Hasil untuk "Manufactures"

Jannatul Bushra, Md Habibor Rahman, Mohammed Shafae et al.

Reverse engineering can be used to derive a 3D model of an existing physical part when such a model is not readily available. For parts that will be fabricated with subtractive and formative manufacturing processes, existing reverse engineering techniques can be readily applied, but parts produced with additive manufacturing can present new challenges due to the high level of process-induced distortions and unique part attributes. This paper introduces an integrated 3D scanning and process simulation data-driven framework to minimize distortions of reverse-engineered additively manufactured components. This framework employs iterative finite element simulations to predict geometric distortions to minimize errors between the predicted and measured geometrical deviations of the key dimensional characteristics of the part. The effectiveness of this approach is then demonstrated by reverse engineering two Inconel-718 components manufactured using laser powder bed fusion additive manufacturing. This paper presents a remanufacturing process that combines reverse engineering and additive manufacturing, leveraging geometric feature-based part compensation through process simulation. Our approach can generate both compensated STL and parametric CAD models, eliminating laborious experimentation during reverse engineering. We evaluate the merits of STL-based and CAD-based approaches by quantifying the errors induced at the different steps of the proposed approach and analyzing the influence of varying part geometries. Using the proposed CAD-based method, the average absolute percent error between simulation-predicted distorted dimensions and actual measured dimensions of the manufactured parts was 0.087%, with better accuracy than the STL-based method.

Sebastian Monka, Irlan Grangel-González, Stefan Schmid et al.

Knowledge graphs (KGs) have transformed data management within the manufacturing industry, offering effective means for integrating disparate data sources through shared and structured conceptual schemas. However, harnessing the power of KGs can be daunting for non-experts, as it often requires formulating complex SPARQL queries to retrieve specific information. With the advent of Large Language Models (LLMs), there is a growing potential to automatically translate natural language queries into the SPARQL format, thus bridging the gap between user-friendly interfaces and the sophisticated architecture of KGs. The challenge remains in adequately informing LLMs about the relevant context and structure of domain-specific KGs, e.g., in manufacturing, to improve the accuracy of generated queries. In this paper, we evaluate multiple strategies that use LLMs as mediators to facilitate information retrieval from KGs. We focus on the manufacturing domain, particularly on the Bosch Line Information System KG and the I40 Core Information Model. In our evaluation, we compare various approaches for feeding relevant context from the KG to the LLM and analyze their proficiency in transforming real-world questions into SPARQL queries. Our findings show that LLMs can significantly improve their performance on generating correct and complete queries when provided only the adequate context of the KG schema. Such context-aware prompting techniques help LLMs to focus on the relevant parts of the ontology and reduce the risk of hallucination. We anticipate that the proposed techniques help LLMs to democratize access to complex data repositories and empower informed decision-making in manufacturing settings.

Rongfei Li

The use of robotic technology has drastically increased in manufacturing in the 21st century. But by utilizing their sensory cues, humans still outperform machines, especially in micro scale manufacturing, which requires high-precision robot manipulators. These sensory cues naturally compensate for high levels of uncertainties that exist in the manufacturing environment. Uncertainties in performing manufacturing tasks may come from measurement noise, model inaccuracy, joint compliance (e.g., elasticity), etc. Although advanced metrology sensors and high precision microprocessors, which are utilized in modern robots, have compensated for many structural and dynamic errors in robot positioning, a well-designed control algorithm still works as a comparable and cheaper alternative to reduce uncertainties in automated manufacturing. Our work illustrates that a multi-robot control system that simulates the positioning process for fastening and unfastening applications can reduce various uncertainties, which may occur in this process, to a great extent. In addition, most research papers in visual servoing mainly focus on developing control and observation architectures in various scenarios, but few have discussed the importance of the camera's location in the configuration. In a manufacturing environment, the quality of camera estimations may vary significantly from one observation location to another, as the combined effects of environmental conditions result in different noise levels of a single image shot at different locations. Therefore, in this paper, we also propose a novel algorithm for the camera's moving policy so that it explores the camera workspace and searches for the optimal location where the image noise level is minimized.

