Stefan Kleszczynski

Bio: Stefan Kleszczynski is an academic researcher from University of Duisburg-Essen. The author has contributed to research in topics: Amorphous metal & Materials science. The author has an hindex of 9, co-authored 26 publications receiving 340 citations. Previous affiliations of Stefan Kleszczynski include RWTH Aachen University.

Error detection in laser beam melting systems by high resolution imaging

Abstract: Laser Beam Melting as a member of Additive Manufacturing processes allows the fa brication of three-dimensional metallic parts with almost unlimited geometrical complexity and very good mechanical properties. However, its potential in areas of application such as aerospace or medicine has not yet been exploited due to the lack of process stability and quality management. For that reason samples with pre-defined process irregularities are built and the resulting errors are detected using high-resolution imaging. This paper presents an overview of typical process errors and proposes a catalog of measures to reduce process breakdowns. Based on this systematical summary a future contribution to quality assurance and process documentation is aspired.

High resolution imaging for inspection of Laser Beam Melting systems

Abstract: Laser Beam Melting (LBM) allows the fabrication of three-dimensional parts from metallic powder with almost unlimited geometrical complexity and very good mechanical properties. LBM works iteratively: a thin powder layer is deposited onto the build platform which is then melted by a laser according to the desired part geometry. Today, the potential of LBM in application areas such as aerospace or medicine has not yet been exploited due to the lack of process stability and quality management. For that reason, we present a high resolution imaging system for inspection of LBM systems which can be easily integrated into existing machines. A container file stores calibration images and all layer images of one build process (powder and melt result) with corresponding metadata (acquisition and process parameters) for documentation and further analysis. We evaluate the resolving power of our imaging system and show that it is able to inspect the process result on a microscopic scale. Sample images of a part built with varied process parameters are provided, which show that our system can detect topological flaws and is able to inspect the surface quality of built layers. The results can be used for flaw detection and parameter optimization, for example in material qualification.

Elevated Region Area Measurement for Quantitative Analysis of Laser Beam Melting Process Stability

Abstract: Laser beam melting (LBM) processes enable layer-based production of geometrically complex metallic parts with very good mechanical properties for Rapid Manufacturing. Collisions between powder coating mechanism and elevated part regions pose a major risk to process stability, which is crucial for industrial application. Minimizing elevated region area usually involves parameter tuning in a trial-and-error approach, as the process outcome is the only measure of stability. One published approach to quantifying elevated region area utilizes an imaging system, which acquires layer images of the powder bed after powder deposition and detects elevated regions using image analysis. We extend the image-based analysis to each part region, create quantitative visualizations of elevated region area for quick assessment/comparison and compute a figure of merit. In experimental build jobs with overhanging structures and different support junction parameters we gain insight into problematic part regions, which can be used as feedback in job design. The presented method helps to improve LBM process stability, which is strongly linked to process efficiency. Introduction In laser beam melting processes, parts are built in a powder bed by selectively melting the current powder layer according to the part geometry. After lowering the build platform a recoater blade or roller moves powder from the powder container onto the build platform (Figure 1a) before the laser melts the next layer. All steps are repeated to build the entire part. The possibility to produce complex and individual parts in a tool-less manufacturing process on the basis of CAD data was identified as the main driver of innovation in [1]. For industrial applications laser beam melting (LBM) is of special interest, due to the possibility to produce complex metal components with suitable mechanical properties. First LBM production lines are already established in the sector of medical technologies [2] and aerospace [3]. A problematic effect during LBM build jobs is the buildup of elevations, which stand out from the part layer (Figure 1b). As the powder layer is very thin (20 to 100 μm) these elevations are not covered by metal powder and may collide with the recoater blade causing

Detection of elevated regions in surface images from laser beam melting processes

Abstract: Laser Beam Melting (LBM) is a promising Additive Manufacturing technology that allows the layer-based production of complex metallic components suitable for industrial applications. Widespread application of LBM is hindered by a lack of quality management and process control. Elevated regions in produced layers pose a major risk to process stability as collisions between the powder coating mechanism and the part may occur, which cause damages to either one or even both. We train a classifier-based detector for elevated regions in laser exposure result images. For this purpose we acquire two high resolution layer images: one after laser exposure and another one after powder deposition for the next layer. Ground truth labels for critical regions are obtained from analysis of the latter, where elevated regions are not covered by powder. We compute dense descriptors (HOG, DAISY, LBP) on the surface image after laser exposure and compare their predictive power. The top five descriptor configurations are used to optimize parameters of Random Forest, Support Vector Machine and Stochastic Gradient Descent (SGD) classifiers. We validate the detectors with optimized parameters using cross-validation on 281 images from three build jobs. Using a DAISY descriptor with a SGD classifier we achieve a F1-score of 0.670. The presented method enables detection of elevated regions before powder coating is performed and can be extended to other surface inspection tasks in LBM layer images. Detection results can be used to assess LBM process parameters with respect to process stability during process design and for quality management in production.

Process defects and in situ monitoring methods in metal powder bed fusion: a review

Abstract: Despite continuous technological enhancements of metal Additive Manufacturing (AM) systems, the lack of process repeatability and stability still represents a barrier for the industrial breakthrough. The most relevant metal AM applications currently involve industrial sectors (e.g., aerospace and bio-medical) where defects avoidance is fundamental. Because of this, there is the need to develop novel in-situ monitoring tools able to keep under control the stability of the process on a layer-by-layer basis, and to detect the onset of defects as soon as possible. On the one hand, AM systems must be equipped with in-situ sensing devices able to measure relevant quantities during the process, a.k.a. process signatures. On the other hand, in-process data analytics and statistical monitoring techniques are required to detect and localize the defects in an automated way. This paper reviews the literature and the commercial tools for insitu monitoring of Powder Bed Fusion (PBF) processes. It explores the different categories of defects and their main causes, the most relevant process signatures and the in-situ sensing approaches proposed so far. Particular attention is devoted to the development of automated defect detection rules and the study of process control strategies, which represent two critical fields for the development of future smart PBF systems.

In-process sensing in selective laser melting (SLM) additive manufacturing

Abstract: Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.