Hasil untuk "Manufacturing industries"

D. Rodrik

M. B. Ali, R. Saidur, M. S. Hossain

A. Bernard, J. Jensen, Robert Z. Lawrence

Eli Berman, John Bound, Z. Griliches

M. Porter

P. Jacobs

Carin Labuschagne, A. Brent, Ron P.G. Van Erck

M. Swink, R. Narasimhan, Cynthia Wang

Marco Simon, Felix Schoeppenthau, Richard Kuntschke et al.

One way to solve the challenge of small and medium-sized enterprise (SME) manufacturers of acquiring sufficient orders is by joining digital Manufacturing-as-a-Service (MaaS) platforms for on-demand manufacturing. However, joining such platforms brings about new challenges such as efficient quoting handling in the face of potentially low success rates and the need for high production quality for low lot sizes. Automating the complete interaction between manufacturers and MaaS platforms, from registering the manufacturer and its capabilities to handling incoming requests and managing offers, orders, and production quality reporting, helps to overcome these challenges. Thus, the increased number of requests can be handled efficiently, and the production quality can be maintained at a high level even for low lot sizes. This paper presents an architecture for automating the interaction and functional building blocks between manufacturers and MaaS platforms, along with a prototype implementation and evaluation of its effectiveness in addressing the challenges SME manufacturers are faced with.

Yifei Li, Ruizhe Fu, Huihang Liu et al.

As the demand for mass customization increases, manufacturing systems must become more flexible and adaptable to produce personalized products efficiently. Additive manufacturing (AM) enhances production adaptability by enabling on-demand fabrication of customized components directly from digital models, but its flexibility remains constrained by fixed equipment layouts. Integrating mobile robots addresses this limitation by allowing manufacturing resources to move and adapt to changing production requirements. Mobile AM Robots (MAMbots) combine AM with mobile robotics to produce and transport components within dynamic manufacturing environments. However, the dynamic manufacturing environments introduce challenges for MAMbots. Disturbances such as obstacles and uneven terrain can disrupt navigation stability, which in turn affects printing accuracy and surface quality. This work proposes a universal mobile printing-and-delivery platform that couples navigation and material deposition, addressing the limitations of earlier frameworks that treated these processes separately. A real-time control framework is developed to plan and control the robot's navigation, ensuring safe motion, obstacle avoidance, and path stability while maintaining print quality. The closed-loop integration of sensing, mobility, and manufacturing provides real-time feedback for motion and process control, enabling MAMbots to make autonomous decisions in dynamic environments. The framework is validated through simulations and real-world experiments that test its adaptability to trajectory variations and external disturbances. Coupled navigation and printing together enable MAMbots to plan safe, adaptive trajectories, improving flexibility and adaptability in manufacturing.

Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng et al.

Manufacturing automation in process planning, inspection planning, and digital-thread integration depends on a unified specification that binds the geometric features of a 3D CAD model to the geometric dimensioning and tolerancing (GD&T) callouts, datum definitions, and surface requirements carried by the corresponding 2D engineering drawing. Although Model-Based Definition (MBD) allows such specifications to be embedded directly in 3D models, 2D drawings remain the primary carrier of manufacturing intent in automotive, aerospace, shipbuilding, and heavy-machinery industries. Correctly linking drawing annotations to the corresponding 3D features is difficult because of contextual ambiguity, repeated feature patterns, and the need for transparent and traceable decisions. This paper presents a deterministic-first, context-aware framework that maps 2D drawing entities to 3D CAD features to produce a unified manufacturing specification. Drawing callouts are first semantically enriched and then scored against candidate features using an interpretable metric that combines type compatibility, tolerance-aware dimensional agreement, and conservative context consistency, along with engineering-domain heuristics. When deterministic scoring cannot resolve an ambiguity, the system escalates to multimodal and constrained large-language-model reasoning, followed by a single human-in-the-loop (HITL) review step. Experiments on 20 real CAD-drawing pairs achieve a mean precision of 83.67%, recall of 90.46%, and F1 score of 86.29%. An ablation study shows that each pipeline component contributes to overall accuracy, with the full system outperforming all reduced variants. By prioritizing deterministic rules, clear decision tracking, and retaining unresolved cases for human review, the framework provides a practical foundation for downstream manufacturing automation in real-world industrial environments.

Yunlei Sun, Peng Dai, Yangxingyue Liu et al.

