Physics-Based Part Orientation and Sentencing: A Solution to Manufacturing Variability

Casting deviations introduce geometric variability that impacts the aerodynamic performance of turbomachinery. These effects are studied for a high-pressure turbine rotor blade from a modern aero-engine. A sample of 197 blades were measured using structured-light three-dimensional scanning, and the performance of each blade is quantified using Reynolds-averaged Navier–Stokes (RANS) simulations. Casting variation is typically managed by applying geometric tolerances to determine the suitability of a component for service. The analysis demonstrates that this approach may not be optimal since it does not necessarily align with performance, in particular the capacity and efficiency. Alternatively, functional acceptance based on the predicted performance of each blade removes the uncertainty associated with geometric tolerancing and gives better performance control. Building on these findings, the paper proposes a method to set the orientation of the fir-tree, which is machined after casting. By customizing the alignment of each blade, performance variability and scrap rates can be significantly reduced. The method uses predictions of performance to reorient the castings to compensate for manufacturing-induced errors, without changing the design-intent blade geometry and with minimal changes to the manufacturing facility.

Physics-Based Part Orientation and Sentencing: A Solution to Manufacturing Variability

Casting deviations introduce geometric variability that impacts the aerodynamic performance of turbomachinery. These effects are studied for a High Pressure Turbine (HPT) rotor blade from a modern aero-engine. 197 blades were measured using three-dimensional structured-light scanning (GOM scanning), and the performance of each blade is quantified using Reynolds-Averaged Navier-Stokes (RANS) simulations. Casting variation is typically managed by applying geometric tolerances to determine the suitability of a component for service. The analysis demonstrates that this approach may not be optimal since it does not necessarily align with performance, in particular the capacity and efficiency. Alternatively, functional acceptance based on the predicted performance of each blade removes the uncertainty associated with geometric tolerancing and gives better performance control. Building on these findings, the paper proposes a method to set the orientation of the fir-tree, which is machined after casting. By customizing the alignment of each blade, performance variability and scrap rates can be significantly reduced. The method uses predictions of performance to reorient the castings to compensate for the manufacturing-induced errors, without changing the design-intent blade geometry and with minimal changes to the manufacturing facility.

Automatic Detection of Directions of Dimensional Control in Mechanical Parts

This research is part of a larger project which aims at developing a tool to help designers create effective GD&T schemas. The first step towards this goal is to determine the particular directions in which dimensions and tolerances need to be controlled. These directions we label here as “Directions of (Dimensional) Control” or DoC for short. Regardless of whether one uses chain dimensioning, reference dimensioning or geometric tolerancing, all size and basic dimensions of position line up in a finite number of directions or Directions of Control. This paper presents an approach for automatically identifying the directions of control from CAD models of mechanical parts. The only input to the system is the geometry of parts or assemblies in STEP file format. The analysis is done part by part for an assembly. First, planar and cylindrical features are recognized and their normal/axes extracted. The extracted features are then organized into groups of parallel normal or axes directions. Cylindrical features can belong to two or more Directions of Control, while planar features belong can only belong to one. Features in each DoC are then ordered based on perpendicular relative distances. Each ordered feature list forms a linear chain in which nodes represent features and links are attributed with relative distance to the nearest neighbors on each side. DoC chains are related to each other by relative orientation. Therefore, the chains are combined into a unified graph, using the junction nodes to contain the relative orientation between the chains. The extracted Directions of Control can be output in both textual and graphical form. Although the primary motivation for automatic DoC graph generation is computer assisted tolerancing and automatic tolerance analysis, the paper also discusses other applications in manufacturing.

Facilitating Higher-Order Learning Through Computer Games

Engineering education needs to focus on equipping students with foundational math, science, and engineering skills, with development of critical and higher-order thinking so they can address novel and complex problems and challenges. Learning through a medium that combines course materials with game characteristics can be a powerful tool for engineering education. Games need to be designed for higher order engagement with students, which go beyond remembering, understanding and applying of engineering concepts. In this paper, we present design, development, implementation, and evaluation of a game for engineers. The developed game is founded on experiential learning theory and uses enhanced game characteristics. The racecar game has been designed to facilitate higher-order learning of geometric tolerancing concepts. The course module has been developed and implemented, with assessment of outcomes. The results show that students using the game module, when compared with the control group (lecture-based instruction), had significant improvements when addressing questions that involved higher-order cognition. Survey results also indicate positive student attitudes towards the learning experience with game modules.