experiment was successfully executed and demonstrates the advantage of long-term storage, knowledge sharing, and convenience that the cloud offers for the manufacturing realm.

No abstract

No abstract

No abstract