Inspector Gadget: A Data Programming-based Labeling System for Industrial Images

As machine learning for images becomes democratized in the Software 2.0 era, one of the serious bottlenecks is securing enough labeled data for training. This problem is especially critical in a manufacturing setting where smart factories rely on machine learning for product quality control by analyzing industrial images. Such images are typically large and may only need to be partially analyzed where only a small portion is problematic (e.g., identifying defects on a surface). Since manual labeling these images is expensive, weak supervision is an attractive alternative where the idea is to generate weak labels that are not perfect, but can be produced at scale. Data programming is a recent paradigm in this category where it uses human knowledge in the form of labeling functions and combines them into a generative model. Data programming has been successful in applications based on text or structured data and can also be applied to images usually if one can find a way to convert them into structured data. In this work, we expand the horizon of data programming by directly applying it to images without this conversion, which is a common scenario for industrial applications. We propose Inspector Gadget, an image labeling system that combines crowdsourcing, data augmentation, and data programming to produce weak labels at scale for image classification. We perform experiments on real industrial image datasets and show that Inspector Gadget obtains better performance than other weak-labeling techniques: Snuba, GOGGLES, and self-learning baselines using convolutional neural networks (CNNs) without pre-training.Comment: 10 pages, 11 figure

Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process

As the complexity of modern manufacturing technologies increases, traditional trial-and-error design, which requires iterative and expensive simulations, becomes unreliable and time-consuming. This difficulty is especially significant for the design of hot-stamped safety-critical components, such as ultra-high-strength-steel (UHSS) B-pillars. To reduce design costs and ensure manufacturability, scalar-based Artificial-Intelligence-empowered surrogate modelling (SAISM) has been investigated and implemented, which can allow real-time manufacturability-constrained structural design optimisation. However, SAISM suffers from low accuracy and generalisability, and usually requires a high volume of training samples. To solve this problem, an image-based Artificial-intelligence-empowered surrogate modelling (IAISM) approach is developed in this research, in combination with an auto-decoder-based blank shape generator. The IAISM, which is based on a Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field of the as-formed component given an arbitrary blank shape. Excellent prediction performance of IAISM is achieved with only 256 training samples, which indicates the small-data learning nature of engineering AI tasks using structured data representations. The trained auto-decoder, trained Mask-Res-SE-U-Net, and Adam optimiser are integrated to conduct blank optimisation by modifying the latent vector. The optimiser can rapidly find blank shapes that satisfy manufacturability criteria. As a high-accuracy and generalisable surrogate modelling and optimisation tool, the proposed pipeline is promising to be integrated into a full-chain digital twin to conduct real-time, multi-objective design optimisation.Comment: 32 pages, 11 figure

Identifying Lung Cancer Using CT Scan Images Based On Artificial Intelligence

Lung cancer appears to be the common reason behind the death of human beings at some stage on the planet. Early detection of lung cancers can growth the possibility of survival amongst human beings. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected in time. Although computerized tomography (CT) is frequently more efficient than X-ray. However, the problem regarded to merge way to time constraints in detecting this lung cancer concerning the numerous diagnosing strategies used. Hence, a lung cancer detection system that usage of image processing is hired to categorize lung cancer in CT images. In image processing procedures, procedures like image pre-processing, segmentation, and have extraction are mentioned intimately. This paper is pointing to set off the extra precise comes approximately through making use of distinctive improve and department procedures. In this proposal paper, the proposed method is built in some filter and segmentation that pre-process the data and classify the trained data. After the classification and trained WONN-MLB method is used to reduce the time complexity of finding result. Therefore, our research goal is to get the maximum result of lung cancer detection

AI-based design methodologies for hot form quench (HFQ®)

This thesis aims to develop advanced design methodologies that fully exploit the capabilities of the Hot Form Quench (HFQ®) stamping process in stamping complex geometric features in high-strength aluminium alloy structural components. While previous research has focused on material models for FE simulations, these simulations are not suitable for early-phase design due to their high computational cost and expertise requirements. This project has two main objectives: first, to develop design guidelines for the early-stage design phase; and second, to create a machine learning-based platform that can optimise 3D geometries under hot stamping constraints, for both early and late-stage design. With these methodologies, the aim is to facilitate the incorporation of HFQ capabilities into component geometry design, enabling the full realisation of its benefits. To achieve the objectives of this project, two main efforts were undertaken. Firstly, the analysis of aluminium alloys for stamping deep corners was simplified by identifying the effects of corner geometry and material characteristics on post-form thinning distribution. New equation sets were proposed to model trends and design maps were created to guide component design at early stages. Secondly, a platform was developed to optimise 3D geometries for stamping, using deep learning technologies to incorporate manufacturing capabilities. This platform combined two neural networks: a geometry generator based on Signed Distance Functions (SDFs), and an image-based manufacturability surrogate model. The platform used gradient-based techniques to update the inputs to the geometry generator based on the surrogate model's manufacturability information. The effectiveness of the platform was demonstrated on two geometry classes, Corners and Bulkheads, with five case studies conducted to optimise under post-stamped thinning constraints. Results showed that the platform allowed for free morphing of complex geometries, leading to significant improvements in component quality. The research outcomes represent a significant contribution to the field of technologically advanced manufacturing methods and offer promising avenues for future research. The developed methodologies provide practical solutions for designers to identify optimal component geometries, ensuring manufacturing feasibility and reducing design development time and costs. The potential applications of these methodologies extend to real-world industrial settings and can significantly contribute to the continued advancement of the manufacturing sector.Open Acces