Akshay Raman, Chad Merrill, Abraham George et al.

Additive manufacturing (AM) has transformed the production landscape by enabling the precision creation of complex geometries. However, AM faces limitations when applied to challenging environments, such as elevated surfaces and remote locations. Aerial additive manufacturing, facilitated by drones, presents a solution to these challenges. However, despite advances in methods for the planning, control, and localization of drones, the accuracy of these methods is insufficient to run traditional feedforward extrusion-based additive manufacturing processes (such as Fused Deposition Manufacturing). Recently, the emergence of LLMs has revolutionized various fields by introducing advanced semantic reasoning and real-time planning capabilities. This paper proposes the integration of LLMs with aerial additive manufacturing to assist with the planning and execution of construction tasks, granting greater flexibility and enabling a feed-back based design and construction system. Using the semantic understanding and adaptability of LLMs, we can overcome the limitations of drone based systems by dynamically generating and adapting building plans on site, ensuring efficient and accurate construction even in constrained environments. Our system is able to design and build structures given only a semantic prompt and has shown success in understanding the spatial environment despite tight planning constraints. Our method's feedback system enables replanning using the LLM if the manufacturing process encounters unforeseen errors, without requiring complicated heuristics or evaluation functions. Combining the semantic planning with automatic error correction, our system achieved a 90% build accuracy, converting simple text prompts to build structures.

Angelos Alexopoulos, Agorakis Bompotas, Nikitas Rigas Kalogeropoulos et al.

Modern manufacturing demands high flexibility and reconfigurability to adapt to dynamic production needs. Model-based Engineering (MBE) supports rapid production line design, but final reconfiguration requires simulations and validation. Digital Twins (DTs) streamline this process by enabling real-time monitoring, simulation, and reconfiguration. This paper presents a novel platform that automates DT generation and deployment using AutomationML-based factory plans. The platform closes the loop with a GAI-powered simulation scenario generator and automatic physical line reconfiguration, enhancing efficiency and adaptability in manufacturing.

Wei Chen, Lianyong Xu, Wenchun Jiang et al.

In this work, we demonstrate a phase engineering strategy, based on a composite design of monomorphic diamond (MD) reinforced FV520B maraging steel, for controlling matrix phase constituents with high strength and ductility. Benefiting from the enhanced grain growth rate induced by the MD addition, the solidification structure transition from planar to cellular and further to dendritic was achieved. The dissolved MD solute modifies the martensite-to-austenite transformation kinetics, promoting the stabilisation of a predominantly austenitic phase structure. Meanwhile, the elemental segregation around the cellular structure contributes to the massive formation of the M23C6 carbides. Consequently, the microstructural changes due to the MD particle addition result in an exceptional synergy of strength and ductility. This work provides a promising way to fabricate dispersion-strengthened maraging steels with high overall performance.

Huijun Li, Paulo Jorge Da Silva Bartolo, Kun Zhou

Four-dimensional (4D) printing combines shape-morphing materials and three-dimensional (3D) printing technology, enabling efficient fabrication of complex shape-changing structures. However, 4D printing of hydrogels into structures with complex shapes suffers from poor printability, which limit their practical applications. Here, we present an efficient strategy for direct 4D printing of hydrogels, leveraging intricate structural designs and highly viscous hydrogels. This strategy facilitates programmable shape-morphing through precise control of filament spacing and orientation, resulting in gradient swelling behaviours when the structures are immersed in a Ca2+-ion solution. Our study also reveals the critical role of printability in improving shape-morphing performance. On this basis, we propose a practical solution to enhance the shape-morphing capability of hydrogels with limited inherent performance by improving their printability through the addition of viscous additives such as MC or PVA. Overall, this strategy expands the list of hydrogels suitable for 4D printing, demonstrating compatibility with both synthetic and natural hydrogels, including Alginate/Methylcellulose (ALG/MC), gelatin methacryloyl (GELMA), and ALG/polyvinyl alcohol (PVA). Various sophisticated plant-inspired shape-morphing behaviours can be achieved in 4D-printed hydrogels through precise control of structural topology. The combined strategy of employing highly viscous hydrogel with intricate structural design demonstrates vast potential for applications in biomimetic soft robotics.

