Hasil untuk "Manufacturing industries"

Justin R. Pierce, Peter K. Schott

This paper finds a link between the sharp drop in U.S. manufacturing employment beginning in 2001 and a change in U.S. trade policy that eliminated potential tariff increases on Chinese imports. Industries where the threat of tariff hikes declines the most experience more severe employment losses along with larger increases in the value of imports from China and the number of firms engaged in China-U.S. trade. These results are robust to other potential explanations of the employment loss, and we show that the U.S. employment trends differ from those in the EU, where there was no change in policy.

K. Govindan, Mathiyazhagan Kaliyan, Devika Kannan et al.

Thomas Spears, S. Gold

Additive manufacturing and specifically metal selective laser melting (SLM) processes are rapidly being industrialized. In order for this technology to see more widespread use as a production modality, especially in heavily regulated industries such as aerospace and medical device manufacturing, there is a need for robust process monitoring and control capabilities to be developed that reduce process variation and ensure quality. The current state of the art of such process monitoring technology is reviewed in this paper. The SLM process itself presents significant challenges as over 50 different process input variables impact the characteristics of the finished part. Understanding the impact of feed powder characteristics remains a challenge. Though many powder characterization techniques have been developed, there is a need for standardization of methods most relevant to additive manufacturing. In-process sensing technologies have primarily focused on monitoring melt pool signatures, either from a Lagrangian reference frame that follows the focal point of the laser or from a fixed Eulerian reference frame. Correlations between process measurements, process parameter settings, and quality metrics to date have been primarily qualitative. Some simple, first-generation process control strategies have also been demonstrated based on these measures. There remains a need for connecting process measurements to process models to enable robust model-based control.

D. Mourtzis, Ekaterini Vlachou, Nikolaos T. Milas

Abstract The radical evolution of internet into a network of interconnected objects that create a smart environment is characterized by the term Internet of Things (IoT). The adoption of IoT in manufacturing enables the transition of tradition manufacturing systems into modern digitalized ones, generating significant economic opportunities through industries re-shaping. Industrial IoT empowers the modern companies to adopt new data-driven strategies and handle the global competitive pressure more easily. However, the adoption of IoT, increases the total volume of the generated data transforming the industrial data into industrial Big Data. The work demonstrated in this paper presents how the adoption of IoT in manufacturing, considering sensory systems and mobile devices, will generate industrial Big Data. Moreover, a developed IoT application is presented showing how real industrial data can be generated leading to Industrial Big Data. The proposed methodology is validated in a real life case study from a mould-making industry.

T. Melton

F. Castellacci

J. Frketic, Tarik J. Dickens, S. Ramakrishnan

Bharathy Jacob, John Rozario Jegaraj, Nithyanandan Kanagaraj

Laser-based manufacturing has emerged as a promising alternative to conventional thermal and mechanical processing owing to its precision, versatility, and ability to work across diverse materials. In particular, tailoring the spatial intensity distribution of laser beams on the fly is pivotal for ensuring keyhole stability, minimizing defects, and enhancing processing quality. To address this need, we propose a multifunctional optical platform designed through a Diffractive Neural Network that provides wavelength adaptability for three industrially relevant wavelengths - 915 nm, 1064 nm, and 1550 nm - while dynamically generating distinct beam profiles at specified propagation planes. The proposed platform not only enables static beam shaping but also supports dynamic beam engineering, including programmable sequencing between profiles, which is highly desirable for optimal manufacturing solutions. With its multifunctionality and adaptability, the DNN-based architecture establishes a transformative pathway for next-generation laser manufacturing, aligning with the industrial revolution while unlocking opportunities in biomedical optics, free-space communications, and sensing.

Mariam Elgamal, Abdulrahman Mahmoud, Gu-Yeon Wei et al.

The electronics and semiconductor industry is a prominent consumer of per- and poly-fluoroalkyl substances (PFAS), also known as forever chemicals. PFAS are persistent in the environment and can bioaccumulate to ecological and human toxic levels. Computer designers have an opportunity to reduce the use of PFAS in semiconductors and electronics manufacturing, including integrated circuits (IC), batteries, displays, etc., which currently account for a staggering 10% of the total PFAS fluoropolymers usage in Europe alone. In this paper, we present a framework where we (1) quantify the environmental impact of PFAS in computing systems manufacturing with granular consideration of the metal layer stack and patterning complexities in IC manufacturing at the design phase, (2) identify contending trends between embodied carbon (carbon footprint due to hardware manufacturing) versus PFAS. For example, manufacturing an IC at a 7 nm technology node using EUV lithography uses 18% less PFAS-containing layers, compared to manufacturing the same IC at a 7 nm technology node using DUV immersion lithography (instead of EUV) unlike embodied carbon trends, and (3) conduct case studies to illustrate how to optimize and trade-off designs with lower PFAS, while meeting power-performance-area constraints. We show that optimizing designs to use less back-end-of-line (BEOL) metal stack layers can save 1.7$\times$ PFAS-containing layers in systolic arrays.

