Hasil untuk "Manufacturing industries"

Y. Kok, X. Tan, Pan Wang et al.

M. Attaran

Duyao Zhang, D. Qiu, M. Gibson et al.

L. Tan, Wei Zhu, K. Zhou

Additive manufacturing (AM) is the process of printing 3D objects in a layer‐by‐layer manner. Polymers and their composites are some of the most widely used materials in modern industries and are of great interest in the field of AM due to their vast potential for various applications, especially in the medical, aerospace, and automotive industries. Many studies have been conducted to develop new polymer materials for AM techniques, which include vat photopolymerization, material jetting, powder bed fusion, material extrusion, binder jetting, and sheet lamination. Although several reviews on the development of polymer materials for AM have been published, most of them only focus on a specific application, process, or type of material. Therefore, this article serves to provide a comprehensive review on the progress in polymer material development for AM techniques. It begins with an introduction to different AM techniques, followed by highlighting the progress of their development. Material requirements, notable advances in newly developed materials and their potential applications are discussed in detail and summarized. This review concludes by identifying the major challenges currently encountered in using AM for polymer materials and providing insights into the valuable opportunities it presents, in hopes of spurring further development in this field.

S.C. Daminabo, S. Goel, S. Grammatikos et al.

Abstract While the developments of additive manufacturing (AM) techniques have been remarkable thus far, they are still significantly limited by the range of printable, functional material systems that meet the requirements of a broad range of industries; including the health care, manufacturing, packaging, aerospace, and automotive industries. Furthermore, with the rising demand for sustainable developments, this review broadly gives the reader a good overview of existing AM techniques; with more focus on the extrusion-based technologies (fused deposition modeling and direct ink writing) due to their scalability, cost efficiency and wider range of material processability. It then goes on to identify the innovative materials and recent research activities that may support the sustainable development of extrusion-based techniques for functional and multifunctional (4D printing) part and product fabrication.

Baolong Yuan, Q. Xiang

Jiewu Leng, Guolei Ruan, P. Jiang et al.

Abstract Sustainability is a pressing need, as well as an engineering challenge, in the modern world. Developing smart technologies is a critical way to ensure that future manufacturing systems are sustainable. Blockchain is a next-generation development of information technology for realizing sustainability in businesses and industries. Much research on blockchain-empowered sustainable manufacturing in Industry 4.0 has been conducted from technical, commercial, organizational, and operational perspectives. This paper surveys how blockchain can overcome potential barriers to achieving sustainability from two perspectives, namely, the manufacturing system perspective and the product lifecycle management perspective. The survey first examines literature on these two perspectives, following which the state of research in blockchain-empowered sustainable manufacturing is presented, which sheds new light on urgent issues as part of the UN's Sustainable Development Goals. We found that blockchain-empowered transformation of a sustainable manufacturing paradigm is still in an early stage of the hype phase, proceeding toward full adoption. The survey ends with a discussion of challenges regarding techniques, social barriers, standards, and regulations with respect to blockchain-empowered manufacturing applications. The paper concludes with a discussion of challenges and social barriers that blockchain technology must overcome to demonstrate its sustainability in industrial and business spheres.

Baicun Wang, F. Tao, Xudong Fang et al.

Abstract The application of intelligence to manufacturing has emerged as a compelling topic for researchers and industries around the world. However, different terminologies, namely smart manufacturing (SM) and intelligent manufacturing (IM), have been applied to what may be broadly characterized as a similar paradigm by some researchers and practitioners. While SM and IM are similar, they are not identical. From an evolutionary perspective, there has been little consideration on whether the definition, thought, connotation, and technical development of the concepts of SM or IM are consistent in the literature. To address this gap, the work performs a qualitative and quantitative investigation of research literature to systematically compare inherent differences of SM and IM and clarify the relationship between SM and IM. A bibliometric analysis of publication sources, annual publication numbers, keywords frequency, and top regions of research and development establishes the scope and trends of the currently presented research. Critical topics discussed include origin, definitions, evolutionary path, and key technologies of SM and IM. The implementation architecture, standards, and national focus are also discussed. In this work, a basis to understand SM and IM is provided, which is increasingly important because the trend to merge both terminologies rises in Industry 4.0 as intelligence is being rapidly applied to modern manufacturing and human–cyber–physical systems.

