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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.03707">arXiv:2411.03707</a> <span> [<a href="https://arxiv.org/pdf/2411.03707">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Fine-Tuning Vision-Language Model for Automated Engineering Drawing Information Extraction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khan%2C+M+T">Muhammad Tayyab Khan</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+L">Lequn Chen</a>, <a href="/search/cs?searchtype=author&query=Ng%2C+Y+H">Ye Han Ng</a>, <a href="/search/cs?searchtype=author&query=Feng%2C+W">Wenhe Feng</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+N+Y+J">Nicholas Yew Jin Tan</a>, <a href="/search/cs?searchtype=author&query=Moon%2C+S+K">Seung Ki Moon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.03707v1-abstract-short" style="display: inline;"> Geometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionality. However, extracting GD&T information from 2D engineering drawings is a time-consuming and labor-intensive task, often relying on manual efforts or semi-automated tools. To address these challenges, this study proposes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03707v1-abstract-full').style.display = 'inline'; document.getElementById('2411.03707v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03707v1-abstract-full" style="display: none;"> Geometric Dimensioning and Tolerancing (GD&T) plays a critical role in manufacturing by defining acceptable variations in part features to ensure component quality and functionality. However, extracting GD&T information from 2D engineering drawings is a time-consuming and labor-intensive task, often relying on manual efforts or semi-automated tools. To address these challenges, this study proposes an automated and computationally efficient GD&T extraction method by fine-tuning Florence-2, an open-source vision-language model (VLM). The model is trained on a dataset of 400 drawings with ground truth annotations provided by domain experts. For comparison, two state-of-the-art closed-source VLMs, GPT-4o and Claude-3.5-Sonnet, are evaluated on the same dataset. All models are assessed using precision, recall, F1-score, and hallucination metrics. Due to the computational cost and impracticality of fine-tuning large closed-source VLMs for domain-specific tasks, GPT-4o and Claude-3.5-Sonnet are evaluated in a zero-shot setting. In contrast, Florence-2, a smaller model with 0.23 billion parameters, is optimized through full-parameter fine-tuning across three distinct experiments, each utilizing datasets augmented to different levels. The results show that Florence-2 achieves a 29.95% increase in precision, a 37.75% increase in recall, a 52.40% improvement in F1-score, and a 43.15% reduction in hallucination rate compared to the best-performing closed-source model. These findings highlight the effectiveness of fine-tuning smaller, open-source VLMs like Florence-2, offering a practical and efficient solution for automated GD&T extraction to support downstream manufacturing tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03707v1-abstract-full').style.display = 'none'; document.getElementById('2411.03707v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper has been submitted to the 9th International Conference on Innovation in Artificial Intelligence (ICIAI 2025)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.02810">arXiv:2411.02810</a> <span> [<a href="https://arxiv.org/pdf/2411.02810">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> Leveraging Vision-Language Models for Manufacturing Feature Recognition in CAD Designs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khan%2C+M+T">Muhammad Tayyab Khan</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+L">Lequn Chen</a>, <a href="/search/cs?searchtype=author&query=Ng%2C+Y+H">Ye Han Ng</a>, <a href="/search/cs?searchtype=author&query=Feng%2C+W">Wenhe Feng</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+N+Y+J">Nicholas Yew Jin Tan</a>, <a href="/search/cs?searchtype=author&query=Moon%2C+S+K">Seung Ki Moon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.02810v1-abstract-short" style="display: inline;"> Automatic feature recognition (AFR) is essential for transforming design knowledge into actionable manufacturing information. Traditional AFR methods, which rely on predefined geometric rules and large datasets, are often time-consuming and lack generalizability across various manufacturing features. To address these challenges, this study investigates vision-language models (VLMs) for automating… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02810v1-abstract-full').style.display = 'inline'; document.getElementById('2411.02810v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.02810v1-abstract-full" style="display: none;"> Automatic feature recognition (AFR) is essential for transforming design knowledge into actionable manufacturing information. Traditional AFR methods, which rely on predefined geometric rules and large datasets, are often time-consuming and lack generalizability across various manufacturing features. To address these challenges, this study investigates vision-language models (VLMs) for automating the recognition of a wide range of manufacturing features in CAD designs without the need for extensive training datasets or predefined rules. Instead, prompt engineering techniques, such as multi-view query images, few-shot learning, sequential reasoning, and chain-of-thought, are applied to enable recognition. The approach is evaluated on a newly developed CAD dataset containing designs of varying complexity relevant to machining, additive manufacturing, sheet metal forming, molding, and casting. Five VLMs, including three closed-source models (GPT-4o, Claude-3.5-Sonnet, and Claude-3.0-Opus) and two open-source models (LLava and MiniCPM), are evaluated on this dataset with ground truth features labelled by experts. Key metrics include feature quantity accuracy, feature name matching accuracy, hallucination rate, and mean absolute error (MAE). Results show that Claude-3.5-Sonnet achieves the highest feature quantity accuracy (74%) and name-matching accuracy (75%) with the lowest