Hasil untuk "Manufacturing industries"

Vignesh Alagappan

Industrial IoT platforms in global manufacturing environments generate continuous operational data across production assets, utilities, and connected products. While data ingestion and storage capabilities have matured significantly, enterprises continue to face systemic challenges in governing IoT data at scale. These challenges are not rooted in tooling limitations but in the absence of a governance model that aligns with the realities of distributed operational ownership, heterogeneous source systems, and continuous change at the edge. This paper presents a federated governance model that emphasizes contract-driven interoperability, policy-as-code enforcement, and asset-centric accountability across global manufacturing organizations. The model addresses governance enforcement at architectural boundaries, enabling semantic consistency, quality assurance, and regulatory compliance without requiring centralized control of operational technology systems. This work contributes a systems architecture and design framework grounded in analysis of manufacturing IoT requirements and constraints; empirical validation remains future work

Mohammadhossein Ghahramani, Mengchu Zhou

Accurate fault detection in high-dimensional industrial environments remains a major challenge due to the inherent complexity, noise, and redundancy in sensor data. This paper introduces CLAIRE, i.e., a hybrid end-to-end learning framework that integrates unsupervised deep representation learning with supervised classification for intelligent quality control in smart manufacturing systems. It employs an optimized deep autoencoder to transform raw input into a compact latent space, effectively capturing the intrinsic data structure while suppressing irrelevant or noisy features. The learned representations are then fed into a downstream classifier to perform binary fault prediction. Experimental results on a high-dimensional dataset demonstrate that CLAIRE significantly outperforms conventional classifiers trained directly on raw features. Moreover, the framework incorporates a post hoc phase, using a game-theory-based interpretability technique, to analyze the latent space and identify the most informative input features contributing to fault predictions. The proposed framework highlights the potential of integrating explainable AI with feature-aware regularization for robust fault detection. The modular and interpretable nature of the proposed framework makes it highly adaptable, offering promising applications in other domains characterized by complex, high-dimensional data, such as healthcare, finance, and environmental monitoring.

Seyed Ali Ghazi Asgar, Narasimha Reddy, Satish T. S. Bukkapatnam

While additive manufacturing has opened interesting avenues to reimagine manufacturing as a service (MaaS) platform, transmission of design files from client to manufacturer over networks opens up many cybersecurity challenges. Securing client's intellectual property (IP) especially from cyber-attacks emerges as a major challenge. Earlier works introduced streaming, instead of sharing process plan (G-code) files, as a possible solution. However, executing client's G-codes on manufacturer's machines exposes them to potential malicious G-codes. This paper proposes a viable approach when the client and manufacturer do not trust each other and both the client and manufacturer want to preserve their IP of designs and manufacturing process respectively. The proposed approach is based on segmenting and streaming design (STL) files and employing a novel machine-specific STL to G-code translator at the manufacturer's site in real-time for printing. This approach secures design and manufacturing process IPs as demonstrated in a real-world implementation.

Hongliang Li, Herschel C. Pangborn, Ilya Kovalenko

Manufacturing industries are among the highest energy-consuming sectors, facing increasing pressure to reduce energy costs. This paper presents an energy-aware Model Predictive Control (MPC) framework to dynamically schedule manufacturing processes in response to time-varying electricity prices without compromising production goals or violating production constraints. A network-based manufacturing system model is developed to capture complex material flows, batch processing, and capacities of buffers and machines. The scheduling problem is formulated as a Mixed-Integer Quadratic Program (MIQP) that balances energy costs, buffer levels, and production requirements. A case study evaluates the proposed MPC framework under four industrial electricity pricing schemes. Numerical results demonstrate that the approach reduces energy usage expenses while satisfying production goals and adhering to production constraints. The findings highlight the importance of considering the detailed electricity cost structure in manufacturing scheduling decisions and provide practical insights for manufacturers when selecting among different electricity pricing strategies.

