Hasil untuk "Manufactures"

Supriyono, Hendra Franka, Rusmalah et al.

This study aims to develop biodegradable materials for sustainable food packaging and household products by enhancing their mechanical strength, barrier properties, and environmental performance. Conducted between January 2022 and July 2023, the research employed a mixed-method experimental design involving material formulation, functional performance testing, and lifecycle assessment (LCA). Three biopolymers – PLA, PHA, and TPS – were reinforced with natural additives such as cellulose, lignin, and nano-fillers. In addition, functional additives including thyme oil, cinnamon oil, tocopherols, and catechins were integrated to create active packaging solutions. The results showed that the modified biopolymers exhibited up to 60% higher mechanical strength and improved thermal and barrier properties. Antimicrobial additives reduced bacterial growth by 60%, while antioxidants extended food shelf life by 30%. Lifecycle analysis revealed a 50% reduction in carbon emissions and lower energy consumption compared to conventional plastics. This study contributes a novel, scalable approach to biodegradable packaging development, offering practical solutions for reducing plastic waste while maintaining product quality and safety. The findings support broader adoption of sustainable materials across packaging and household industries, promoting circular economy practices.

Sho Okazaki, Kohei Kaminishi, Takuma Fujiu et al.

Fault cause identification in automated manufacturing lines is challenging due to the system's complexity, frequent reconfigurations, and the limited reusability of existing Failure Mode and Effects Analysis (FMEA) knowledge. Although FMEA worksheets contain valuable expert insights, their reuse across heterogeneous lines is hindered by natural language variability, inconsistent terminology, and process differences. To address these limitations, this study proposes a process-aware framework that enhances FMEA reusability by combining manufacturing-domain conceptualization with graph neural network (GNN) reasoning. First, FMEA worksheets from multiple manufacturing lines are transformed into a unified knowledge graph through ontology-guided large language model (LLM) extraction, capturing domain concepts such as actions, states, components, and parameters. Second, a Relational Graph Convolutional Network (RGCN) with the process-aware scoring function learns embeddings that respect both semantic relationships and sequential process flows. Finally, link prediction is employed to infer and rank candidate fault causes consistent with the target line's process flow. A case study on automotive pressure sensor assembly lines demonstrates that the proposed method outperforms a state-of-the-art retrieval-augmented generation (RAG) baseline (F1@20 = 0.267) and an RGCN approach (0.400), achieving the best performance (0.523) in fault cause identification. Ablation studies confirm the contributions of both LLM-driven domain conceptualization and process-aware learning. These results indicate that the proposed framework significantly improves the transferability of FMEA knowledge across heterogeneous lines, thereby supporting operators in diagnosing failures more reliably and paving the way for future domain-adaptive LLM applications in smart manufacturing.

Jonas Werheid, Shengjie He, Aymen Gannouni et al.

Quality control of assembly processes is essential in manufacturing to ensure not only the quality of individual components but also their proper integration into the final product. To assist in this matter, automated assembly control using computer vision methods has been widely implemented. However, the costs associated with image acquisition, annotation, and training of computer vision algorithms pose challenges for integration, especially for small- and medium-sized enterprises (SMEs), which often lack the resources for extensive training, data collection, and manual image annotation. Synthetic data offers the potential to reduce manual data collection and labeling. Nevertheless, its practical application in the context of assembly quality remains limited. In this work, we present a novel approach for easily integrable and data-efficient visual assembly control. Our approach leverages simulated scene generation based on computer-aided design (CAD) data and object detection algorithms. The results demonstrate a time-saving pipeline for generating image data in manufacturing environments, achieving a mean Average Precision (mAP@0.5:0.95) up to 99,5% for correctly identifying instances of synthetic planetary gear system components within our simulated training data, and up to 93% when transferred to real-world camera-captured testing data. This research highlights the effectiveness of synthetic data generation within an adaptable pipeline and underscores its potential to support SMEs in implementing resource-efficient visual assembly control solutions.