Engineering drawings are fundamental to industries such as oil and gas, construction, and manufacturing. However, current practices relying on manual design or rigid parametric templates often suffer from inefficiency and layout inconsistencies. To address these issues, the layout task is formulated as the Orthogonal Rectangle Packing Problem with Multiple Configurations and Complex Constraints (ORPPMC). The Deep Reinforcement Learning for Multi-Configuration Drawing Layout (DRL-MCDL) framework is proposed, which integrates the Pointer Network for Drawing Element Sequencing (PN-DES) with the Target-Type-Matching-based Multi-Pattern Positioning Strategy (TTM-MPPS). Within this framework, PN-DES employs deep reinforcement learning and feature fusion to combine element attributes with layout configurations for optimal sequence inference, while TTM-MPPS performs precise positioning in accordance with industrial rules to ensure strict adherence to aesthetic requirements. Ablation experiments validate the contribution of each module. Experimental results on real-world engineering drawings demonstrate that DRL-MCDL achieves a Feasibility Rate (FR) exceeding 98.5% on standard instances (12–40 elements), significantly outperforming traditional methods. Furthermore, it maintains a high inference efficiency with an Average Time (AT) of less than 0.3 s, striking an optimal balance between layout quality and computational speed.

Beiming Liu, Zhizhuo Cui, Siteng Hu et al.

Aerospace manufacturing demands exceptionally high precision in technical parameters. The remarkable performance of Large Language Models (LLMs), such as GPT-4 and QWen, in Natural Language Processing has sparked industry interest in their application to tasks including process design, material selection, and tool information retrieval. However, LLMs are prone to generating "hallucinations" in specialized domains, producing inaccurate or false information that poses significant risks to the quality of aerospace products and flight safety. This paper introduces a set of evaluation metrics tailored for LLMs in aerospace manufacturing, aiming to assess their accuracy by analyzing their performance in answering questions grounded in professional knowledge. Firstly, key information is extracted through in-depth textual analysis of classic aerospace manufacturing textbooks and guidelines. Subsequently, utilizing LLM generation techniques, we meticulously construct multiple-choice questions with multiple correct answers of varying difficulty. Following this, different LLM models are employed to answer these questions, and their accuracy is recorded. Experimental results demonstrate that the capabilities of LLMs in aerospace professional knowledge are in urgent need of improvement. This study provides a theoretical foundation and practical guidance for the application of LLMs in aerospace manufacturing, addressing a critical gap in the field.

Goretti Cabaleiro-Cerviño, Pedro Mendi

This paper studies how the interplay between the cycle and firm-specific characteristics (firm size and exporting activities) shapes firms’ choice between exploratory and exploitative innovation investments. We analyze the PITEC database, a panel of Spanish firms in manufacturing and service industries with data spanning the 2005–2013 period, which includes the Great Recession years, characterized by sharp demand reductions and increased industry turnover. The results show that: 1) downturns affect exploration more than exploitation, making the probability of firms being classified as exploratory pro-cyclical; 2) the impact of downturns is stronger among small and medium enterprises (SMEs) than among large companies; and 3) exporting companies seem to be less affected by the cycle in their choice between exploration and exploitation. These results contribute to a deeper understanding of how external shocks interact with internal firm characteristics to shape innovation pathways.

I. I. Deren, V. V. Shmatkova

The Russian industry and its contribution to the country’s GDP have been analyzed, and the industrial sector by region has been studied. Statistical data and official reports of the Government of the Russian Federation for several years up to 2024 have been applied in order to track development trends. It has been estimated that the share of the industry in the country’s economy is about 30% of GDP and 22% of employment, reflecting its importance in the economy structure. In the context of foreign economic challenges and sanctions in 2022–2024, the industry has demonstrated adaptability through import substitution and government support, while maintaining its strategic importance. Industrial potential plays a key role in shaping the economic profile of Russian regions, especially in the Volga, Siberian, and Ural Districts. Despite the predominance of the service sector in the GDP structure (over 60%), the industry remains the most important sector. In 2023–2024, against the background of import substitution and digitalization measures, the manufacturing sector positions strengthened, reducing dependence on the raw material model. It has been recommended to pay more attention to industries that can provide domestic consumption of the country. The analysis gives an idea of the importance of industrial production for developing Russia’s economy and helps correctly prioritize the regulation and government support of these industries, but also pay attention to personnel training for these industries.

