Predictive process control (PPC) is the use of predictive, physical models as the basis for process control [1]. In contrast, conventional control algorithms utilize statistical models that are derived from repetitive process trials. PPC employs in-process monitoring and control of manufacturing processes. PPC algorithms are very promising approaches for welding of small lots or customized products with rapid changes in materials, geometry, or processing conditions. They may also be valuable for welding high value products for which repeated trials and waste are not acceptable. In this research, small-lot braze-welding of UNS C22000 commercial bronze with gas metal arc welding (GMAW) technology is selected as a representative application of PPC. Thermal models of the welding process are constructed to predict the effects of changes in process parameters on the response of temperature measurements. Because accurate thermal models are too computationally expensive for direct use in a control algorithm, metamodels are constructed to drastically reduce computational expense while retaining a high degree of accuracy. Then, the feasibility of PPC of welding applications is analyzed with regard to uncertainties and time delays in an existing welding station and thermal metamodels of the welding process. Lastly, a qualitative residual stress model is developed to nondestructively assess weld quality in end-user parts. / text