An experimental study of factors affecting the selective inhibition of sintering process

Selective Inhibition of Sintering (SIS) is a new rapid prototyping method that builds parts in a layer-by-layer fabrication basis. SIS works by joining powder particles through sintering in the part's body, and by sintering inhibition of some selected powder areas.; The objective of this research has been to improve the new SIS process, which has been invented at USC. The process improvement is based on statistical design of experiments. To conduct the needed experiments a working machine and related path generator software were needed. The machine and its control software were made available prior to this research. The path generator algorithms and software had to be created. This program should obtain model geometry data from a CAD file and generate an appropriate path file for the printer nozzle. Also, the program should generate a simulation file for path file inspection using virtual prototyping. The activities related to path generator constitute the first part of this research, which has resulted in an efficient path generator.; In addition, to reach an acceptable level of accuracy, strength, and surface quality in the fabricated parts, all effective factors in the SIS process should be identified and controlled. Simultaneous analytical and experimental studies were conducted to recognize effective factors and to control the SIS process. Also, it was known that polystyrene was the most appropriate polymer powder and saturated potassium iodide was the most effective inhibitor among the available candidate materials. In addition, statistical tools were applied to improve the desirable properties of the parts fabricated by the SIS process. An investigation of part strength was conducted using the Response Surface Methodology (RSM) and a region of acceptable operating conditions for the part strength was found. Then, through analysis of the experimental results, the impact of the factors on the final part surface quality and dimensional accuracy was modeled. After developing a desirability function model, process operating conditions for maximum desirability were identified. Finally, the desirability model was validated.