Destiny F. Williams, Shohreh Hemmati

Silver nanowires (AgNWs) have garnered significant attention in nanotechnology due to their unique mechanical and electrical properties and versatile applications. This review explores the synthesis of AgNWs, with a specific focus on the utilization of millifluidic flow reactors (MFRs) as a promising platform for controlled and efficient production. It begins by elucidating the exceptional characteristics and relevance of AgNWs in various technological domains and then delves into the principles and advantages of MFRs by showcasing their pivotal role in enhancing the precision and scalability of nanowire synthesis. Within this review, an overview of the diverse synthetic methods employed for AgNW production using MFRs is provided. Special attention is given to the intricate parameters and factors influencing synthesis and how MFRs offer superior control over these critical variables. Recent advances in this field are highlighted, revealing innovative strategies and promising developments that have emerged. As with any burgeoning field, challenges are expected, so future directions are explored, offering insights into the current limitations and opportunities for further exploration. In conclusion, this review consolidates the state-of-the-art knowledge in AgNW synthesis and emphasizes the critical role of MFRs in shaping the future of nanomaterial production and nanomanufacturing.

Xiao Liu, Alessandra Mileo, Alan F. Smeaton

In-situ monitoring incorporating data from visual and other sensor technologies, allows the collection of extensive datasets during the Additive Manufacturing (AM) process. These datasets have potential for determining the quality of the manufactured output and the detection of defects through the use of Machine Learning during the manufacturing process. Open and annotated datasets derived from AM processes are necessary for the machine learning community to address this opportunity, which creates difficulties in the application of computer vision-related machine learning in AM. This systematic review investigates the availability of open image-based datasets originating from AM processes that align with a number of pre-defined selection criteria. The review identifies existing gaps among the current image-based datasets in the domain of AM, and points to the need for greater availability of open datasets in order to allow quality assessment and defect detection during additive manufacturing, to develop.

Hongrui Chen, Aditya Joglekar, Zack Rubinstein et al.

Advances in CAD and CAM have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for manufacturing, and digitally tracking the entire process from design to procurement in the form of product life-cycle management tools. However, existing solutions force design teams and corporations to take a primarily serial approach where manufacturing and procurement decisions are largely contingent on design, rather than being an integral part of the design process. In this work, we propose a new approach to part making where design, manufacturing, and supply chain requirements and resources can be jointly considered and optimized. We present the Generative Manufacturing compiler that accepts as input the following: 1) An engineering part requirements specification that includes quantities such as loads, domain envelope, mass, and compliance, 2) A business part requirements specification that includes production volume, cost, and lead time, 3) Contextual knowledge about the current manufacturing state such as availability of relevant manufacturing equipment, materials, and workforce, both locally and through the supply chain. Based on these factors, the compiler generates and evaluates manufacturing process alternatives and the optimal derivative designs that are implied by each process, and enables a user guided iterative exploration of the design space. As part of our initial implementation of this compiler, we demonstrate the effectiveness of our approach on examples of a cantilever beam problem and a rocket engine mount problem and showcase its utility in creating and selecting optimal solutions according to the requirements and resources.

Gyeong Taek Lee, Oh-Ran Kwon

In the manufacturing process, sensor data collected from equipment is crucial for building predictive models to manage processes and improve productivity. However, in the field, it is challenging to gather sufficient data to build robust models. This study proposes a novel predictive model based on the Transformer, utilizing statistical feature embedding and window positional encoding. Statistical features provide an effective representation of sensor data, and the embedding enables the Transformer to learn both time- and sensor-related information. Window positional encoding captures precise time details from the feature embedding. The model's performance is evaluated in two problems: fault detection and virtual metrology, showing superior results compared to baseline models. This improvement is attributed to the efficient use of parameters, which is particularly beneficial for sensor data that often has limited sample sizes. The results support the model's applicability across various manufacturing industries, demonstrating its potential for enhancing process management and yield.

Raihanah Razita, Arif Fahmi, Muhammad Baisa Said

Along with the revolution of the manufacturing world, companies face a new challenge to continuously improving their production processes to meet the escalating demand for high-quality goods in a fiercely competitive market. The previous concept of Cloud-Based Quality Analyzer (CQA) can be used as a real-time monitoring software that provides information of the on-going process. This concept can be implemented as an effort to achieve high quality manufacturing. This study aimed to develop the descriptive analysis module to realise the concept of CQA and evaluate its ability in realising online quality monitoring process. By employing the waterfall methodology, this study developed and implemented the descriptive analysis module in the CQA environment. The module was implemented in a case study in guitar manufacturing. The result showed that this module worked perfectly in displaying the multivariate process control chart and successfully gave the warning when some processes were in out-ofcontrol state. Furthermore, the user acceptance test also showed a positive response from the users. The descriptive analysis module was believed able to enhance the quality of manufacturing process while reducing the dependencies to the human quality engineers. However, more case studies in various industries were required to evaluate the benefit of implementing this module.