Hierarchical metamodeling: Cross validation and predictive uncertainty

At Esaform 2013 a hierarchical metamodeling approach had been presented, able to com- bine the results of numerical simulations and physical experiments into a unique response surface, which is a "fusion'' of both data sets. The method had been presented with respect to the structural optimization of a steel tube, filled with an aluminium foam, intended as an anti-intrusion bar. The prediction yielded by a conventional way of metamodeling the results of FEM simulations can be considered trustworthy only if the accuracy of numerical models have been thoroughly tested and the simulation parameters have been sufficiently calibrated. On the contrary, the main advantage of a hierarchical metamodel is to yield a reliable prediction of a response variable to be optimized, even in the presence of non-completely calibrated or accurate FEM models. In order to demonstrate these statements, in this paper the authors wish to compare the prediction ability of a "fusion'' metamodel based on under-calibrated simulations, with a conventional approach based on calibratedFEMresults. Both metamodels will be cross validated with a "leave-one-out'' technique, i.e. by excluding one ex- perimental observation at a time and assessing the predictive ability of the model. Furthermore, the paper will demonstrate how the hierarchical metamodel is able to provide not only an average esti- mated value for each excluded experimental observation, but also an estimation of uncertainty of the prediction of the average value

Improved Signal Characterization via Empirical Mode Decomposition to Enhance in-line Quality Monitoring

The machine tool industry is facing the need to increase the sensorization of production systems to meet evolving market demands. This leads to the increasing interest for in-process monitoring tools that allow a fast detection of faults and unnatural process behaviours during the process itself. Nevertheless, the analysis of sensor signals implies several challenges. One major challenge consists of the complexity of signal patterns, which often exhibit a multiscale content, i.e., a superimposition of both stationary and non-stationary fluctuations on different time-frequency levels. Among time-frequency techniques, Empirical Mode Decomposition (EMD) is a powerful method to decompose any signal into its embedded oscillatory modes in a fully data-driven way, without any ex-ante basis selection. Because of this, it might be used effectively for automated monitoring and diagnosis of manufacturing processes. Unfortunately, it usually yields an over-decomposition, with single oscillation modes that can be split into more than one scale (this effect is also known as “mode mixing”). The literature lacks effective strategies to automatically synthetize the decomposition into a minimal number of physically relevant and interpretable components. This paper proposes a novel approach to achieve a synthetic decomposition of complex signals through the EMD procedure. A new criterion is proposed to group together multiple components associated to a common time-frequency pattern, aimed at summarizing the information content into a minimal number of modes, which may be easier to interpret. A real case study in waterjet cutting is presented, to demonstrate the benefits and the critical issues of the proposed approach

Multi-sensor multi-resolution data fusion modeling

Inspection analysis of 3D objects has progressed significantly due to the evolution of advanced sensors. Current sensors facilitate surface scanning at high or low resolution levels. In the inspection field, data from multi-resolution sensors have significant advantages over single-scale data. However, most data fusion methods are single-scale and are not suitable in their current form for multi-resolution sensors. Currently the main challenge is to integrate the diverse scanned information into a single geometric hierarchical model. In this work, a new approach for data fusion from multi-resolution sensors is presented. In addition, a correction function for data fusion, based on statistic models, for processing highly dense data (low accuracy) with respect to sparse data (high accuracy) is described. The feasibility of the methods is demonstrated on synthetic data that imitates CMM and laser measurements

Study of weighted fusion methods for the measurement of surface geometry

Four types of weighted fusion methods, including pixel-level, least-squares, parametrical and non-parametrical, have been classified and theoretically analysed in this study. In particular, the uncertainty propagation of the weighted least-squares fusion was analysed and its relation to the Kalman filter was studied. In cooperation with different fitting models, these four weighted fusion methods can be applied to a range of measurement challenges. The experimental results of this study show that the four weighted fusion methods compose a computationally efficient and reliable system for multi-sensor measurement problems, especially for freeform surface measurement. A comparison of weighted fusion with residual approximation-based fusion has also been conducted by providing the input datasets with different noise levels and sample sizes. The results demonstrated that weighted fusion and residual approximation-based fusion are complementary approaches applicable to most fusion scenarios

The effect of energy density and porosity structure on tensile properties of 316L stainless steel produced by laser powder bed fusion

Understanding the influence of process parameters and defect structure on the properties of parts produced via laser powder bed fusion (L-PBF) is a fundamental step towards the broader use of additive manufacturing technologies in critical applications. Furthermore, the ability to predict mechanical properties by simply considering information on the process parameters and defects observed via X-ray computer tomography (XCT) allows one to avoid expensive destructive testing, provide an in-depth understanding of the process quality and represents a viable solution towards process optimisation. Most of the previous works showed that energy density could be used as an excellent synthetic indicator to predict the mechanical properties of parts produced by L-PBF. This paper explores the effect of different energy density levels on the tensile properties of 316L stainless steel parts produced by L-PBF. Different from previous works in the literature, the same level of energy density is obtained considering various combinations of process parameters (speed, power and hatch distance). While energy density is shown to be a good synthetic indicator for predicting ultimate tensile strength (UTS) and yield strength (YS), a different behaviour is observed for elongation. Elongation shows a significant variability even when samples are produced at the same level of energy density, which contrasts with results obtained for UTS and YS. Synthetic indices representing the porosity structure are shown to be quite significant for predicting elongation even when the optimal energy density is considered. By combining process parameters with porosity structure, we show that almost a full prediction of the tensile properties can be achieved, paving the way for a significant reduction in expensive destructive tests

Spatially weighted PCA for monitoring video image data with application to additive manufacturing

Machine vision systems for in-line process monitoring in advanced manufacturing applications have attracted an increasing interest in recent years. One major goal is to quickly detect and localize the onset of defects during the process. This implies the use of image-based statistical process monitoring approaches to detect both when and where a defect originated within the part. This study presents a spatiotemporal method based on principal component analysis (PCA) to characterize and synthetize the information content of image streams for statistical process monitoring. A spatially weighted version of the PCA, called ST-PCA, is proposed to characterize the temporal auto-correlation of pixel intensities over sequential frames of a video-sequence while including the spatial information related to the pixel location within the image. The method is applied to the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras. A k-means clustering-based alarm rule is proposed to provide an identification of defects in both time and space. A comparison analysis based on simulated and real data shows that the proposed approach is faster than competitor methods in detecting the defects. A real case study in selective laser melting (SLM) of complex geometries is presented to demonstrate the performances of the approach and its practical use

Opportunities and challenges of quality engineering for additive manufacturing

Additive manufacturing (AM), commonly known as three-dimensional printing, is widely recognized as a disruptive technology, and it has the potential to fundamentally change the nature of future manufacturing. Through building products layer by layer, AM represents a paradigm shift in manufacturing, with many industrial applications. It enables production of huge varieties of customized products with considerable geometric complexity, extended capabilities, and functional performances. Despite tremendous enthusiasm AM faces major research challenges for widespread adoption of this innovative technology. Specifically, addressing the unique challenges associated with quality engineering of AM processes is crucial to the eventual success of AM. This article presents an overview of quality-related issues for AM processes and products, focusing on opportunities and challenges in quality inspection, monitoring, control, optimization, and transfer learning as well as on building quality into the product through design