Saad Saleh Alghamdi, S. John, N. Roy Choudhury et al.

The use of additive manufacturing (AM) has moved well beyond prototyping and has been established as a highly versatile manufacturing method with demonstrated potential to completely transform traditional manufacturing in the future. In this paper, a comprehensive review and critical analyses of the recent advances and achievements in the field of different AM processes for polymers, their composites and nanocomposites, elastomers and multi materials, shape memory polymers and thermo-responsive materials are presented. Moreover, their applications in different fields such as bio-medical, electronics, textiles, and aerospace industries are also discussed. We conclude the article with an account of further research needs and future perspectives of AM process with polymeric materials.

Chunyang Xia, Z. Pan, Joseph Polden et al.

Abstract Wire arc additive manufacturing technology (WAAM) has become a very promising alternative to high-value large metal components in many manufacturing industries. Due to its long process cycle time and arc-based deposition, defect monitoring, process stability and control are critical for the WAAM system to be used in the industry. Although major progress has been made in process development, path slicing and programming, and material analysis, a comprehensive process monitoring, and control system are yet to be developed. This paper aims to provide an in-depth review of sensing and control design suitable for a WAAM system, including technologies developed for the generic Arc Welding process, the Wire Arc Additive Manufacturing process and laser Additive Manufacturing. Particular focus is given to the integration of sensor-based feedback control, and how they could be implemented into the WAAM process to improve its accuracy, reliability, and efficiency. The paper concludes by proposing a framework for sensor-based monitoring and control system for the GMAW based WAAM process. This framework provides a blueprint for the monitoring and control strategies during the WAAM process and aims to identify and reduce defects using information fusion techniques.

Sachin Kumar, T. Gopi, N. Harikeerthana et al.

For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

Sofiene Lassoued, Laxmikant Shrikant Bahetic, Nathalie Weiß-Borkowskib et al.

Flexible Manufacturing Systems (FMS) are pivotal in optimizing production processes in today's rapidly evolving manufacturing landscape. This paper advances the traditional job shop scheduling problem by incorporating additional complexities through the simultaneous integration of automated guided vehicles (AGVs) and tool-sharing systems. We propose a novel approach that combines Colored-Timed Petri Nets (CTPNs) with actor-critic model-based reinforcement learning (MBRL), effectively addressing the multifaceted challenges associated with FMS. CTPNs provide a formal modeling structure and dynamic action masking, significantly reducing the action search space, while MBRL ensures adaptability to changing environments through the learned policy. Leveraging the advantages of MBRL, we incorporate a lookahead strategy for optimal positioning of AGVs, improving operational efficiency. Our approach was evaluated on small-sized public benchmarks and a newly developed large-scale benchmark inspired by the Taillard benchmark. The results show that our approach matches traditional methods on smaller instances and outperforms them on larger ones in terms of makespan while achieving a tenfold reduction in computation time. To ensure reproducibility, we propose a gym-compatible environment and an instance generator. Additionally, an ablation study evaluates the contribution of each framework component to its overall performance.

Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson et al.

