Aircraft components are commonly produced with 7000 series aluminum alloys (AA) due to its weight, strength, and fatigue properties. Auto Industry is also choosing more and more aluminum component for weight reduction. Current additive manufacturing (AM) methods fall short of successfully producing 7000 series AA due to the reflective nature of the material along with elements with low vaporization temperature. Moreover, lacking in ideal thermal control, print inherently defective products with such issues as poor surface finish alloying element loss and porosity. All these defects contribute to reduction of mechanical strength. By monitoring plasma with spectroscopic sensors, multiple information such as line intensity, standard deviation, plasma temperature or electron density, and by using different signal processing algorithm, AM defects have been detected and classified. For composition analysis, the ratio of the maximum intensities of Mg(I)/Al(I) shows a strong trend with the amount of Zn and Mg in the powder, and the results are extremely promising regarding the ability to use the online spectra for real time determination of the composition of the AA7075 powders with high accuracy. A test matrix based on DOE was built and response surface analysis was performed to get a regression formulae. The formula was utilized to control porosity during the process. Minimizing porosity level, process parameters can be further optimized and verified with the regression formulae. Having optimized process parameters, a preliminary design of in-process control system is followed, incorporating spectral signal data, such as Mg(II)/Mg(I), peak-line intensity ratio of Mg/Al, and so on.