Johan Lööf

Optimizing Locator Position to Maximize Robustness in Critical Product Dimensions

The way parts are located in relation to each other or in fixtures is critical for how geometrical variation will propagate and cause variation in critical product dimensions. Therefore, more emphasis should be put on this activity in early design phases in order to avoid assembly and production problems later on. In earlier literature, locator positions have been defined using optimization to reach a robust locating scheme. This implies that the total robustness of a part is optimized by placing the locators in an optimal way. Sometimes there are areas on parts that are more sensitive to variation than others. Therefore, this paper suggests an approach for optimizing the positions of locators in a locating scheme to maximize robustness in defined critical dimensions. A formulation of an optimization problem is presented, and an algorithm solving this in a heuristic approach is developed. Finally, this algorithm is applied on an industrial example.

Discrete tolerance allocation for product families

This article extends earlier research on the discrete tolerance allocation problem in order to optimize an entire product family simultaneously. This methodology enables a top-down tolerancing approach where requirements at assembly level on products within a family are allocated to single part requirements. The proposed solution has been implemented as an interface with an optimization algorithm coupled with variation simulation software. The article also consists of an extensive review on the discrete tolerance allocation problem which motivates the extension of the discrete tolerance allocation problem. The suggested approach has been applied to a robot family consisting of two variants with the same requirements at product level.

Virtual Robustness Evaluation of Turbine Structure Assemblies Using 3D Scanner Data

In this paper, the functional robustness of a jet engine component is investigated. Located at the rear part of the engine, the Turbine Rear Structure (TRS) provides a support structure for the low-pressure shaft, while redirecting the exhaust flow from the low-pressure turbine to the exit nozzle. For larger engines, TRSs are fabricated assemblies consisting of cast, wrought and sheet metal parts. In a case study, virtual tools are used to examine how geometrical variation in cast parts of the TRS assembly affects performance. Variation data are obtained by scanning cast parts in a 3D laser scanner. The resulting data are fed into a CAD model as surface point parameters. The parts are then assembled virtually using CAT software. The assemblies are subsequently fed into a simulation platform where they are meshed, and CFD and FEM are used to evaluate the structural and aerodynamic effects of the variation. To quantitatively analyze the effects of variation, five cast parts with different geometrical variations are virtually assembled into 25 geometries and analyzed with respect to sixteen functional properties. Results show that geometric variation has a noticeable effect on performance. We believe this approach to be a useful tool in engine design. Being able to virtually examine the geometrical robustness of a design in early phases reduces the need for redesign loops. This leads not only to faster and less expensive product development, but also to better and more reliable engine designs.

Bridging the gap between point cloud and CAD: A method to assess form error in aero structures

One barrier to the successful implementation of probabilistic design methods is the lack of methods for characterizing form error. Form error, defined as the irregular deviations in geometry, is hard to describe in a virtual environment. This paper showcases a method that uses a simulation platform to assess the effects of form error on the aerodynamic, thermal and structural performance of an aero structure. Particularly, it looks at how bridging the gap between nominal CAD-geometries and point-cloud-based scanned geometries, creates a unified model where physical geometrical deviations can be isolated from model uncertainties. In a sample fatigue life problem, the effects of geometrically deviated parts is assessed. Further, a permutation genetic algorithm is implemented to optimize deviated part configuration. From a research standpoint, the showcased method contributes to addressing the genesis problem inherent in uncertainty quantification. From and industrial point of view, this method provides a precise, cost-effective tool for dealing with effects variations, which in turn increases both product quality and development process efficiency.

Top-Down Decomposition of Multi-Product Requirements onto Locator Tolerances

The tolerance allocation problem consists of choosing tolerances on dimensions of a complex assembly so that they combine into an ‘optimal state’ while fulfilling certain requirements on an allowed variation. This optimal state often coincides with the minimum manufacturing cost of the product. Sometimes it is balanced with an artificial cost that the deviation from target induces on the quality of the product. This paper suggests a multiobjective formulation of the tolerance allocation problem to automatically decompose requirements for an allowed variation on a set of critical product dimensions. This formulation is demonstrated using a rear lamp on a car with multiple requirements on allowed variation. In this case only the tolerances on locators that locates the lamp on the body are considered. The paper also reviews a selection of work that has been made on solving tolerance allocation problems.

Evaluating How Functional Performance in Aerospace Components Is Affected by Geometric Variation

Geometric variation stemming from manufacturing can be a limiting factor for the quality and reliability of products. Therefore, manufacturing assessments are increasingly being performed during the early stages of product development. In the aerospace industry, products are complex engineering systems, the development of which require multidisciplinary expertise. In such contexts, there are significant barriers against assessing the effects of geometric variation on the functionality of products. To overcome these barriers, this article introduces a new methodology consisting of a modelling approach linked to a multidisciplinary simulation environment. The modelling approach is based on the parametric point method, which allows point-scanned data to be transferred to parameterised CAD models. In a case study, the methodology is implemented in an industrial setting. The capability of the methodology is demonstrated through a few applications, in which the effects of geometric variation on the aerodynamic, thermal, and structural performance of a load-bearing turbofan component are analysed. The proposed methodology overcomes many of the current barriers, making it more feasible to assess the effects of geometric variation during the early design phases. Despite some limitations, the methodology contributes to an academic understanding of how to evaluate geometric variation in multidisciplinary simulations and provides a tool for industry.

Designing simulation platforms for uncertainty—An example from an aerospace supplier

Variation poses a serious threat to the functionality, safety and reliability of aircraft. As the aerospace industry depends ever more heavily on modeling and simulation in their product development, there is an increased need to assess the effects of variation in a virtual environment. This paper outlines the methods proposed by a Swedish aerospace supplier to incorporate robust design methodology into platform-based product development. These methods evaluate how geometric variation affects the aerodynamic, thermal and structural performance of turbofan engine components. The results of the study show that simulation results are heavily affected by variations in geometry. Moreover, this study showcases automated simulation platforms as a powerful tool for robustness analyses. In addition to optimizing the robustness of products, these tools are equally effective as a tool for allocating engineering resources to optimize quality-to-cost ratio.