Modern manufacturing environments demand real-time, trustworthy, and interpretable root-cause insights to sustain productivity and quality. Traditional analytics pipelines often treat anomaly detection, causal inference, and root-cause analysis as isolated stages, limiting scalability and explainability. In this work, we present CausalPulse, an industry-grade multi-agent copilot that automates causal diagnostics in smart manufacturing. It unifies anomaly detection, causal discovery, and reasoning through a neurosymbolic architecture built on standardized agentic protocols. CausalPulse is being deployed in a Robert Bosch manufacturing plant, integrating seamlessly with existing monitoring workflows and supporting real-time operation at production scale. Evaluations on both public (Future Factories) and proprietary (Planar Sensor Element) datasets show high reliability, achieving overall success rates of 98.0% and 98.73%. Per-criterion success rates reached 98.75% for planning and tool use, 97.3% for self-reflection, and 99.2% for collaboration. Runtime experiments report end-to-end latency of 50-60s per diagnostic workflow with near-linear scalability (R^2=0.97), confirming real-time readiness. Comparison with existing industrial copilots highlights distinct advantages in modularity, extensibility, and deployment maturity. These results demonstrate how CausalPulse's modular, human-in-the-loop design enables reliable, interpretable, and production-ready automation for next-generation manufacturing.

Yan WU, Suke WU, Zhiqiang ZHAO

ObjectiveThe low-altitude economy covers low-altitude manufacturing, flight operations, support services and comprehensive service industries, and has the characteristics of spatially three-dimensional, regional dependence, digital ecology, industrial integration and radiation driving. With the continuous development of digitization and informatization, these characteristics increasingly affect the interaction between low-altitude activities and urban and natural environments, which provides new ideas for the development of landscape architecture, and the coordinated development of the two is an important issue in the transformation of landscape architecture.MethodsThe knowledge map method was used to analyze the research status of environmental low-altitude economy at home and abroad. By combing and reading relevant literature at home and abroad, the research trends of low-altitude economy in logistics, aviation, ecological monitoring and other fields were systematically analyzed by reviewing literature research, case studies and comparative analysis. ResultsBy analyzing the domestic and foreign research, the internal mechanism of the cross research between landscape architecture and low-altitude economy is revealed four aspects. In the aspect of technology application, low-altitude aircraft tend to be transformed from perception tools to "design intelligence" which can guide ecological design and space construction. In the aspect of spatial planning, landscape architecture will realize the spatial transformation from plane extension to three-dimensional reconstruction under the development of low-altitude economy. In the aspect of ecological impact, the research focus has changed from identifying environmental risks by using low-altitude facilities to systematic assessment and control of ecology. In the aspect of humanistic experience, the combination of low-altitude economy and cultural narrative has further stimulated the vitality of landscape architecture discipline. It is found that under the influence of low-altitude economy, the development of landscape architecture faces some new problems, such as imperfect policies and regulations, insufficient adaptability of spatial planning system, systematic lack of ecological protection, technical bottleneck restriction, homogenization dilemma of cultural and tourism integration, including lack of unified standards for low-altitude facility design, traditional two-dimensional planning being difficult to meet the needs of air-space coordination, ecological destruction caused by noise and habitat disturbance, lack of ecological protection system, etc. There are technical bottlenecks in data processing and flight stability; homogenization of tourism products, focusing on tourism over cultural innovation. In order to promote the coordinated development of the two, this paper puts forward the implementation path of the integration of low-altitude economy and landscape architecture: to ensure the adaptation of policies and regulations, to improve policies and regulations and technical standards, to formulate low-altitude greening design and ecological evaluation standards, and to lay a foundation for the integration of landscape architecture and low-altitude economy; Conduct spatial value evaluation, clarify the use, development subject and income distribution mechanism of each low-altitude area, promote the market-oriented operation of public resources, and use unmanned aerial vehicles to carry out ecological background analysis and evaluate the ecological carrying capacity of low-altitude activities; Conduct functional compound planning, make landscape architecture break through the limitation of traditional ground perspective, bring low-altitude airspace into the vertical space system of landscape architecture, add low-altitude related infrastructure in the garden, and realize efficient coordinated utilization of airspace and ground resources; In ecological service monitoring, we should increase the investment in R&D of UAV technology, low-altitude aircraft noise reduction technology, flight safety guarantee technology, etc., improve the intelligent level of landscape architecture monitoring and management, and establish a new ecological assessment mode; Promote the integrated development of culture and tourism, break through the homogenization dilemma from three dimensions of cultural empowerment, spatial differentiation and experience depth, and integrate garden cultural elements into low-altitude experience links, to improve the overall operation effect. ConclusionBased on the research, we have drawn conclusions in three aspects. 1) The global low-altitude economy industry will become the next development hotspot, and it should accelerate the integration with transportation logistics, cultural tourism and other formats, expand more application scenarios, promote the integration of landscape architecture and related industries, and activate the new vitality of landscape architecture. 2) Green space and parks in cities will become important carriers for low-altitude transportation in the future, and planning and design of low-altitude composite public space will be the focus of research, and research will move from two-dimensional garden aesthetic space to three-dimensional traffic spatial pattern. 3) The development of unmanned aerial vehicle and related technologies provide refined intelligent solutions for intelligent garden management, and low-altitude monitoring data provide new tools for ecological value assessments of gardens, significantly improve ecological monitoring and management levels.

