Additive manufacturing is one of the most promising technologies, expected to pave the way we manufacture today by avoiding conventional manufacturing processes due to several advantages and capabilities such as reduction of material wastage. However, despite these advantages, there is still a lack of thorough understanding on how different defects can form and originate during additive manufacturing processing leading quite challenges in rapid qualification and certification of printed parts. Current manufacturing industries extensively rely on experimental methods to qualify printed part, requiring huge amount of resource and time to comprehensively evaluate parts quality. The aim of the research study was developing a computational framework used for prediction of residual stress and distortion of printed part that will be applied during manufacturing operation of powder bed fusion machines. Then find out optimum process parameters that will result with low response of defects by systematically investigating the influence of different process parameters and material property on the printed part final quality. To develop the framework a statistical evaluation method such as design of experiment and response surface methodology were used getting number of simulation runs including thermal modeling, residual and distortion modeling during AM operation. The printed part was Small Fragment Fixation Locking Plate, and Metastable-  titanium was chosen for the study due to current high demand for orthopaedic implant application. Multi-Attribute- Decision Making method was applied for selection of the right type of specific machine from list of different machines. The study also incorporate the statistical methods such as ANOVA to determine Pareto-chart which helped to find which process parameters and their combinations will have high effect on the response of the final printed part. Choice of path of the model was selected among computational and statistical methods considering specific application of the selected material by tradeoff between accuracy and computational expense. This resulted getting the most optimized pathway and was confirmed with low percentage error value of 2.3% and 1.2% of distortion and residual stress respectively. The specific machine EOS M290 is selected with highest performance of 0.60315. High amount of residual stress and distortion that developed during the process was then minimized to 20.378 MPa and 0.04498 mm by using optimum parameters such as laser power 100 W, travel speed 400 m/s and layer thickness 0.29 mm. The predictive model will help engineers and designers to discover the capability and quality of a particular component before embarking on manufacturing, allowing the designers to make corrections to the designs where it is necessary. Also results, for example residual stress is very important for determining other properties of printed parts such as fatigue life, corrosion resistance, distortion and dimensional stability without the use of expensive and time-consuming experimental techniques like X- ray Diffraction. Keywords: Additive Manufacturing; ANOVA; Bone Fragment Fixation Locking Plate; Orthopaedic Implant; Powder Bed Fusion.