Influence of the ratio on the mechanical properties of epoxy resin composite with diapers waste as fillers for partition panel application

Materials play significant role in the domestic economy and defense with the fast growth of science and technology field. New materials are the core of fresh technologies and the three pillars of modern science and technology are materials science, power technology and data science. The prior properties of the partition panel by using recycled diapers waste depend on the origin of waste deposits and its chemical constituents. This study presents the influence of the ratio on the mechanical properties of polymer in diapers waste reinforced with binder matrix for partition panel application. The aim of this study was to investigate the influence of different ratio of diapers waste polymer reinforced epoxy-matrix with regards to mechanical properties and morphology analysis. The polymer includes polypropylene, polystyrene, polyethylene and superabsorbent polymer (SAP) were used as reinforcing material. The tensile and bending resistance for ratio of 0.4 diapers waste polymers indicated the optimum ratio for fabricating the partition panel. Samples with 0.4 ratios of diapers waste polymers have highest stiffness of elasticity reading with 76.06 MPa. A correlation between the micro structural analysis using scanning electron microscope (SEM) and the mechanical properties of the material has been discussed

Life jacket

Anyone who cannot swim well should wear life jacket whether they are in the water or around the water. Even those who are can swim well should wear the life jacket when they are doing activity such as swimming, fishing, boating or while doing any water-related activity. Life jacket is a kind of safety jacket that keeping the wearer float the in the water. The wearer may be in the conscious or unconscious condition but by wearing the life jacket we can minimize the risk of drowning because life jacket assist the wearer to keep floating in the water

Diagnosis and Prognosis of Occupational disorders based on Machine Learn- ing Techniques applied to Occupational Profiles

