Interactive Quality Inspection of Measured Deviations in Sheet Metal Assemblies

We present an exploratory data analysis approach for finite element (FE) simulations to interactively inspect measured deviations in sheet metals arising in automotive applications. Exterior car body parts consist of large visible surfaces, and strict tolerances must be met by them to satisfy both aesthetic requirements and quality performance requirements. To fulfill quality requirements like gap and flushness, exterior vehicle components have adjustable mechanical boundaries. These boundaries are used to influence the shape and position of a sheet metal part relative to its chassis. We introduce a method that supports an inspection engineer with an interactive framework that makes possible a detailed analysis of measured sheet metal deviation fields generated from 3D scans. An engineer can interactively change boundary conditions and obtains the resulting deviation field in real-time. Thus, it is possible to determine viable and desirable adjustments efficiently, leading to time and cost savings in the assembly process

Modelling geomagnetically induced currents in midlatitude Central Europe using a thin-sheet approach

Geomagnetically induced currents (GICs) in power systems, which can lead to transformer damage over the short and the long term, are a result of space weather events and geomagnetic variations. For a long time, only high-latitude areas were considered to be at risk from these currents, but recent studies show that considerable GICs also appear in midlatitude and equatorial countries. In this paper, we present initial results from a GIC model using a thin-sheet approach with detailed surface and subsurface conductivity models to compute the induced geoelectric field. The results are compared to measurements of direct currents in a transformer neutral and show very good agreement for short-period variations such as geomagnetic storms. Long-period signals such as quiet-day diurnal variations are not represented accurately, and we examine the cause of this misfit. The modelling of GICs from regionally varying geoelectric fields is discussed and shown to be an important factor contributing to overall model accuracy. We demonstrate that the Austrian power grid is susceptible to large GICs in the range of tens of amperes, particularly from strong geomagnetic variations in the east–west direction

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter

Objective. Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots. Approach. We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction. Main results. The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 107 protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm. Significance. Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.publishedVersio

Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks

Background: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality.  Material and methods: The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods.  Results: The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads. Conclusion: The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality

Investigating particle track topology for range telescopes in particle radiography using convolutional neural networks

Background: Proton computed tomography (pCT) and radiography (pRad) are proposed modalities for improved treatment plan accuracy and in situ treatment validation in proton therapy. The pCT system of the Bergen pCT collaboration is able to handle very high particle intensities by means of track reconstruction. However, incorrectly reconstructed and secondary tracks degrade the image quality. We have investigated whether a convolutional neural network (CNN)-based filter is able to improve the image quality.  Material and methods: The CNN was trained by simulation and reconstruction of tens of millions of proton and helium tracks. The CNN filter was then compared to simple energy loss threshold methods using the Area Under the Receiver Operating Characteristics curve (AUROC), and by comparing the image quality and Water Equivalent Path Length (WEPL) error of proton and helium radiographs filtered with the same methods.  Results: The CNN method led to a considerable improvement of the AUROC, from 74.3% to 97.5% with protons and from 94.2% to 99.5% with helium. The CNN filtering reduced the WEPL error in the helium radiograph from 1.03 mm to 0.93 mm while no improvement was seen in the CNN filtered pRads. Conclusion: The CNN improved the filtering of proton and helium tracks. Only in the helium radiograph did this lead to improved image quality

Exploration of Differentiability in a Proton Computed Tomography Simulation Framework

Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how 'well-linearizable' the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path. Jumps in the MC function likely arose from changes in the control flow that affect the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. The MC and MBIR codes are ready for the integration of AD, and further research on surrogate models for the tracking subprocedure is necessary