Vavrová Katarína, Šarlina Igor, Kostiuk Yaroslava et al.

Background: Smart technologies serve as a bridge between strategic business goals and sustainable development, creating a synergy among the economic, environmental, and social dimensions of business and circular urbanism. Purpose: The objective of this paper is to analyse the impact of implementing smart technologies on the economic benefits for an urban centre in a Central European Union state. Studydesign/methodology/approach: The research employs an econometric model to predict financial savings (30%, 40%, and 55%) resulting from the implementation of smart technologies in waste management within a selected urban centre. Findings/conclusions: The predictions confirmed the existence of a positive and growing trend in financial savings across all analysed areas, highlighting the economic benefits of smart technology adoption. Limitations/future research: The limitations of the research consist of inconsistencies in the implementation of smart technologies in waste management across different municipalities within the analysed country. Future research could expand the research sample to multiple urban centres and countries after the introduction of legislation that incentivises the uniform adoption of current smart technologies and the publication of up-to-date implementation data. This would facilitate the development of sustainable strategic plans and decisions that are tailored to both national level and local needs of individual urban centres, offering effective and long-term solutions for sustainable urban development.

Natthakritta Rungtalay, Somyot Kaitwanidvilai

ABSTRACT This study aims to predict hard disk drives (HDDs) that pass initial testing but fail during reliability testing, using historical data from 8968 records with 218 features, such as head position and flying height of the read/write head. Since reliability testing is time‐intensive, early failure prediction can significantly accelerate problem detection and resolution. The research focuses on detecting fly height modulation, a key symptom of HDD failure, and introduces an adaptive machine learning (ML) framework integrating AutoML for optimised model selection and hyperparameter tuning with MLOps for deployment, monitoring and continuous updates. Building on a previously proposed dual‐stage classification framework that combines novelty detection and supervised learning, the proposed framework addresses the inefficiencies of manual hyperparameter tuning inherent in the earlier methods. The proposed framework achieves 92% accuracy in novelty detection and 100% in supervised learning, outperforming prior approaches. This integration of AutoML and MLOps offers a scalable, robust solution for early failure prediction, enabling real‐time adaptability with minimal human intervention. Future work will focus on enhancing computational efficiency and responsiveness to data shifts and drifts, advancing data‐driven decision‐making in reliability testing.

Elizabeth Oluwakemi Ayandibu

This study investigates the role of academic advising and mentorship in shaping students’ entrepreneurial career choices, emphasizing their psychological and developmental impacts. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a systematic literature review was conducted across Scopus, Web of Science, and Google Scholar to identify relevant peer-reviewed articles, institutional reports, and publications from 2010 to 2025. Thematic analysis revealed that academic advising, when applied holistically, extends beyond course selection and graduation planning to include personal development, networking, and exposure to entrepreneurial ecosystems. Mentorship complements this role by providing real-world insights, professional networks, and role models that build entrepreneurial self-efficacy and resilience. Findings show that self-efficacy is a central determinant of entrepreneurial intention, with advisors and mentors jointly fostering confidence, motivation, and readiness to pursue self-employment. Case evidence indicates that institutions integrating structured mentorship programs within advising achieve higher student engagement and entrepreneurial uptake. Despite these benefits, challenges persist, including inadequate advisor training, limited institutional resources, and underutilization of mentorship opportunities. The study contributes to the literature by demonstrating how the advising–mentorship nexus can serve as an innovative mechanism for embedding entrepreneurship within higher education, particularly by leveraging hybrid delivery models and targeted outreach for underrepresented groups. Policy and practice implications include the need for professional training of advisors, investment in institutional support systems, and the use of digital platforms to expand mentorship access. Strengthening the synergy between academic advising and mentorship can foster an inclusive entrepreneurial ecosystem in higher education, equipping students with the confidence, networks, and skills necessary for sustainable entrepreneurial careers.