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.

Christopher Leet, Aidan Sciortino, Sven Koenig

Modern automated factories increasingly run manufacturing procedures using a matrix of programmable machines, such as 3D printers, interconnected by a programmable transport system, such as a fleet of tabletop robots. To embed a manufacturing procedure into a smart factory, an operator must: (a) assign each of its processes to a machine and (b) specify how agents should transport parts between machines. The problem of embedding a manufacturing process into a smart factory is termed the Smart Factory Embedding (SFE) problem. State-of-the-art SFE solvers can only scale to factories containing a couple dozen machines. Modern smart factories, however, may contain hundreds of machines. We fill this hole by introducing the first highly scalable solution to the SFE, TS-ACES, the Traffic System based Anytime Cyclic Embedding Solver. We show that TS-ACES is complete and can scale to SFE instances based on real industrial scenarios with more than a hundred machines.

Sathish Krishna Anumula, Sivaramkumar Ponnarangan, Faizal Nujumudeen et al.

A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a suggested way to evaluate intelligent robotic systems in industry, we give an overview of how their use impacts productivity, safety and operational costs. Experience and case studies confirm the benefits this area brings and the problems that have yet to be solved. The findings indicate that intelligent robotics involves more than a technology change; it introduces important new methods in engineering.

Yuanze Li, Shihao Yuan, Haolin Wang et al.

Although recent methods have tried to introduce large multimodal models (LMMs) into industrial anomaly detection (IAD), their generalization in the IAD field is far inferior to that for general purposes. We summarize the main reasons for this gap into two aspects. On one hand, general-purpose LMMs lack cognition of defects in the visual modality, thereby failing to sufficiently focus on defect areas. Therefore, we propose to modify the AnyRes structure of the LLaVA model, providing the potential anomalous areas identified by existing IAD models to the LMMs. On the other hand, existing methods mainly focus on identifying defects by learning defect patterns or comparing with normal samples, yet they fall short of understanding the causes of these defects. Considering that the generation of defects is closely related to the manufacturing process, we propose a manufacturing-driven IAD paradigm. An instruction-tuning dataset for IAD (InstructIAD) and a data organization approach for Chain-of-Thought with manufacturing (CoT-M) are designed to leverage the manufacturing process for IAD. Based on the above two modifications, we present Triad, a novel LMM-based method incorporating an expert-guided region-of-interest tokenizer and manufacturing process for industrial anomaly detection. Extensive experiments show that our Triad not only demonstrates competitive performance against current LMMs but also achieves further improved accuracy when equipped with manufacturing processes. Source code, training data, and pre-trained models will be publicly available at https://github.com/tzjtatata/Triad.

Juma M. Matindana, Francis D. Sinkamba, Mussa I. Mgwatu

ABSTRACT With growing competition among manufacturing industries in Tanzania, there is a need to adopt lean manufacturing (LM). The adoption of LM in Tanzania and other developing countries is low. This study identifies drivers for LM implementation in the country. Survey and purposive sampling were used to collect responses from 243 manufacturing industries in Tanzania. Partial least squares—structural equation modeling (PLS‐SEM) and relative importance index (RII) were used to determine and rank the drivers for LM. PLS‐SEM involved the development of a measurement and structural model for drivers of LM adoption using Smart PLS 4. Model fit indices on the effects of drivers on the adoption of LM, such as the normed fit index (NFI), were ≥ 0.7, demonstrating the model was good. External and policy drivers positively impact the adoption of LM in Tanzania. The drivers are to increase capacity to fulfill demands, establish standard operating procedures, balance workload on different workstations, reduce lead time, and improve process control. Identifying the drivers enhances competition among local industries, which, in turn, improves the sector's contribution to the country's gross domestic product. Furthermore, it assists policymakers in setting appropriate policies and strategies for promoting industrial growth in Tanzania.

Shaofeng Jia, Yuchen Xu, Jun Lin et al.

With the continued advancement of deep electrification across various industries, the demand for higher power density in electric machines is steadily increasing. However, realizing high power density remains a significant technical challenge and has become a major bottleneck in machine development. The design of such machines is inherently constrained by the strong coupling among electromagnetic (EM), thermal, and mechanical domains, while systematic analyses of these challenges remain insufficient. This paper clarifies the interdependent relationships among these domains during the machine design process. It reviews key enabling strategies, including machine design based on advanced electromagnetic theory, innovative thermal management techniques, cutting-edge material advancements, and state-of-the-art manufacturing technologies, that collectively enhance the performance and feasibility of high power density machines (HPDMs). The insights provided aim to support the development of next-generation machine systems with higher power density, compact size, and robust, sustainable performance across a wide range of industrial and technological applications.