Hui Yang, Faisal Aqlan, Richard Zhao

The rapid evolution of modern manufacturing systems is driven by the integration of emerging metaverse technologies such as artificial intelligence (AI), digital twin (DT) with different forms of extended reality (XR) like virtual reality (VR), augmented reality (AR), and mixed reality (MR). These advances confront manufacturing workers with complex and evolving environments that demand digital literacy for problem solving in the future workplace. However, manufacturing industry faces a critical shortage of skilled workforce with digital literacy in the world. Further, global pandemic has significantly changed how people work and collaborate digitally and remotely. There is an urgent need to rethink digital platformization and leverage emerging technologies to propel industrial evolution toward human-centered manufacturing metaverse (MfgVerse). This paper presents a forward-looking perspective on the development of smart MfgVerse, highlighting current efforts in learning factory, cognitive digital twinning, and the new sharing economy of manufacturing-as-a-service (MaaS). MfgVerse is converging into multiplex networks, including a social network of human stakeholders, an interconnected network of manufacturing things or agents (e.g., machines, robots, facilities, material handling systems), a network of digital twins of physical things, as well as auxiliary networks of sales, supply chain, logistics, and remanufacturing systems. We also showcase the design and development of a learning factory for workforce training in extended reality. Finally, future directions, challenges, and opportunities are discussed for human-centered manufacturing metaverse. We hope this work helps stimulate more comprehensive studies and in-depth research efforts to advance MfgVerse technologies.

Jiahang Li, Lucas Cazzonelli, Jacqueline Höllig et al.

The growing adoption of Industrial Internet of Things (IIoT) technologies enables automated, real-time collection of manufacturing process data, unlocking new opportunities for data-driven product development. Current data-driven methods are generally applied within specific domains, such as design or manufacturing, with limited exploration of integrating design features and manufacturing process data. Since design decisions significantly affect manufacturing outcomes, such as error rates, energy consumption, and processing times, the lack of such integration restricts the potential for data-driven product design improvements. This paper presents a data-driven approach to mapping and analyzing the relationship between design features and manufacturing process data. A comprehensive system architecture is developed to ensure continuous data collection and integration. The linkage between design features and manufacturing process data serves as the basis for developing a machine learning model that enables automated design improvement suggestions. By integrating manufacturing process data with sustainability metrics, this approach opens new possibilities for sustainable product development.

Byasdeb Dasgupta, Dip Dutta

The present study is an attempt to analyse and assess the wage-productivity gap in the Indian manufacturing industries during the last few decades along with a focus on labour market flexibility in recent time. We carry out the study at the All-India level using the available ASI database for the period 1973-2020. The basic objective of the study is two-fold: (a) to assess the wage productivity gap in Indian manufacturing industry based on secondary data available from Annual Survey of Industries (ASI) and (b) to see whether the labour market flexibility at the same time period has any mutual association with the wage productivity gap in Indian manufacturing industry. We measured the wage-productivity gap at the 3-digit level of the NIC classification of industry groups by regrouping them into divisions of industries. We have tried to relate the wage productivity gap in terms of labour share with the ongoing effort for labour market flexibility in India since 1991.

Al-Mahdi Sallam, Norhaliza Abdul Wahab, Muhammad Zakiyullah Romdlony et al.

Mobile robots have been widely used in many industries including manufacturing, healthcare and warehouse automation. To ensure efficiency and safety of the robots, it is crucial to design effective control strategies that can adapt to changing environments. This paper presents reinforcement learning (RL) algorithms including Q-learning, Deep Q-Learning (DQN), and Double Deep Q-Learning (DDQN) for autonomous navigation using the Turtle-Bot3 Waffle Pi in a Gazebo-simulated environment. Three progressively complex training stages were designed to evaluate the algorithms: (1) static obstacles with predefined goals, (2) randomized goals with static obstacles, and (3) dynamic obstacles with moving goals. Performance metrics, including success rates, collision avoidance, and reward stability, were analyzed to compare algorithm effectiveness. Key results highlight DDQN’s superiority in handling complex navigation tasks. In the most challenging stage, DDQN achieved a 100% success rate and zero collisions, outperforming DQN, which attained an 88% success rate with higher collision rates. Q-learning performed well only in simple environments, as it cannot easily handle continuous state spaces. This study demonstrates the scalability of RL-based navigation systems for autonomous mobile robots. The findings provide a foundation for future advancements in dynamic and real-world robot navigation.