Work-related disorders have a global influence on people’s well-being and quality of life and are a financial burden for organizations because they reduce productivity, increase absenteeism, and promote early retirement. Work-related musculoskeletal disorders, in particular, represent a significant fraction of the total in all occupational contexts. In automotive and industrial settings where workers are exposed to work-related muscu- loskeletal disorders risk factors, occupational physicians are responsible for monitoring workers’ health protection profiles. Occupational technicians report in the Occupational Health Protection Profiles database to understand which exposure to occupational work- related musculoskeletal disorder risk factors should be ensured for a given worker. Occu- pational Health Protection Profiles databases describe the occupational physician states, and which exposure the physicians considers necessary to ensure the worker’s health protection in terms of their functional work ability. The application of Human-Centered explainable artificial intelligence can support the decision making to go from worker’s Functional Work Ability to explanations by integrating explainability into medical (re- striction) and supporting in two decision contexts: prognosis and diagnosis of individual, work related and organizational risk condition. Although previous machine learning ap- proaches provided good predictions, their application in an actual occupational setting is limited because their predictions are difficult to interpret and hence, not actionable. In this thesis, injured body parts in which the ability changed in a worker’s functional work ability status are targeted. On the one hand, artificial intelligence algorithms can help technical teams, occupational physicians, and ergonomists determine a worker’s workplace risk via the diagnosis and prognosis of body part(s) injuries; on the other hand, these approaches can help prevent work-related musculoskeletal disorders by identifying which processes are lacking in working condition improvement and which workplaces have a better match between the remaining functional work abilities. A sample of 2025 for the prognosis part (from the years of 2019 to 2020) and 7857 for the prognosis part of Occupational Health Protection Profiles based on Functional Work Ability textual re- ports in the Portuguese language in automotive industry factory. Machine learning-based Natural Language Processing methods were implemented to extract standardized infor- mation. The prognosis and diagnosis of Occupational Health Protection Profiles factors were developed in reliable Human-Centered explainable artificial intelligence system to promote a trustworthy Human-Centered explainable artificial intelligence system (enti- tled Industrial microErgo application). The most suitable regression models to predict the next medical appointment for the injured body regions were the models based on CatBoost regression, with R square and an RMSLE of 0.84 and 1.23 weeks, respectively. In parallel, CatBoost’s best regression model for most body parts is the prediction of the next injured body parts based on these two errors. This information can help tech- nical industrial teams understand potential risk factors for Occupational Health Protec- tion Profiles and identify warning signs of the early stages of musculoskeletal disorders.Os transtornos relacionados ao trabalho têm influência global no bem-estar e na quali- dade de vida das pessoas e são um ônus financeiro para as organizações, pois reduzem a produtividade, aumentam o absenteísmo e promovem a aposentadoria precoce. Os distúr- bios osteomusculares relacionados ao trabalho, em particular, representam uma fração significativa do total em todos os contextos ocupacionais. Em ambientes automotivos e industriais onde os trabalhadores estão expostos a fatores de risco de distúrbios osteomus- culares relacionados ao trabalho, os médicos do trabalho são responsáveis por monitorar os perfis de proteção à saúde dos trabalhadores. Os técnicos do trabalho reportam-se à base de dados dos Perfis de Proteção da Saúde Ocupacional para compreender quais os fatores de risco de exposição a perturbações músculo-esqueléticas relacionadas com o tra- balho que devem ser assegurados para um determinado trabalhador. As bases de dados de Perfis de Proteção à Saúde Ocupacional descrevem os estados do médico do trabalho e quais exposições os médicos consideram necessária para garantir a proteção da saúde do trabalhador em termos de sua capacidade funcional para o trabalho. A aplicação da inteligência artificial explicável centrada no ser humano pode apoiar a tomada de decisão para ir da capacidade funcional de trabalho do trabalhador às explicações, integrando a explicabilidade à médica (restrição) e apoiando em dois contextos de decisão: prognóstico e diagnóstico da condição de risco individual, relacionado ao trabalho e organizacional . Embora as abordagens anteriores de aprendizado de máquina tenham fornecido boas pre- visões, sua aplicação em um ambiente ocupacional real é limitada porque suas previsões são difíceis de interpretar e portanto, não acionável. Nesta tese, as partes do corpo lesiona- das nas quais a habilidade mudou no estado de capacidade funcional para o trabalho do trabalhador são visadas. Por um lado, os algoritmos de inteligência artificial podem aju- dar as equipes técnicas, médicos do trabalho e ergonomistas a determinar o risco no local de trabalho de um trabalhador por meio do diagnóstico e prognóstico de lesões em partes do corpo; por outro lado, essas abordagens podem ajudar a prevenir distúrbios muscu- loesqueléticos relacionados ao trabalho, identificando quais processos estão faltando na melhoria das condições de trabalho e quais locais de trabalho têm uma melhor correspon- dência entre as habilidades funcionais restantes do trabalho. Para esta tese, foi utilizada uma base de dados com Perfis de Proteção à Saúde Ocupacional, que se baseiam em relató- rios textuais de Aptidão para o Trabalho em língua portuguesa, de uma fábrica da indús- tria automóvel (Auto Europa). Uma amostra de 2025 ficheiros foi utilizada para a parte de prognóstico (de 2019 a 2020) e uma amostra de 7857 ficheiros foi utilizada para a parte de diagnóstico. . Aprendizado de máquina- métodos baseados em Processamento de Lingua- gem Natural foram implementados para extrair informações padronizadas. O prognóstico e diagnóstico dos fatores de Perfis de Proteção à Saúde Ocupacional foram desenvolvidos em um sistema confiável de inteligência artificial explicável centrado no ser humano (inti- tulado Industrial microErgo application). Os modelos de regressão mais adequados para prever a próxima consulta médica para as regiões do corpo lesionadas foram os modelos baseados na regressão CatBoost, com R quadrado e RMSLE de 0,84 e 1,23 semanas, res- pectivamente. Em paralelo, a previsão das próximas partes do corpo lesionadas com base nesses dois erros relatados pelo CatBoost como o melhor modelo de regressão para a mai- oria das partes do corpo. Essas informações podem ajudar as equipes técnicas industriais a entender os possíveis fatores de risco para os Perfis de Proteção à Saúde Ocupacio- nal e identificar sinais de alerta dos estágios iniciais de distúrbios musculoesqueléticos

Recent advances and applications of machine learning in metal forming processes

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Recent Advances and Applications of Machine Learning in Metal Forming Processes

Machine learning (ML) technologies are emerging in Mechanical Engineering, driven by the increasing availability of datasets, coupled with the exponential growth in computer performance. In fact, there has been a growing interest in evaluating the capabilities of ML algorithms to approach topics related to metal forming processes, such as: Classification, detection and prediction of forming defects; Material parameters identification; Material modelling; Process classification and selection; Process design and optimization. The purpose of this Special Issue is to disseminate state-of-the-art ML applications in metal forming processes, covering 10 papers about the abovementioned and related topics

Hardware Implementation of Convolutional STDP for On-line Visual Feature Learning

We present a highly hardware friendly STDP (Spike Timing Dependent Plasticity) learning rule for training Spiking Convolutional Cores in Unsupervised mode and training Fully Connected Classifiers in Supervised Mode. Examples are given for a 2-layer Spiking Neural System which learns in real time features from visual scenes obtained with spiking DVS (Dynamic Vision Sensor) Cameras.EU H2020 grant 644096 “ECOMODE”EU H2020 grant 687299 “NEURAM3”Ministry of Economy and Competitivity (Spain) /European Regional Development Fund TEC2012-37868-C04-01 (BIOSENSE)Junta de Andalucía (España) TIC-6091 (NANONEURO