S. S. Krasnykh

The purpose of the study is spatial modeling of the impact of developed advanced production technologies on the volume of mineral extraction, manufacturing industries, and power generation in Russian regions.   The SAR and SEM models, as well as global and local Moran index, have been applied. The volumes of developed advanced production technologies, mining, manufacturing industries, and electricity generation for 2022 have been considered as the variables under study. It has been found that the processes of advanced production technologies development affect the manufacturing industries and electricity generation. The spatial autocorrelation coefficients analysis showed that in the processes of developing advanced production technologies, mining, manufacturing, and electricity generation regions form clusters, and there is a positive spatial autocorrelation. Russian regions have been classified using the local Moran index, which allowed us to identify growth poles, high values clusters, territories influenced by these clusters, and territories with a low value of developed advanced production technologies, manufacturing industries, and electricity generation. The negative value of the spatial autocorrelation coefficient in the SAR model showed that an increase in the amount of developed advanced manufacturing technologies in one region leads to a decrease in their level in another one. It indicates spatial heterogeneity of the processes of advanced technologies development in the regions.

Zuzanna Domżalska, Ewa Jakubczyk

Three-dimensional printing, or additive manufacturing, produces three-dimensional objects using a digital model. Its utilisation has been observed across various industries, including the food industry. Technology offers a wide range of possibilities in this field, including creating innovative products with unique compositions, shapes, and textures. A significant challenge in 3D printing is the development of the optimal ink composition. These inks must possess the appropriate rheology and texture for printing and meet nutritional and sensory requirements. The rheological properties of inks play a pivotal role in the printing process, influencing the formation of stable structures. This article comprehensively characterises food inks, distinguishing two primary categories and their respective subgroups. The first category encompasses non-natively extrudable inks, including plant-based inks derived from fruits and vegetables and meat-based inks. The second category comprises natively extrudable inks, encompassing dairy-based, hydrogel-based, and confectionary-based inks. The product properties of rheology, texture, fidelity, and printing stability are then discussed. Finally, the innovative use of food inks is shown.

M. Sharp, Ronay Ak, Thomas Hedberg, Jr.

Machine learning (ML) (a subset of artificial intelligence that focuses on autonomous computer knowledge gain) is actively being used across many domains, such as entertainment, commerce, and increasingly in industrial settings. The wide applicability and low barriers for development of these algorithms are allowing for innovations, once thought unattainable, to be realized in an ever more digital world. As these innovations continue across industries, the manufacturing industry has also begun to gain benefits. With the current push for Smart Manufacturing and Industrie 4.0, ML for manufacturing is experiencing unprecedented levels of interest; but how much is industry actually using these highly-publicized techniques? This paper sorts through a decade of manufacturing publications to quantify the amount of effort being put towards advancing ML in manufacturing. This work identifies both prominent areas of ML use, and popular algorithms. This also allows us to highlight any gaps, or areas where ML could play a vital role. To maximize the search space utilization of this investigation, ML based Natural Language Processing (NLP) techniques were employed to rapidly sort through a vast corpus of engineering documents to identify key areas of research and application, as well as uncover documents most pertinent to this survey. The salient outcome of this research is the presentation of current focus areas and gaps in ML applications to the manufacturing industry, with particular emphasis on cross domain knowledge utilization. A full detailing of methods and findings is presented.

Baolong Yuan, Shenggang Ren, Xiaohong Chen

Nitol Saha, Philip Samaha, Ramy Harik

This research proposes a blockchain-based data visualization framework integrated with VR to get manufacturing insights. This framework is implemented at the testbed of the Future Factories Lab at the University of South Carolina. The proposed system aims to enhance understanding, analysis, and decision-making processes by immersing users in a VR environment where complex manufacturing data stored using blockchain is translated into intuitive and interactive representations. The project focuses on two main components: blockchain and VR. Hyperledger Fabric is employed to establish a blockchain network, recording data from the Future Factories testbed. This system captures information from various sources, such as potentiometers on robot grippers to measure grip positioning, load cells to gauge pressure, emergency stop buttons, temperature, speed, and vibration sensors on the conveyors. Whenever predefined conditions are met, pertinent data, including sensor ID, timestamp, value, cause, and importance, is securely recorded in the blockchain, signaling the occurrence of a defect within the cell. Data retrieved from the blockchain system is accessed through 'GET' API requests. A VR application is developed using a cross-platform Unity game engine to visualize the data retrieved from the blockchain database. Meta Quest 3 is used as the targeted Head Mounted VR device. The VR application has two C# scripts: one script to query blockchain data using 'GET' API calls and another script converts the JSON object to text data to visualize in the VR system. The proposed system leverages blockchain technology and VR visualization to deliver immersive, actionable insights using secure data transmission. By embracing the proposed framework, manufacturers can unlock new potential for efficiency, sustainability, and resilience in today's increasingly complex and interconnected manufacturing workplace.