Vaishnavi Vaishnavi, Gopal Krishna Roy

Abstract This paper examines whether spatial variation across states in the severity of corruption, as reported by manufacturing firms, is associated with the decisions of India’s organised manufacturing factories to adopt greener production measures by investing in pollution control equipment. We collate data from two sources: the Indian government’s principal source of industrial statistics, namely the Annual Survey of Industries 2022-23, for factory-level information, and the World Bank Enterprise Survey for India 2022, to construct a composite index that measures the state-wise variation in corruption based on indicators of firms’ experiences and perceptions of corruption as an obstacle using Principal Component Analysis. We model firms’ decision to invest in pollution control equipment as a two-step process: first, deciding whether to invest, and second, determining the extent of expenditure if they do. We correct the selection bias using the Heckman Selection model. Our results provide evidence supporting the hypothesis that factories located in states with higher levels of corruption tend to spend less on pollution control equipment in both high-polluting and less-polluting industries. Among firm-specific factors, we find that firm size, ISO 14000 series certification, and R&D activity drive both the decision and intensity of pollution-control equipment expenditure. Exporting increases the likelihood of spending on pollution control equipment, while other production subsidies lead to an increased level of expenditure on such equipment in polluting industries. The paper contributes to the expanding body of research on institutional quality and the environmental sustainability of production processes, viewed through a regional perspective in a developing economy with a strong manufacturing sector.

Jorge Manuel Mercado-Colmenero, Daniel Diaz - Perete, Miguel Angel Rubio- Paramio et al.

The present research is developed into the realm of industrial design engineering and additive manufacturing by introducing a parametric design model and adaptive mechanical analysis for a new lattice structure, with a focus on 3D additive manufacturing of complex parts. Focusing on the land-scape of complex parts additive manufacturing, this research proposes geometric parameterization, mechanical adaptive sizing, and numerical validation of a novel lattice structure to optimize the final printed part volume and mass, as well as its structural rigidity. The topology of the lattice structures exhibited pyramidal geometry. Complete parameterization of the lattice structure ensures that the known geometric parameters adjust to defined restrictions, enabling dynamic adaptability based on its load states and boundary conditions, thereby enhancing its mechanical performance. The core methodology integrates analytical automation with mechanical analysis by employing a model based in two-dimensional beam elements. The dimensioning of the lattice structure is analyzed using rigidity models of its sub-elements, providing an evaluation of its global structural behavior after applying the superposition principle. Numerical validation was performed to validate the proposed analytical model. This step ensures that the analytical model defined for dimensioning the lattice structure adjusts to its real mechanical behavior and allows its validation. The present manuscript aims to advance additive manufacturing methodologies by offering a systematic and adaptive approach to lattice structure design. Parametric and adaptive techniques foster new industrial design engineering methods, enabling the dynamic tailoring of lattice structures to meet their mechanical demands and enhance their overall efficiency and performance.

