Advanced in-situ layer-wise quality control for laser-based additive manufacturing using image sequence analysis

Quality assurance has been one of the major challenges in laser-based additive manufacturing (AM) processes. This study proposes a novel process modeling methodology for layer-wise in-situ quality monitoring based on image series analysis. An image-based autoregressive (AR) model has been proposed based on the image registration function between consecutively observed thermal images. Image registration is used to extract melt pool location and orientation change between consecutive images, which contains sensing stability information. Subsequently, a Gaussian process model is used to characterize the spatial correlation within the error matrix. Finally, the extracted features from the aforementioned processes are jointly used for layer-wise quality monitoring. A case study of a thin wall fabrication by a Directed Laser Deposition (DLD) process is used to demonstrate the effectiveness of the proposed methodology

Spatio-temporal Analysis of Thermal Profiles in Extrusion-based Additive Manufacturing

Extrusion-based Additive Manufacturing (AM) processes have recently gained increasing attention in the scientific and industrial communities because of the wide range of processible materials (from thermoplastics to composite and biomaterials), printable volumes, and industrial applications. As for many other AM processes, the actual problems with process stability and repeatability are still limiting the industrial process adoption, as these problems can significantly impact on the final part quality. In this framework, a latest research trend aims at developing in-situ monitoring solutions for inline defect detection, in a zero-waste production perspective. Among the existing in-situ sensing techniques, many studies showed that in-situ thermography represents a viable solution to describe the temperature dynamic and validate the thermal models but very few approaches have been proposed to quantitively study the temperature evolution to quickly detect process instabilities. This paper presents a new approach to quickly analyse the temporal dynamic of temperature in the printed layer while providing a spatial mapping of the temperature homogeneities. Compared with previous methods, the current one has the main novelty feature of combining both the spatial and temporal signature in a synthetic mapping that allows to detect unstable or unusual problems. In order to show the effectiveness of the proposed solution, a real case study of Big Area Additive Manufacturing (BAAM) for composite materials is considered. The study shows that the provided method can clearly enhance defect detection and represents a new solution for detecting anomalous areas where thermal profiles behave differently with respect to the surrounding areas. The same methodology underlined the thermal evolution complexity in the BAAM case study and enabled the detection of local flaws, i.e., hot and cold spots

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

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

Laser powder bed additive manufacturing: A review on the four drivers for an online control

Online control of Additive Manufacturing (AM) processes appears to be the next challenge in the transition toward Industry 4.0 (I4.0). Although many efforts have been dedicated by industry and research in the last decades, there remains substantial room for improvement. Additionally, the existing scientific literature lacks a wide-ranging identification and classification of the primary drivers that enable online control of AM processes. This article focuses on online control of one of the most industrially widespread AM processes: metal Laser Powder Bed Fusion (L-PBF), with particular emphasis on two subcategories, namely Selective Laser Sintering (SLS) and Selective Laser Melting (SLM). Through a systematic literature review, this article initially identified over 200 manuscripts. The search was conducted utilizing a defined research query within the Scopus database, double checked on Scholar. The results were refined through multiple phases of inclusion/exclusion criteria, culminating in the selection of 95 pertinent papers. This article aims to provide a systematic and comprehensive review of four identified drivers i) Online controllable input parameters, ii) Online observable output signatures, iii) Online sensing techniques, iv) Online feedback strategies, adopted from the general Deming control loop Plan-Do-Check-Act (PDCA). Ultimately, this article delves into the challenges and prospects inherent in the online control of metal L-PBF

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals

Evaluation of Generative Models for Predicting Microstructure Geometries in Laser Powder Bed Fusion Additive Manufacturing

In-situ process monitoring for metals additive manufacturing is paramount to the successful build of an object for application in extreme or high stress environments. In selective laser melting additive manufacturing, the process by which a laser melts metal powder during the build will dictate the internal microstructure of that object once the metal cools and solidifies. The difficulty lies in that obtaining enough variety of data to quantify the internal microstructures for the evaluation of its physical properties is problematic, as the laser passes at high speeds over powder grains at a micrometer scale. Imaging the process in-situ is complex and cost-prohibitive. However, generative modes can provide new artificially generated data. Generative adversarial networks synthesize new computationally derived data through a process that learns the underlying features corresponding to the different laser process parameters in a generator network, then improves upon those artificial renderings by evaluating through the discriminator network. While this technique was effective at delivering high-quality images, modifications to the network through conditions showed improved capabilities at creating these new images. Using multiple evaluation metrics, it has been shown that generative models can be used to create new data for various laser process parameter combinations, thereby allowing a more comprehensive evaluation of ideal laser conditions for any particular build

Powder bed monitoring via digital image analysis in additive manufacturing

Due to the nature of Selective Laser Melting process, the built parts suffer from high chances of defects formation. Powders quality have a significant impact on the final attributes of SLM-manufactured items. From a processing standpoint, it is critical to ensure proper powder distribution and compaction in each layer of the powder bed, which is impacted by particle size distribution, packing density, flowability, and sphericity of the powder particles. Layer-by-layer study of the process can provide better understanding of the effect of powder bed on the final part quality. Image-based processing technique could be used to examine the quality of parts fabricated by Selective Laser Melting through layerwise monitoring and to evaluate the results achieved by other techniques. In this paper, a not supervised methodology based on Digital Image Processing through the build-inmachine camera is proposed. Since the limitation of the optical system in terms of resolution, positioning, lighting, field-of-view, many efforts were paid to the calibration and to the data processing. Its capability to individuate possible defects on SLM parts was evaluated by a Computer Tomography results verification

Seeing the sound: a new multimodal imaging device for computer vision

Audio imaging can play a fundamental role in computer vision, in particular in automated surveillance, boosting the accuracy of current systems based on standard optical cameras. We present here a new hybrid device for acousticoptic imaging, whose characteristics are tailored to automated surveillance. In particular, the device allows realtime, high frame rate generation of an acoustic map, overlaid over a standard optical image using a geometric calibration of audio and video streams. We demonstrate the potentialities of the device for target tracking on three challenging setup showing the advantages of using acoustic images against baseline algorithms on image tracking. In particular, the proposed approach is able to overcome, often dramatically, visual tracking with state-of-art algorithms, dealing efficiently with occlusions, abrupt variations in visual appearence and camouflage. These results pave the way to a widespread use of acoustic imaging in application scenarios such as in surveillance and security