Rahul Kumar, Vikram Singh, Priya Gupta

The advent of 3D printing has transformed the pharmaceutical industry, enabling precision drug manufacturing with controlled release profiles, dosing, and structural complexity. Additive manufacturing (AM) addresses the growing demand for personalized medicine, overcoming limitations of traditional methods. This technology facilitates tailored dosage forms, complex geometries, and real-time quality control. Recent advancements in drop-on-demand printing, UV curable inks, material science, and regulatory frameworks are discussed. Despite opportunities for cost reduction, flexibility, and decentralized manufacturing, challenges persist in scalability, reproducibility, and regulatory adaptation. This review provides an in-depth analysis of the current state of AM in pharmaceutical manufacturing, exploring recent developments, challenges, and future directions for mainstream integration.

Martin Nisser

Fabrication today relies on disparate, large machines spread across industrial facilities. These are operated by domain experts to construct and assemble artefacts in sequential steps from large numbers of parts. This traditional, centralized mass manufacturing paradigm is characterized by large capital costs and inflexibility to changing needs, complex global supply chains hinged on economic and political stability, and waste and over-manufacturing of uniform artefacts that fail to meet the technical and personal needs of today's diverse individuals and use cases. Today, these challenges are particularly severe at points of need, such as the space environment. The space environment is remote and unpredictable, and the ability to manufacture in situ offers unique opportunities to address new challenges as they arise. However, the challenges faced in space are often mirrored on Earth. In hospitals, disaster zones, low resource environments and laboratories, the ability to manufacture customized artefacts at points of need can significantly enhance our ability to respond rapidly to unforeseen events. In this thesis, I introduce digital fabrication platforms with co-developed hardware and software that draw on tools from robotics and human-computer interaction to automate manufacturing of customized artefacts at the point of need. Highlighting three research themes across fabrication machines, modular assembly, and programmable materials, the thesis will cover a digital fabrication platform for producing functional robots, a modular robotic platform for in-space assembly deployed in microgravity, and a method for programming magnetic material to selectively assemble.

Alexander D. Zemskov, Yao Fu, Runchao Li et al.

In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remain inadequately explored. Towards that end, this paper analyzes the cybersecurity threats of digital twins for advanced manufacturing in the context of data collection, data sharing, machine learning and deep learning, and system-level security and privacy. We also provide several solutions to the threats in those four categories that can help establish more trust in digital twins.

Mohamed Abdelaal

In this paper, we explore the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, highlighting its potential to revolutionize industry practices and enhance operational efficiency. We delve into various applications of AI in manufacturing, with a particular emphasis on human-machine interfaces (HMI) and AI-powered milling machines, showcasing how these technologies contribute to more intuitive operations and precision in production processes. Through rigorous market analysis, the paper presents insightful data on AI adoption rates among German manufacturers, comparing these figures with global trends and exploring the specific uses of AI in production, maintenance, customer service, and more. In addition, the paper examines the emerging field of Generative AI and the potential applications of large language models in manufacturing processes. The findings indicate a significant increase in AI adoption from 6% in 2020 to 13.3% in 2023 among German companies, with a projection of substantial economic impact by 2030. The study also addresses the challenges faced by companies, such as data quality and integration hurdles, providing a balanced view of the opportunities and obstacles in AI implementation.

Elham Haghighat Naeini, Robert Sekula

In recent years, additive manufacturing (AM) has made considerable progress and has spread in many industries. Despite the advantages of this technology including freedom of design, lead time reduction, material waste reduction, special tools manufacturing elimination, and sustainability, there are still a lot of challenges regarding finding the beneficial application. In this study, the feasibility of replacing traditional manufacturing methods with additive manufacturing in the energy sector is investigated, with a specific focus on gas-insulated high-voltage switchgear (GIS). All aluminum parts in one specific GIS product are analyzed and a decision flowchart is proposed. Using this flowchart, printability and the best AM technique are suggested with respect to part size, required surface roughness, requirements of electrical and mechanical properties, and additional post processes. Simple to medium complexity level of geometry, large size, high requirements for electrical and mechanical properties, threading and sealing, and lack of a standard for printed parts in the high voltage industry make AM a challenging manufacturing technology for this specific product. In total, implementing AM as a short series production method for GIS aluminum parts may not be sufficient because of the higher cost and more complex supply chain management, but it can be beneficial in R&D cases or prototyping scenarios where a limited number of parts are needed in a brief time limit.

Rama Seshu K. V. Ganga, Ramu Inala, Chandra Sekhar Jowdula et al.