Mojtaba A. Farahani, Md Irfan Khan, Thorsten Wuest

The convergence of Agentic AI and MAS enables a new paradigm for intelligent decision making in SMS. Traditional MAS architectures emphasize distributed coordination and specialized autonomy, while recent advances in agentic AI driven by LLMs introduce higher order reasoning, planning, and tool orchestration capabilities. This paper presents a hybrid agentic AI and multi agent framework for a Prescriptive Maintenance use case, where LLM based agents provide strategic orchestration and adaptive reasoning, complemented by rule based and SLMs agents performing efficient, domain specific tasks on the edge. The proposed framework adopts a layered architecture that consists of perception, preprocessing, analytics, and optimization layers, coordinated through an LLM Planner Agent that manages workflow decisions and context retention. Specialized agents autonomously handle schema discovery, intelligent feature analysis, model selection, and prescriptive optimization, while a HITL interface ensures transparency and auditability of generated maintenance recommendations. This hybrid design supports dynamic model adaptation, cost efficient maintenance scheduling, and interpretable decision making. An initial proof of concept implementation is validated on two industrial manufacturing datasets. The developed framework is modular and extensible, supporting seamless integration of new agents or domain modules as capabilities evolve. The results demonstrate the system capability to automatically detect schema, adapt preprocessing pipelines, optimize model performance through adaptive intelligence, and generate actionable, prioritized maintenance recommendations. The framework shows promise in achieving improved robustness, scalability, and explainability for RxM in smart manufacturing, bridging the gap between high level agentic reasoning and low level autonomous execution.

Xiaohan Li, Sebastian Pattinson

Defects in extrusion additive manufacturing remain common despite its prevalent use. While numerous AI-driven approaches have been proposed to improve quality assurance, the inherently dynamic nature of the printing process poses persistent challenges. Regardless of how comprehensive the training dataset is, out-of-distribution data remains inevitable. Consequently, deterministic models often struggle to maintain robustness and, in some cases, fail entirely when deployed in new or slightly altered printing environments. This work introduces an agent that dynamically adjusts flow rate and temperature setpoints in real time, optimizing process control while addressing bottlenecks in training efficiency and uncertainty management. It integrates a vision-based uncertainty quantification module with a reinforcement learning controller, using probabilistic distributions to describe printing segments. While the underlying networks are deterministic, these evolving distributions introduce adaptability into the decision-making process. The vision system classifies material extrusion with a certain level of precision, generating corresponding distributions. A deep Q-learning controller interacts with a simulated environment calibrated to the vision system precision, allowing the agent to learn optimal actions while demonstrating appropriate hesitation when necessary. By executing asynchronous actions and applying progressively tightened elliptical reward shaping, the controller develops robust, adaptive control strategies that account for the coupling effects between process parameters. When deployed with zero-shot learning, the agent effectively bridges the sim-to-real gap, correcting mild and severe under- and over-extrusion reliably. Beyond extrusion additive manufacturing, this scalable framework enables practical AI-driven quality assurance across various additive manufacturing processes.

Hao Liu, Fan Jiang, Cheng Li et al.

To address the surface oxidation and porosity defects in the wire-arc directed energy deposition of aluminum alloys, a laser-arc composite cleaning method is proposed for the irregular surface morphology of the deposited layer during the deposition process. The study analyzed the microscopic morphology of the surfaces after various cleaning methods and the changes in surface oxygen content, exploring the complementary rules and enhancement mechanisms of laser and electric arc cleaning in combined cleaning, as well as the optimal process parameter matching strategy. The effectiveness of the cleaning methods was evaluated by testing the porosity of the secondary deposited specimens after cleaning. The uncleaned specimens exhibited a porosity of 2.81%. Laser cleaning reduced this to 2.11%, while arc cleaning achieved 0.13%. The most significant reduction came from composite cleaning, with a minimum porosity of 0.05%. This indicates that composite cleaning can effectively reduce the porosity defects in additive manufacturing.

Cristiano Jesus, Ivan Amorim, Eduardo Pontes et al.

This article presents a time and motion study tool, that migrate from a descriptive approach to one based on data analysis. It presents the engineering design, its application, the analysis of the results obtained in a pilot study, the results of the validation procedure and the theoretical framework in operations management. The tool in question has been proved to be efficient for data collecting at workstations from video processing. It offers the means for intensive data collection, as alternative to sampling-based methods. The tool was developed with the aim of facilitating and speeding up the analysis of approximately 44 hours of video footage. The effort and processing time were significantly reduced by approximately 6 times, and the new method enabled detailed recording of each occurrence not foreseen in the process. This study makes a scientific contribution by proposing a roadmap for the development of the time and motion study.

