Accommodating Casting and Fixturing Errors by Adjusting the Machining Coordinate Frame

In order to save valuable machining time expended on machining bad casting, a point-cloud based analysis is proposed to perform a pre-process check on raw casting material conditions. This analysis virtually compares the point cloud data of the raw casting with the nominal CAD model of the final casting and analyzes if the dimensional tolerances on the finished casting can be satisfied by adjusting the coordinate frame in which the casting is machined. The proposed analysis includes the segmentation of the raw point cloud-data followed by extracting the functional features. The material conditions of all planar surfaces are expressed using linear algebraic inequalities. A linear programming-based methodology is developed that helps in aligning the raw casting to the nominal CAD frame so that the conformity is guaranteed. The proposed methodology with the help of slack variables can deal with the casting with unsatisfiable material conditions. An example problem dealing with machining of raw casting clamped on a 4-axis machine-tool is presented to check the validity of proposed method. The virtual gage analysis accurately suggests a solution to compensate part variation caused by fixturing and locating error.

Measurement System Analyses – Gauge Repeatability and Reproducibility Methods

The submitted article focuses on a detailed explanation of the average and range method (Automotive Industry Action Group, Measurement System Analysis approach) and of the honest Gauge Repeatability and Reproducibility method (Evaluating the Measurement Process approach). The measured data (thickness of plastic parts) were evaluated by both methods and their results were compared on the basis of numerical evaluation. Both methods were additionally compared and their advantages and disadvantages were discussed. One difference between both methods is the calculation of variation components. The AIAG method calculates the variation components based on standard deviation (then a sum of variation components does not give 100 %) and the honest GRR study calculates the variation components based on variance, where the sum of all variation components (part to part variation, EV & AV) gives the total variation of 100 %. Acceptance of both methods among the professional society, future use, and acceptance by manufacturing industry were also discussed. Nowadays, the AIAG is the leading method in the industry.

Human behaviour capturing in manual tungsten inert gas welding for intelligent automation

Tungsten inert gas welding is extensively used in aerospace applications due to its unique ability to produce higher quality welds compared to other conventional arc welding processes. However, most tungsten inert gas welding is performed manually, and it has not achieved the required level of automation. This is mostly attributed to the lack of process knowledge and adaptability to complexities, such as mismatches due to part fit-up and thermal deformations associated with the tungsten inert gas welding process. This article presents a novel study on quantifying manual tungsten inert gas welding, which will ultimately help intelligent automation of tungsten inert gas welding. Through tungsten inert gas welding experimentation, the study identifies the key process variables, critical tasks and strategies adapted by manual welders. Controllability of welding process parameters and human actions in challenging welding situations were studied both qualitatively and quantitatively. Results show that welders with better process awareness can successfully adapt to variations in the geometry and the tungsten inert gas welding process variables. Critical decisions taken to achieve such adaptations are mostly based on visual observation of the weld pool. Results also reveal that skilled welders prioritise a small number of process parameters to simplify the dynamic nature of tungsten inert gas welding process so that part variation can be accommodated.

Simulating Assembly Geometric and Stress Variation Considering Machining-Induced Residual Stress

Nowadays, large monolithic machined parts are increasingly used in aerospace industry. Machining-induced residual stress usually dominates thin part’s distortion. The resultant part input with mutual corresponding distortion pattern and inner residual stress has an impact on subsequent assembly geometric and stress variation of thin-walled structure. Final pre-stressed assembly state further affects product functionality. This paper illustrates a simulated methodology combining automatic scripting technique with a FEA tool to reveal the assembly variation’s distribution and pattern caused by the fluctuation of machining-induced residual stress. A typical assembly unit case in aircraft is investigated by applying the proposed methodology, more specifically, considering the uncertainty of the dominant shear stress via simulating the normal distributed weights of basis functions. Moreover, the quantitative effect of introducing residual stress is explored to examine the applicability of traditional Method of Influence Coefficients, which constructs a sensitivity matrix of the linear relation between incoming part variation and output assembly variation using FEA. This study enhances the understanding of the effect of introducing residual stress into assembly and helps the statistical inference about both geometric and stress aspects.

Tolerance Plugin Module in Integrated Design

Increased use of recycled material in high-end structural components based on wrought alloys is the goal of the EC project Suplight. The project proposes a framework for multi objective optimization. In this paper, a tolerance plugin module used in this project is described. The tolerance plugin module aims at controlling if the geometrical variation requirements on part level are fulfilled. The variation in the part stems from variation in material and process parameters and the relationship between variation in process and material parameters is estimated using designed computer experiments. Moreover, the tolerance plugin module offers an automatically generated meta-model, based on principal component analysis, for handling part variation that allows for faster Monte Carlo simulations and a format that can be used in variation simulations in succeeding assembly steps. The functionality is illustrated using two cases studies; one for investigation of geometrical part variation due to a stamping process and one for investigation of geometrical part variation due to a sheet metal forming process.