Peter Pak, Amir Barati Farimani

In this work we investigate the ability of large language models to predict additive manufacturing defect regimes given a set of process parameter inputs. For this task we utilize a process parameter defect dataset to fine-tune a collection of models, titled AdditiveLLM, for the purpose of predicting potential defect regimes including Keyholing, Lack of Fusion, and Balling. We compare different methods of input formatting in order to gauge the model's performance to correctly predict defect regimes on our sparse Baseline dataset and our natural language Prompt dataset. The model displays robust predictive capability, achieving an accuracy of 93\% when asked to provide the defect regimes associated with a set of process parameters. The incorporation of natural language input further simplifies the task of process parameters selection, enabling users to identify optimal settings specific to their build.

Shichen Li, Chenhui Shao

Fruit drying is widely used in food manufacturing to reduce product moisture, ensure product safety, and extend product shelf life. Accurately predicting final moisture content (MC) is critically needed for quality control of drying processes. State-of-the-art methods can build deterministic relationships between process parameters and MC, but cannot adequately account for inherent process variabilities that are ubiquitous in fruit drying. To address this gap, this paper presents a novel multi-modal data fusion framework to effectively fuse two modalities of data: tabular data (process parameters) and high-dimensional image data (images of dried apple slices) to enable accurate MC prediction. The proposed modeling architecture permits flexible adjustment of information portion from tabular and image data modalities. Experimental validation shows that the multi-modal approach improves predictive accuracy substantially compared to state-of-the-art methods. The proposed method reduces root-mean-squared errors by 19.3%, 24.2%, and 15.2% over tabular-only, image-only, and standard tabular-image fusion models, respectively. Furthermore, it is demonstrated that our method is robust in varied tabular-image ratios and capable of effectively capturing inherent small-scale process variabilities. The proposed framework is extensible to a variety of other drying technologies.

Rui Han, Chenwei Wang, Yuzhong Wang et al.

Abstract Additive Manufacturing (AM) technology has gained widespread application across various industries due to its capability to directly produce products from computer-aided design models. Among AM techniques, the Electron Beam Selective Melting (EBSM) process has attracted significant attention, particularly in aerospace and automotive industries, owing to its high precision, speed, and excellent material properties. However, various defects, especially internal defects that inevitably arise during the manufacturing process, significantly limit the performance of EBSM parts. In this study, X-ray computed tomography (CT) was utilized to scan EBSM parts, and cross-sectional images were employed to train several state-of-the-art modern object detection models for evaluating their effectiveness in detecting internal defects. Sparse R-CNN demonstrated the best overall performance, while the YOLO series excelled in specific metrics. To further capitalize on the strengths of different detection models, a model ensemble approach, Selective Box Fusion (SBF) is proposed. This approach employs voting and weighted fusion of detection boxes to mitigate errors inherent in individual models. Experimental results show that the SBF ensemble method effectively integrates the advantages of multiple detection models, leading to improvements across various evaluation metrics compared to individual models and other ensemble methods.

Ruohan Yu, Yuan Chi, Richard Fuchs et al.

Abstract Conventional display technologies rely on rigid architectures, limiting their adaptability for reconfigurable systems. Plasma discharge, as a field‐driven excitation method, offers great opportunities for visual interfaces, yet integrating it into controllable and adaptable color display platforms remains challenging. Here, configurable and adaptable electroluminescent platforms based on the plasma discharge of phosphor‐coated liquid metal marbles based on eutectic gallium indium liquid metal droplets are presented. Electroluminescent phosphors emitting the red, green, and blue primary colors are used as a functionalizing coating for the droplets. Mixing different types of phosphor particles at controllable ratios fine tunes the electroluminescent color emitted from individual air gaps between adjacent liquid metal marbles. Such a particle‐mixing‐enabled additive color mixing strategy enables bright color emission across the whole visible spectrum and plasma‐discharge‐based pixelated multicolor display of diverse reconfigurable patterns. This low‐cost and easily reconfigurable liquid metal marble platform offers a multicolor display technique for future displays.