Bolanle Maryam Akintola, Anil Kumar, Hemakshi Chokshi et al.

Purpose – The rise of the coronavirus disease 2019 (COVID-19) pandemic has enabled researchers and industry professionals to reinvent their strategies for basic economic understanding. Two years after the outbreak of the pandemic, businesses are now trying to adapt to the impact it has brought, hoping to receive support as it did in the past. However, before this feat can be accomplished, it is imperative to understand the recovery hurdles created by the pandemic. This research aims to fill the literature gaps by examining the challenges during recovery within the creative small and medium-sized enterprise (SME) industry, as there are few relevant studies that focus on this field. Design/methodology/approach – Through a methodical bibliometric literature review and network analysis, the paper intends to critically explore relevant recovery challenges within the field while providing answers to the appropriate research questions. A total of 43 articles were selected for an in-depth review. Using the analysis from the selected articles as a guide, a framework was developed to address the recovery challenges alongside the recommended propositions. Findings – The findings from this paper suggest that a lack of synergy among four major categories (governmental, supply chain, organizational and stakeholders) contributes to recovery challenges within the field of research. Originality/value – The review also offers clarification in understanding the current and upcoming trends within the creative industry, SMEs and COVID-19. This paper can thus help researchers, industry practitioners and managers discover and analyze the recovery challenges brought about by the COVID-19 pandemic.

Briony Forsberg, Dr Henry Williams, Prof Bruce MacDonald et al.

This paper develops an approach to classify instances of product failure in a complex textiles manufacturing dataset using explainable techniques. The dataset used in this study was obtained from a New Zealand manufacturer of woollen carpets and rugs. In investigating the trade-off between accuracy and explainability, three different tree-based classification algorithms were evaluated: a Decision Tree and two ensemble methods, Random Forest and XGBoost. Additionally, three feature selection methods were also evaluated: the SelectKBest method, using chi-squared as the scoring function, the Pearson Correlation Coefficient, and the Boruta algorithm. Not surprisingly, the ensemble methods typically produced better results than the Decision Tree model. The Random Forest model yielded the best results overall when combined with the Boruta feature selection technique. Finally, a tree ensemble explaining technique was used to extract rule lists to capture necessary and sufficient conditions for classification by a trained model that could be easily interpreted by a human. Notably, several features that were in the extracted rule lists were statistical features and calculated features that were added to the original dataset. This demonstrates the influence that bringing in additional information during the data preprocessing stages can have on the ultimate model performance.

David Mascareñas, Andre Green, Ashlee Liao et al.

We demonstrate the suitability of high dynamic range, high-speed, neuromorphic event-based, dynamic vision sensors for metallic additive manufacturing and welding for in-process monitoring applications. In-process monitoring to enable quality control of mission critical components produced using metallic additive manufacturing is of high interest. However, the extreme light environment and high speed dynamics of metallic melt pools have made this a difficult environment in which to make measurements. Event-based sensing is an alternative measurement paradigm where data is only transmitted/recorded when a measured quantity exceeds a threshold resolution. The result is that event-based sensors consume less power and less memory/bandwidth, and they operate across a wide range of timescales and dynamic ranges. Event-driven driven imagers stand out from conventional imager technology in that they have a very high dynamic range of approximately 120 dB. Conventional 8 bit imagers only have a dynamic range of about 48 dB. This high dynamic range makes them a good candidate for monitoring manufacturing processes that feature high intensity light sources/generation such as metallic additive manufacturing and welding. In addition event based imagers are able to capture data at timescales on the order of 100 μs, which makes them attractive to capturing fast dynamics in a metallic melt pool. In this work we demonstrate that event-driven imagers have been shown to be able to observe tungsten inert gas (TIG) and laser welding melt pools. The results of this effort suggest that with additional engineering effort, neuromorphic event imagers should be capable of 3D geometry measurements of the melt pool, and anomaly detection/classification/prediction.

Hao Yue, Yingtao Wu, Min Wang et al.

Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve the efficiency and reduce the cost in CMCP, we propose a new framework which considers the collaboration among services with the same functionality. A mathematical evaluation formulation for the service composition and service usage scheme is constructed with the following critical indexes: completion time, cost, and number of selected services. Subsequently, a problem decomposition based genetic algorithm is designed to obtain the optimal service compositions with service usage schemes. A smart clothing customization case is illustrated so as to show the effectiveness and efficiency of the method proposed in this paper. Finally, the results of simulation experiments and comparisons show that these solutions obtained by our method are with the minimum time, a lower cost, and the fewer selected services.

Sudeshna Roy, Thorsten Pöschel

The quality of the powder spread in additive manufacturing devices depends sensitively on the particles' shapes. Here, we study powder spreading for mixtures of spherical and irregularly shaped particles in Polyamide 12 powders. Using DEM simulations, including heat transfer, we find that spherical particles exhibit better flowability. Thus, the particles are deposited far ahead of the spreading blade. In contrast, a large fraction of non-spherical particles hinders the flow. Therefore, the cold particles are deposited near the front of the spreading blade. This results in a temperature drop of the deposited particles near the substrate, which cannot be seen with spherical particles. The particles of both shapes are homogeneously distributed in the deposited powder layer.

Jingren Pan, Longxi Luo, Tao Lu et al.

The wire arc additive manufacturing (WAAM) process often results in geometric inaccuracies in the produced struts, highlighting the need for in-situ monitoring. Industrial cameras struggle to capture accurate images due to interference from arc light and metal radiation. This study introduces a real-time visual reconstruction method that leverages regions of interest (ROI) to enhance profile accuracy. Initially, the melt pool is analyzed and segmented from a series of frames to establish an initial strut profile. This profile then serves as the ROI for segmenting the solidified weld bead regions, yielding a more precise strut profile. Comparative analysis shows that the proposed method achieves a high Intersection Over Union (IOU) score of 0.911 and an Average Symmetric Surface Distance (ASD) error of 0.355 mm, demonstrating both accuracy and robustness. Additionally, the inclination angle and diameter are extracted from the reconstructed profiles.

Markus J. Buehler

Learning from nature has been a quest of humanity for millennia. While this has taken the form of humans assessing natural designs such as bones, butterfly wings, or spider webs, we can now achieve generating designs using advanced computational algorithms. In this paper we report novel biologically inspired designs of diatom structures, enabled using transformer neural networks, using natural language models to learn, process and transfer insights across manifestations. We illustrate a series of novel diatom-based designs and also report a manufactured specimen, created using additive manufacturing. The method applied here could be expanded to focus on other biological design cues, implement a systematic optimization to meet certain design targets, and include a hybrid set of material design sets.

Huizhong Cao, Henrik Söderlund, Mélanie Despeisse et al.

Over the past decade, the value and potential of VR applications in manufacturing have gained significant attention in accordance with the rise of Industry 4.0 and beyond. Its efficacy in layout planning, virtual design reviews, and operator training has been well-established in previous studies. However, many functional requirements and interaction parameters of VR for manufacturing remain ambiguously defined. One area awaiting exploration is spatial recognition and learning, crucial for understanding navigation within the virtual manufacturing system and processing spatial data. This is particularly vital in multi-user VR applications where participants' spatial awareness in the virtual realm significantly influences the efficiency of meetings and design reviews. This paper investigates the interaction parameters of multi-user VR, focusing on interactive positioning maps for virtual factory layout planning and exploring the user interaction design of digital maps as navigation aid. A literature study was conducted in order to establish frequently used technics and interactive maps from the VR gaming industry. Multiple demonstrators of different interactive maps provide a comprehensive A/B test which were implemented into a VR multi-user platform using the Unity game engine. Five different prototypes of interactive maps were tested, evaluated and graded by the 20 participants and 40 validated data streams collected. The most efficient interaction design of interactive maps is thus analyzed and discussed in the study.

Ahmed Missaoui, Cemalettin Ozturk, Barry O'Sullivan et al.

The social context in relation to energy policies, energy supply, and sustainability concerns as well as advances in more energy-efficient technologies is driving a need for a change in the manufacturing sector. The main purpose of this work is to provide a research framework for energy-efficient scheduling (EES) which is a very active research area with more than 500 papers published in the last 10 years. The reason for this interest is mostly due to the economic and environmental impact of considering energy in production scheduling. In this paper, we present a systematic literature review of recent papers in this area, provide a classification of the problems studied, and present an overview of the main aspects and methodologies considered as well as open research challenges.