Xingyu Li, Fei Tao, Wei Ye et al.

In this study, we introduce Generative Manufacturing Systems (GMS) as a novel approach to effectively manage and coordinate autonomous manufacturing assets, thereby enhancing their responsiveness and flexibility to address a wide array of production objectives and human preferences. Deviating from traditional explicit modeling, GMS employs generative AI, including diffusion models and ChatGPT, for implicit learning from envisioned futures, marking a shift from a model-optimum to a training-sampling decision-making. Through the integration of generative AI, GMS enables complex decision-making through interactive dialogue with humans, allowing manufacturing assets to generate multiple high-quality global decisions that can be iteratively refined based on human feedback. Empirical findings showcase GMS's substantial improvement in system resilience and responsiveness to uncertainties, with decision times reduced from seconds to milliseconds. The study underscores the inherent creativity and diversity in the generated solutions, facilitating human-centric decision-making through seamless and continuous human-machine interactions.

Muhammad Tayyab Khan, Lequn Chen, Ye Han Ng et al.

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.

Yu-hsuan Chen, Jonathan Cagan, Levent Burak kara

Designing for manufacturing poses significant challenges in part due to the computation bottleneck of Computer-Aided Manufacturing (CAM) simulations. Although deep learning as an alternative offers fast inference, its performance is dependently bounded by the need for abundant training data. Representation learning, particularly through pre-training, offers promise for few-shot learning, aiding in manufacturability tasks where data can be limited. This work introduces VIRL, a Volume-Informed Representation Learning approach to pre-train a 3D geometric encoder. The pretrained model is evaluated across four manufacturability indicators obtained from CAM simulations: subtractive machining (SM) time, additive manufacturing (AM) time, residual von Mises stress, and blade collisions during Laser Power Bed Fusion process. Across all case studies, the model pre-trained by VIRL shows substantial enhancements on demonstrating improved generalizability with limited data and superior performance with larger datasets. Regarding deployment strategy, case-specific phenomenon exists where finetuning VIRL-pretrained models adversely affects AM tasks with limited data but benefits SM time prediction. Moreover, the efficacy of Low-rank adaptation (LoRA), which balances between probing and finetuning, is explored. LoRA shows stable performance akin to probing with limited data, while achieving a higher upper bound than probing as data size increases, without the computational costs of finetuning. Furthermore, static normalization of manufacturing indicators consistently performs well across tasks, while dynamic normalization enhances performance when a reliable task dependent input is available.

Alireza Barekatain, Hamed Habibi, Holger Voos

This paper provides a structured and practical roadmap for practitioners to integrate Learning from Demonstration (LfD ) into manufacturing tasks, with a specific focus on industrial manipulators. Motivated by the paradigm shift from mass production to mass customization, it is crucial to have an easy-to-follow roadmap for practitioners with moderate expertise, to transform existing robotic processes to customizable LfD-based solutions. To realize this transformation, we devise the key questions of "What to Demonstrate", "How to Demonstrate", "How to Learn", and "How to Refine". To follow through these questions, our comprehensive guide offers a questionnaire-style approach, highlighting key steps from problem definition to solution refinement. The paper equips both researchers and industry professionals with actionable insights to deploy LfD-based solutions effectively. By tailoring the refinement criteria to manufacturing settings, the paper addresses related challenges and strategies for enhancing LfD performance in manufacturing contexts.

Somraj Bhattacharjee, Archit Vinod Tapar

Junhong Ye, Zicheng Zhuang, Fei Teng et al.