MAE (3.2), while GPT-4o records the lowest hallucination rate (8%). In contrast, open-source models have higher hallucination rates (>30%) and lower accuracies (<40%). This study demonstrates the potential of VLMs to automate feature recognition in CAD designs within diverse manufacturing scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02810v1-abstract-full').style.display = 'none'; document.getElementById('2411.02810v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper has been submitted to The ASME Journal of Computing and Information Science in Engineering (JCISE)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.01371">arXiv:2410.01371</a> <span> [<a href="https://arxiv.org/pdf/2410.01371">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Applied Physics">physics.app-ph</span> </div> </div> <p class="title is-5 mathjax"> A method to estimate well flowing gas-oil ratio and composition using pressure and temperature measurements across a production choke, a seed composition of oil and gas, and a thermodynamic simulator </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Moon%2C+S+K">Seok Ki Moon</a>, <a href="/search/cs?searchtype=author&query=Stanko%2C+M">Milan Stanko</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.01371v1-abstract-short" style="display: inline;"> In this work we propose and demonstrate a method to estimate the flowing gas-oil ratio and composition of a hydrocarbon well stream using measurements of pressure and temperature across a production choke. The method consists of using a numerical solver on a thermodynamic simulator to recombine a seed oil and gas until the simulated temperature drop across the choke is equal to the measured value.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01371v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01371v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01371v1-abstract-full" style="display: none;"> In this work we propose and demonstrate a method to estimate the flowing gas-oil ratio and composition of a hydrocarbon well stream using measurements of pressure and temperature across a production choke. The method consists of using a numerical solver on a thermodynamic simulator to recombine a seed oil and gas until the simulated temperature drop across the choke is equal to the measured value. This method is meant for cases where it is not possible to measure periodically individual well composition. A study case and reference solution were generated using the reservoir model presented in the SPE (Society of Petroleum Engineers) comparative case Nr. 5 linked with a process simulator. Time profiles of well producing gas-oil ratio, wellstream compositions, compositions of surface conditions oil and gas, and temperature drop across the choke were generated with the models. The method proposed was then employed to estimate the flowing gas-oil ratio of the reference solution. Results show that the proposed method predicts with reasonable accuracy (maximum 12% percent error) the well gas-oil ratio and compositions during the life of the field when using compositions of surface oil and gas from initial time. When using compositions of surface oil and gas from later times, the prediction accuracy of the gas-oil ratio improves at those times but worsens for times before and after. A measurement error for the temperature drop across the choke of at least 0.01 掳C is required to achieve convergence of the method. The mean percent error between the predicted and real mole fractions has an upper bound in time of 21% when using initial surface oil and gas as seed compositions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01371v1-abstract-full').style.display = 'none'; document.getElementById('2410.01371v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 pages, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.06891">arXiv:2408.06891</a> <span> [<a href="https://arxiv.org/pdf/2408.06891">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Automatic Feature Recognition and Dimensional Attributes Extraction From CAD Models for Hybrid Additive-Subtractive Manufacturing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Khan%2C+M+T">Muhammad Tayyab Khan</a>, <a href="/search/cs?searchtype=author&query=Feng%2C+W">Wenhe Feng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+L">Lequn Chen</a>, <a href="/search/cs?searchtype=author&query=Ng%2C+Y+H">Ye Han Ng</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+N+Y+J">Nicholas Yew Jin Tan</a>, <a href="/search/cs?searchtype=author&query=Moon%2C+S+K">Seung Ki Moon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.06891v2-abstract-short" style="display: inline;"> The integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) plays a crucial role in modern manufacturing, facilitating seamless transitions from digital designs to physical products. However, a significant challenge within this integration is the Automatic Feature Recognition (AFR) of CAD models, especially in the context of hybrid… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06891v2-abstract-full').style.display = 'inline'; document.getElementById('2408.06891v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.06891v2-abstract-full" style="display: none;"> The integration of Computer-Aided Design (CAD), Computer-Aided Process Planning (CAPP), and Computer-Aided Manufacturing (CAM) plays a crucial role in modern manufacturing, facilitating seamless transitions from digital designs to physical products. However, a significant challenge within this integration is the Automatic Feature Recognition (AFR) of CAD models, especially in the context of hybrid manufacturing that combines subtractive and additive manufacturing processes. Traditional AFR methods, focused mainly on the identification of subtractive (machined) features including holes, fillets, chamfers, pockets, and slots, fail to recognize features pertinent to additive manufacturing. Furthermore, the traditional methods fall short in accurately extracting geometric dimensions and orientations, which are also key factors for effective manufacturing process planning. This paper presents a novel approach for creating a synthetic CAD dataset that encompasses features relevant to both additive and subtractive machining through Python Open Cascade. The Hierarchical Graph Convolutional Neural Network (HGCNN) model is implemented to accurately identify the composite additive-subtractive features within the synthetic CAD dataset. The key novelty and contribution of the proposed methodology lie in its ability to recognize a wide range of manufacturing features, and precisely extracting their dimensions, orientations, and stock sizes. The proposed model demonstrates remarkable feature recognition accuracy exceeding 97% and a dimension extraction accuracy of 100% for identified features. Therefore, the proposed methodology enhances the integration of CAD, CAPP, and CAM within hybrid manufacturing by providing precise feature recognition and dimension extraction. It facilitates improved manufacturing process planning, by enabling more informed decision-making. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06891v2-abstract-full').style.display = 'none'; document.getElementById('2408.06891v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 12 figures. This paper has been accepted for presentation at the ASME IDETC-CIE 2024 conference</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.05307">arXiv:2408.05307</a> <span> [<a href="https://arxiv.org/pdf/2408.05307">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Engineering, Finance, and Science">cs.CE</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xie%2C+J">Jiarui Xie</a>, <a href="/search/cs?searchtype=author&query=Safdar%2C+M">Mutahar Safdar</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+L">Lequn Chen</a>, <a href="/search/cs?searchtype=author&query=Moon%2C+S+K">Seung Ki Moon</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+Y+F">Yaoyao Fiona Zhao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.05307v2-abstract-short" style="display: inline;"> Various machine learning (ML)-based in-situ monitoring systems have been developed to detect anomalies and defects in laser additive manufacturing (LAM) processes. While multimodal fusion, which integrates data from visual, audio, and other modalities, can improve monitoring performance, it also increases hardware, computational, and operational costs due to the use of multiple sensor types. This… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05307v2-abstract-full').style.display = 'inline'; document.getElementById('2408.05307v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.05307v2-abstract-full" style="display: none;"> Various machine learning (ML)-based in-situ monitoring systems have been developed to detect anomalies and defects in laser additive manufacturing (LAM) processes. While multimodal fusion, which integrates data from visual, audio, and other modalities, can improve monitoring performance, it also increases hardware, computational, and operational costs due to the use of multiple sensor types. This paper introduces a cross-modality knowledge transfer (CMKT) methodology for LAM in-situ monitoring, which transfers knowledge from a source modality to a target modality. CMKT enhances the representativeness of the features extracted from the target modality, allowing the removal of source modality sensors during prediction. This paper proposes three CMKT methods: semantic alignment, fully supervised mapping, and semi-supervised mapping. The semantic alignment method establishes a shared encoded space between modalities to facilitate knowledge transfer. It employs a semantic alignment loss to align the distributions of identical groups (e.g., visual and audio defective groups) and a separation loss to distinguish different groups (e.g., visual defective and audio defect-free groups). The two mapping methods transfer knowledge by deriving features from one modality to another using fully supervised and semi-supervised learning approaches. In a case study for LAM in-situ defect detection, the proposed CMKT methods were compared with multimodal audio-visual fusion. The semantic alignment method achieved an accuracy of 98.7% while removing the audio modality during the prediction phase, which is comparable to the 98.2% accuracy obtained through multimodal fusion. Using explainable artificial intelligence, we discovered that semantic alignment CMKT can extract more representative features while reducing noise by leveraging the inherent correlations between modalities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.05307v2-abstract-full').style.display = 'none'; document.getElementById('2408.05307v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 9 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">45 pages, 17 figures, 6 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.04598">arXiv:2304.04598</a> <span> [<a href="https://arxiv.org/pdf/2304.04598">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1016/j.addma.2023.103547">10.1016/j.addma.2023.103547 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+L">Lequn Chen</a>, <a href="/search/cs?searchtype=author&query=Yao%2C+X">Xiling Yao</a>, <a href="/search/cs?searchtype=author&query=Tan%2C+C">Chaolin Tan</a>, <a href="/search/cs?searchtype=author&query=He%2C+W">Weiyang He</a>, <a href="/search/cs?searchtype=author&query=Su%2C+J">Jinlong Su</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+F">Fei Weng</a>, <a href="/search/cs?searchtype=author&query=Chew%2C+Y">Youxiang Chew</a>, <a href="/search/cs?searchtype=author&query=Ng%2C+N+P+H">Nicholas Poh Huat Ng</a>, <a href="/search/cs?searchtype=author&query=Moon%2C+S+K">Seung Ki Moon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2304.04598v1-abstract-short" style="display: inline;"> Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04598v1-abstract-full').style.display = 'inline'; document.getElementById('2304.04598v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.04598v1-abstract-full" style="display: none;"> Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04598v1-abstract-full').style.display = 'none'; document.getElementById('2304.04598v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">36 Pages, 16 Figures, accepted at journal Additive Manufacturing</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 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