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The new product development process (NPDP) is crucial for maintaining business up and running in manufacturing corporations worldwide. NPDP helps firms improve their profits, where stakeholder attention is mostly focused. NPDP Risk Assessment (NPDP-RA) is a vital activity to achieve a successful new product launch. However, managing different kinds of risks over the process presents enormous challenges; thus, risk assessment over NPDP still shows gaps, even when different tools are employed to identify, mitigate, and eliminate risks throughout the process. The identified gaps are mainly produced because the uncertainty added by the individuals assessing the risks, and the currently used tools are generally focused on a single phase of the NPDP, disregarding the stakeholders’ project objectives. This paper presents the main NPDP risks for stakeholders through the NPDP phases; likewise, the integration of Pythagorean fuzzy dimensional analysis – failure mode and effect analysis and value stream mapping (PFDA-FMEA-VSM). This novel integrated method is intended to improve the manner to perform the NPDP-RA for stakeholders. A practical example is presented to demonstrate the proposed integration of PFDA-FMEA-VSM for stakeholders at NPDP of electronic devices project.

In this article, particle image velocimetry studies were conducted in a low-speed wind tunnel to investigate the effects of blowing ratio and blade span in terms of the characteristics of the flow field around a film-cooled blade leading edge. The measurements were performed at 20%, 40%, 60%, and 80% of blade span and blowing ratios of M = 0.5, M = 0.75, M = 1, M = 1.5, and M = 2. Velocity, turbulence intensity, and structure of vortices during the interaction between cooling flow and mainstream were analyzed in detail. The analysis shows a significant increase in mainstream velocity at low blowing ratios, M

This paper discusses a research related with the innovative use of a decision support system based on data mining (to evaluate historical information) and the support vector machine method to determine the optimal values related with the cooling efficiency of a gas turbine blade and to determine the adequate selection of components to build scenarios under uncertainty. This research allows the selection of a specific number of optimal values for components, in a time horizon of a power energy installation (approximately four hours). These components are evaluated with data from an information repository of a successful energy system. The intent of this research is to apply the computational properties of an established model of intelligent optimization. The case study allowed the analysis of the individual features of each component with the emulation from set matching features (optimal values reached by our hybrid algorithm). This way it is possible to predict a better functionality in this kind of system.