Showing papers by "Marco Grasso published in 2022"

A Novel Method for In-process Inspection of Lattice Structures via In-situ Layerwise Imaging

Abstract: Lattice structure are among most promising geometrical features to take full advantage of the design freedom enabled by additive manufacturing. However, their several benefits can be adversely affected by local defects and geometrical deviations, which can be hardly identified via ex-situ metrology. This paper is the first to prove that lattice structure inspection can be implemented in-situ, tackling the uncertainty of in-process imaging. The method combines powder bed image segmentation with robust statistical modelling to translate the in-line 3D geometry reconstruction into a 1D representation of unit cell’s properties. Results demonstrate the agreement between in-situ modelling and ex-situ ground-truth inspections.

Open data for open science in Industry 4.0: In-situ monitoring of quality in additive manufacturing

Abstract: Abstract Open science has the capacity of boosting innovative solutions and knowledge development thanks to a transparent access to data shared within the research community and collaborative networks. Because of this, it has become a policy priority in various research and development strategy plans and roadmaps, but the awareness if its potential is still limited in industry. Additive manufacturing (AM) represents a field where open science initiatives may have a great impact, as large academic and industrial communities are working in the same area, enormous quantities of data are generated on a daily basis by companies and research centers, and many challenging problems still need to be solved. This article presents a case study based on an open science collaboration project between TRUMPF Laser- und Systemtechnik GmbH, one of the major AM systems developers and Politecnico di Milano. The case study relies on an open data set including in-line and in-situ signals gathered during the laser powder bed fusion of specimens of aluminum parts on an industrial machine. The signals were acquired by means of two photodiodes installed co-axially to the laser path. The specimens were designed to introduce, on purpose, anomalies in certain locations and in certain layers. The data set is specifically designed to support the development of novel in-situ monitoring methodologies for fast and robust anomaly detection while the part is being built. A layerwise statistical monitoring approach is proposed and preliminary results are presented, but the problem is open to additional research and to the exploration of novel solutions.

A tensor‐based hierarchical process monitoring approach for anomaly detection in additive manufacturing

Abstract: Additive manufacturing (AM) is a technology that enables the creation of complex shapes with advanced structural and functional properties. It has transformed the traditional manufacturing operations into a more flexible and efficient process, reshaping the whole value chain and allowing new levels of product customization. AM is a layer‐by‐layer manufacturing process, in which materials are deposited in each layer to create the object of interest. Due to the layer‐wise nature of the process, anomalies and defects might occur within each layer, across several layers or throughout the whole sample. An accurate and responsive detection strategy that enables the detection of various types of anomalies is essential for ensuring the quality and integrity of the manufactured product. In this paper, a hierarchical in situ process monitoring approach, namely, a three level monitoring strategy, is proposed to detect local, layer‐wise, and sample‐wise anomalies using thermal videos acquired during the manufacturing process. The proposed approach integrates hierarchical low‐rank tensor decomposition methods with statistical monitoring techniques to effectively detect anomalies at different levels, namely, the within‐layer level, the layer level, and the sample level. Simulations are used to evaluate the performance of the method and compare with existing benchmarks. The proposed approach is also applied to thermal videos acquired during the laser powder bed fusion (L‐PBF) process to illustrate its effectiveness in practice.

Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing

Abstract: Abstract For an increasing number of applications, the quality and the stability of manufacturing processes can be determined via image and video-image data analysis and new techniques are required to extract and synthesize the relevant information content enclosed in big sensor data to draw conclusions about the process and the final part quality. This paper focuses on video image data where the phenomena under study is captured by a point process whose spatial signature is of interest. A novel approach is proposed which combines spatial data modeling via Ripley’s K-function with Functional Analysis of Variance (FANOVA), i.e., Analysis of Variance on Functional data. The K-function allows to synthesize the spatial pattern information in a function while preserving the capability to capture changes in the process behavior. The method is applicable to quantities and phenomena that can be represented as clusters, or clouds, of spatial points evolving over time. In our case, the motivating case study regards the analysis of spatter ejections caused by the laser-material interaction in Additive Manufacturing via Laser Powder Bed Fusion (L-PBF). The spatial spread of spatters, captured in the form of point particles through in-situ high speed machine vision, can be used as a proxy to select the best conditions to avoid defects (pores) in the manufactured part. The proposed approach is shown to be not only an efficient way to translate the high-dimensional video image data into a lower dimensional format (the K-function curves), but also more effective than benchmark methods in detecting departures from a stable and in-control state.