Hua Zheng, Wei Xie, Keqi Wang et al.

Driven by the key challenges of cell therapy manufacturing, including high complexity, high uncertainty, and very limited process observations, we propose a hybrid model-based reinforcement learning (RL) to efficiently guide process control. We first create a probabilistic knowledge graph (KG) hybrid model characterizing the risk- and science-based understanding of biomanufacturing process mechanisms and quantifying inherent stochasticity, e.g., batch-to-batch variation. It can capture the key features, including nonlinear reactions, nonstationary dynamics, and partially observed state. This hybrid model can leverage existing mechanistic models and facilitate learning from heterogeneous process data. A computational sampling approach is used to generate posterior samples quantifying model uncertainty. Then, we introduce hybrid model-based Bayesian RL, accounting for both inherent stochasticity and model uncertainty, to guide optimal, robust, and interpretable dynamic decision making. Cell therapy manufacturing examples are used to empirically demonstrate that the proposed framework can outperform the classical deterministic mechanistic model assisted process optimization.

Lance Cooley, David Larbalestier, Kathleen Amm

We take a comprehensive look at conductors used in superconducting magnets for the accelerator sector and explore the ramifications of the present marketplace for supply of conductor to future accelerator facilities. While there are thousands of superconductors, many of which have promising properties for applications, we outline the journey a promising material must take to become a magnet conductor that is manufactured at the scale needed for an accelerator facility. Among the few materials that actually reach this scale, Nb$_{3}$Sn is arguably the workhorse conductor for the next generation of accelerators. Yet, a marketplace pull equivalent to the medical imaging magnet industry, which consumes close to 1000 tons of commodity-scale Nb-Ti conductor per year, has not emerged. This aspect greatly complicates the steps that must be taken to assure readiness of manufacturing for the next accelerator facility. Meanwhile, high-temperature superconductors (HTS), which are capable of extremely high fields at low temperature, are advancing rapidly as magnet conductors, and the long horizons of large physics projects could provide time for them to emerge and displace Nb-based materials. We close by examining in more detail the ecosystem that connects accelerator magnet conductors with broader industry applications, in particular areas that are presently in rapid development such as fusion and wind turbines and which would potentially require hundreds of tons of conductor.

Sen Liu, Craig Brice, Xiaoli Zhang

Wire-feed laser additive manufacturing is an emerging fabrication technique capable of highly automated large-scale volume production that can reduce both material waste and overall cost while improving product lead times. Quality assurance is necessary for implementation into critical structural applications. However, the large number of process variables along with the cost associated with traditional trial and error methods makes this difficult. This study investigates a comprehensive quality framework based on learning from experimental data that will enable improved quality control along with consistent microstructural features of the part. Specifically, a comprehensive experimental data across multiple process variables and output characteristics in terms of overall bead quality, geometric shape (i.g. bead height, width, fusion zone depth, etc.), and microstructural features are collected. The predicted process-geometry-microstructure relations are then captured by virtue of data-driven machine learning models. The properties of printed beads are visualized based on an extensive range of processing space within a 3-dimensional contour map. The insights and impacts of process variables on bead morphology, geometric and microstructural features are comprehensively investigated for quality improvement during manufacturing processes.

Markatos Dionysios N., Katsiropoulos Christos V., Tserpes Konstantinos I. et al.

In the present study, a holistic End-of-Life (EoL) Index is introduced to serve as a decision support tool for choosing the optimal recycling process among a number of alternative recycling techniques of CFRP waste. For the choice of the optimal recycling process, quality of the recycled fibers as well as cost and environmental impact of the recycling methods under consideration, are accounted for. Quality is interpreted as the reusability potential of the recycled fibers; that is quantified through the equivalent volume fraction of recycled fibers that balances the mechanical properties of a composite composed of a certain volume fraction of virgin fibers. The proposed Index is offering an estimated balanced score, quantifying a trade-off between the reusability potential of the recycled fibers as well as the cost and the environmental impact of the recycling methods considered.