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.

https://iopscience.iop.org/article/10.1088/1361-6501/aa5c4f/pdf

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

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

Laser based additive manufacturing in industry and academia

Despite the rapid adoption of laser powder bed fusion (LPBF) Additive Manufacturing by industry, current processes remain largely open-loop, with limited real-time monitoring capabilities. While some machines offer powder bed visualization during builds, they lack automated analysis capability. This work presents an approach for in-situ monitoring and analysis of powder bed images with the potential to become a component of a real-time control system in an LPBF machine. Specifically, a computer vision algorithm is used to automatically detect and classify anomalies that occur during the powder spreading stage of the process. Anomaly detection and classification are implemented using an unsupervised machine learning algorithm, operating on a moderately-sized training database of image patches. The performance of the final algorithm is evaluated, and its usefulness as a standalone software package is demonstrated with several case studies.

Anomaly Detection and Classification in a Laser Powder Bed Additive Manufacturing Process using a Trained Computer Vision Algorithm

In this paper, we propose a discriminative representation for patterned fabric defect detection when only limited negative samples are available. Fabric patches are efficiently classified into defectless and defective categories by Fisher criterion-based stacked denoising autoencoders (FCSDA). First, fabric images are divided into patches of the same size, and both defective and defectless samples are utilized to train FCSDA. Second, test patches are classified through FCSDA into defective and defectless categories. Finally, the residual between the reconstructed image and defective patch is calculated, and the defect is located by thresholding. Experimental results demonstrate the effectiveness of the proposed scheme in the defect detection for periodic patterned fabric and more complex jacquard warp-knitted fabric.

Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning

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.

/pdf/high-resolution-imaging-for-inspection-of-laser-beam-melting-4bijydbqt3.pdf

High resolution imaging for inspection of Laser Beam Melting systems

For the purpose of textile quality assurance, an algorithmic framework for fully automatic detection of weave patterns in woven fabrics is presented. The proposed method is able to handle fabrics of any rotation, material, and binding. Periodicity features within highly resolved fabric images are found and structured in a compact yarn matrix representation which allows to estimate the trajectories of single yarns. Fourier analysis, template matching, and fuzzy clustering are some of the key methods employed during the process. From the yarn matrix, the fabric's weave and density can directly be derived. Since a multitude of factors may falsify the output, a feedback loop is integrated to iteratively find an optimal result. The framework works completely blind, i.e., without any a priori knowledge of the fabric. The evaluation has been conducted on an extensive image database of 140 real-world fabric images including cotton, polyester, viscose, and carbon materials of plain, twill, or satin weave. The system proved to be robust and versatile as a 97 % detection accuracy could be achieved. Source codes and image databases are provided.

Blind weave detection for woven fabrics

A vision-based measurement system to quantify the yarn density of woven fabrics during production is presented. As an extension to an earlier developed fabric flaw detection system, the proposed framework consists of a combination of basic and custom-made image-processing techniques that allow to precisely track single wefts and warps within fabric images—in real-time. Several adaptations facilitate the measurement of density changes for plain, satin, and twill weaves. In this paper, the algorithmic framework has been evaluated in several comprehensive on-line experiments on a real-world air-jet loom and is additionally compared with three alternative methods for fabric density measurement. It proved to be precise, robust, and applicable for industrial use as it overcomes many of the existing shortcomings of current methods.

Vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

A self-contained inspection system for vision-based on-loom fabric defect detection is presented in this paper. Design and loom integration of a traversing camera sled, a camera vibration damper and a complementary back-light illumination are presented and discussed. Image acquisition strategies and traverse control are described to complete the discussion on hardware and mechanics. The main part of the paper focuses on a novel algorithmic framework for woven fabric defect detection in highly resolved (1,000+ ppi) image data. Within this scope, single yarns are tracked and measured in terms of position, size, and appearance in real time. An inspection prototype has been mounted onto an industrial loom. Extensive on-line and off-line evaluations for various fabric materials gave precise and stable detection results with few false alarms. A brief cost analysis for the prototype system is provided and completes the presentation of the system.

A traverse inspection system for high precision visual on-loom fabric defect detection

Computational Thinking is a core capability for most engineers. The term summarizes a set of skills needed to transform real-life challenges into problems that can be solved with the help of a computer and to apply computer-based solutions to questions at hand. This mindset is fundamental to almost every engineering task.

Dorian Schneider

Papers

High resolution imaging for inspection of Laser Beam Melting systems

Blind weave detection for woven fabrics

Vision-Based On-Loom Measurement of Yarn Densities in Woven Fabrics

A traverse inspection system for high precision visual on-loom fabric defect detection

Fostering computational thinking in engineering education: Challenges, examples, and best practices