Engineered cementitious composites (ECC) with superior tensile properties have potential to print self-reinforced structures. However, the environmental performance of 3D concrete printing with ECC (3DP-ECC) lacks further investigation. This study evaluates the environmental impacts of structures printed with 3DP-ECC via life cycle assessment. Results show that 3DP-ECC incorporating incineration bottom ash (IBA), crumb rubber (CR), and limestone powder (LP) reduce carbon emission by 25%, 24%, and 47%, respectively, compared to that of reinforced concrete (RC) with a steel ratio of 1.01%. A frame structure printed by LP-ECC reduces carbon emission by 42% compared to that of the unit fabricated by mold-cast RC (MC-RC). A circle house printed by LP-ECC reduces carbon emission by 28% compared to that of the counterpart fabricated by MC-RC. Sensitivity analysis identifies the transportation distance range to achieve a sustainable 3DCP. The findings provide a guideline to select appropriate 3DP-ECC and construction methods for sustainable construction.

Arti Kumbhar, Amruta Chougule, Priya Lokhande et al.

Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This technology excels in precisely identifying faults by extracting intricate details from product photographs, utilizing RNNs to detect evolving errors and generating synthetic defect data to bolster the model's robustness and adaptability across various defect scenarios. The project leverages a deep learning framework to automate real-time flaw detection in the manufacturing process. It harnesses extensive datasets of annotated images to discern complex defect patterns. This integrated system seamlessly fits into production workflows, thereby boosting efficiency and elevating product quality. As a result, it reduces waste and operational costs, ultimately enhancing market competitiveness.

Pablo Martin-Ramiro, Unai Sainz de la Maza, Sukhbinder Singh et al.

Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector. In this work, we introduce a Tensor Convolutional Neural Network (T-CNN) and examine its performance on a real defect detection application in one of the components of the ultrasonic sensors produced at Robert Bosch's manufacturing plants. Our quantum-inspired T-CNN operates on a reduced model parameter space to substantially improve the training speed and performance of an equivalent CNN model without sacrificing accuracy. More specifically, we demonstrate how T-CNNs are able to reach the same performance as classical CNNs as measured by quality metrics, with up to fifteen times fewer parameters and 4% to 19% faster training times. Our results demonstrate that the T-CNN greatly outperforms the results of traditional human visual inspection, providing value in a current real application in manufacturing.

Chaoyue Liu, Teo Lombardo, Jiahui Xu et al.

Electrode manufacturing is at the core of the lithium ion battery (LIB) fabrication process. The electrode microstructure and the electrochemical performance are determined by the adopted manufacturing parameters. However, in view of the strong interdependencies between these parameters, evaluating their influence on the performance is not a trivial task. In this work we present an experimentally validated 3D-resolved electrochemical model of a NMC111-based electrode which reveals how slurry formulation and calendering degree affect the electrode performance. A series of electrodes with different formulations and calendering degrees were fabricated at the experimental level. Corresponding three-dimensional manufacturing models were built based on the same experimental manufacturing parameters to generate the digital counterparts of the experimental electrodes that were then used in the electrochemical model. The results of simulations and experiments were compared individually. Among the manufacturing parameters analyzed, we found that the major factors linking manufacturing parameters and electrode performance are the carbon and binder domain (CBD) distribution within the electrode volume, and the electrostatic potential difference between the electrode and the current collector. A well-connected electronic conductive network throughout the electrode is vital for ensuring full utilization of active material, and it was found that increasing calendering degree is effective in reducing interfacial impedance. This work uncovers, based on a dual modeling/experimental approach, the essence of how electrode manufacturing process takes effect on electrode performance by influencing its microstructure.

Zakia Batool, Muhammad Haroon, Sajjad Ali et al.

Over the last several decades, the amount of international migration has increased dramatically, resulting in enormous cash flows to labour-exporting nations. The importance of remittances in sustaining families in poor nations has been well acknowledged by many researchers but at the same time a well-functioning banking system has been deemed important to increase migrant transfers by lowering prices and improving service availability. Therefore, this study attempts to analyse the role of financial sector development in enhancing the effect of remittances in spurring economic growth. This study uses time series data for the period of 198-2020 to delve into the nexus. Using ARDL approach, this study finds the complementary role of remittances and financial sector in both long run and short run.