Fused deposition modelling (FDM) is a widely used 3D printing technique known for its versatility across industries. However, achieving optimal strength, crucial for applications like the automotive and aerospace industries, remains a challenge. This study demonstrates the efficacy of metal-infused filaments in enhancing FDM’s strength and quality. By incorporating metal particles into polymer matrices, their mechanical properties are notably improved. PLA and metal-infill PLA (copper, silver) are tested, with silver PLA showing notably higher tensile strength and hardness. Considerations such as infill density and pattern are discussed for optimizing object strength. This work underscores the potential of metal-infused FDM printing for advancing manufacturing capabilities, especially for intricate, high-strength metal components.

Mehdi Heydari, alireza alinezhad, Behnam Vahdani

Introduction: In light of recent advancements in the modern world, multivariate-multistage quality control patterns are increasingly recognized as vital and indispensable in manufacturing industries. This study delves into the significance and necessity of multivariate-multistage quality control in manufacturing, specifically focusing on motor oil production. As a foundational factor, motor oil quality considerably influences engine performance, lifespan, customer satisfaction, and market positioning. This research employs deep learning algorithms for monitoring and fault detection in quality components. The primary rationale for opting for deep learning algorithms over conventional statistical methods is the non-normal distribution of data and the large sample sizes, which can lead to inaccurate estimations and unstable analyses. Conversely, the unique capabilities of deep learning algorithms in handling complex data and extracting meaningful features from extensive motor oil production data justify their selection. Methods: To bolster accuracy and effective quality control, a combination of deep learning algorithms is utilized, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and hybrid models such as LSTM-CNN, as well as Residual Networks (ResNet) with Dense Networks (DenseNet). The LSTM-CNN algorithm is applied to control numerical quality variables and identify temporal and sequential patterns in the data. Meanwhile, ResNet-DenseNet manages and analyzes visual data with non-uniform and intricate distributions. By integrating LSTM networks, CNNs, and residual connections, these algorithms excel at extracting meaningful features and capturing complex relationships within the data. This enhances performance and efficiency in quality control processes and facilitates intelligent decision-making. Such an approach is adept at uncovering latent patterns and intricate relationships between variables and quality attributes, enhancing quality control procedures and intelligent decision-making. Results and discussion: The amalgamation of these algorithmic capabilities enhances the efficacy of quality control processes, outperforming single-algorithm approaches. Additionally, the Bee Colony Clonal Algorithm (BCC) is employed to fine-tune the parameters of the LSTM-CNN and ResNet-DenseNet algorithms. This hybrid approach harnesses the Artificial Bee Colony (ABC) and Genetic Algorithm (GA) strengths, markedly improving the performance of deep learning algorithms in quality control and reducing the time required to achieve desired outcomes. To illustrate the practical applicability of the proposed algorithms, a case study in the motor oil production industry is examined. The proposed LSTM-CNN hybrid algorithm in fault detection demonstrated superior results compared to standalone CNN and LSTM algorithms, achieving performance improvements of approximately 15% and 8%, respectively. Furthermore, the proposed ResNet-DenseNet hybrid algorithm exhibited higher accuracy in visual components, enhancing performance by approximately 10% and 15% compared to ResNet and DenseNet algorithms, respectively.  Conclusions: From both academic and practical standpoints, this research scrutinizes deep learning algorithms' influence on enhancing motor oil quality and efficiency. Advanced data analysis methods, particularly hybrid deep learning algorithms, are employed to identify quality patterns in production data.

Devang Mehta, Noah Klarmann

Manufacturing industries require efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precision. In general, automation based on computer vision is a promising solution to prevent bottlenecks at the product quality checkpoint. We considered recent advancements in machine learning to improve visual defect localization, but challenges persist in obtaining a balanced feature set and database of the wide variety of defects occurring in the production line. Hence, this paper proposes a defect localizing autoencoder with unsupervised class selection by clustering with k-means the features extracted from a pre-trained VGG16 network. Moreover, the selected classes of defects are augmented with natural wild textures to simulate artificial defects. The study demonstrates the effectiveness of the defect localizing autoencoder with unsupervised class selection for improving defect detection in manufacturing industries. The proposed methodology shows promising results with precise and accurate localization of quality defects on melamine-faced boards for the furniture industry. Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios.

Haotian Zhang, Semujju Stuart Dereck, Zhicheng Wang et al.

Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods. The emergence of large scale foundational models(LSFMs) had triggered a wave in the field of artificial intelligence, shifting deep learning models from single-task, single-modal, limited data patterns to a paradigm encompassing diverse tasks, multimodal, and pre-training on massive datasets. Although LSFMs had demonstrated powerful generalization capabilities, automatic high-quality training dataset generation and superior performance across various domains, applications of LSFMs on intelligent manufacturing were still in their nascent stage. A systematic overview of this topic was lacking, especially regarding which challenges of deep learning can be addressed by LSFMs and how these challenges can be systematically tackled. To fill this gap, this paper systematically expounded current statue of LSFMs and their advantages in the context of intelligent manufacturing. and compared comprehensively with the challenges faced by current deep learning models in various intelligent manufacturing applications. We also outlined the roadmaps for utilizing LSFMs to address these challenges. Finally, case studies of applications of LSFMs in real-world intelligent manufacturing scenarios were presented to illustrate how LSFMs could help industries, improve their efficiency.

David M. Wirth, Chi Chung Li, Jonathan K. Pokorski et al.

The field of additive manufacturing (AM) has advanced considerably over recent decades through the development of novel methods, materials, and systems. However, as the field approaches maturity, it is relevant to investigate the scaling frontiers and fundamental limits of AM in a generalized sense. Here we propose a simplified universal mathematical model that describes the essential process dynamics of many AM hardware platforms. We specifically examine the influence of several key parameters on total manufacturing time, comparing these with performance results obtained from real-world AM systems. We find a inverse-cubic dependency on minimal feature size and a linear dependency on overall structure size. These relationships imply how certain process features such as parallelization and process dimensionality can help move toward the fundamental limits. AM methods that are capable of varying the size of deposited voxels provide one possibility to overcome these limits in the future development of AM. We also propose a new framework for classifying manufacturing processes as "top-down" vs "bottom-up" paradigms, which differs from the conventional usage of such terms, and present considerations for how "bottom-up" manufacturing approaches may surpass the fundamental limits of "top-down" systems.

Prithwish Basu Roy, Mudit Bhargava, Chia-Yun Chang et al.

Digital Manufacturing (DM) refers to the on-going adoption of smarter, more agile manufacturing processes and cyber-physical systems. This includes modern techniques and technologies such as Additive Manufacturing (AM)/3D printing, as well as the Industrial Internet of Things (IIoT) and the broader trend toward Industry 4.0. However, this adoption is not without risks: with a growing complexity and connectivity, so too grows the cyber-physical attack surface. Here, malicious actors might seek to steal sensitive information or sabotage products or production lines, causing financial and reputational loss. Of particular concern are where such malicious attacks may enter the complex supply chains of DM systems as Trojans -- malicious modifications that may trigger their payloads at later times or stages of the product lifecycle. In this work, we thus present a comprehensive overview of the threats posed by Trojans in Digital Manufacturing. We cover both hardware and software Trojans which may exist in products or their production and supply lines. From this, we produce a novel taxonomy for classifying and analyzing these threats, and elaborate on how different side channels (e.g. visual, thermal, acoustic, power, and magnetic) may be used to either enhance the impact of a given Trojan or utilized as part of a defensive strategy. Other defenses are also presented -- including hardware, web-, and software-related. To conclude, we discuss seven different case studies and elaborate how they fit into our taxonomy. Overall, this paper presents a detailed survey of the Trojan landscape for Digital Manufacturing: threats, defenses, and the importance of implementing secure practices.

Xiao-Ling Tang, Jia-Wei Yu, Yu-Heng Geng et al.

The increasingly complex molecular structures and high requirements of advanced industries are triggering a transformation in chemical production modes. Bio-manufacturing provides efficient strategies and brings the advantages of high atomic economy, few side reactions, and strong adaptability to processes, as well as environmental friendliness, which can contribute toward global efforts against greenhouse effect and environmental pollution. The significance of bio-manufacturing can be specifically illustrated by examining the bio-manufacturing process from the scientific discovery of a key compound to its technological integration and engineering innovation. The development of statins—important drugs for hypercholesterolemia treatment—is a good example of the progress and application of bio-manufacturing. The production of the first-generation statins from microorganisms, the second-generation statins using bioconversion, and the third-generation statins through an evolution from total chemical synthesis to chemoenzymatic synthesis demonstrates the technological and engineering revolution of bio-manufacturing, which is of great importance for energy conservation, cost saving, and waste emission reduction. With advances in cutting-edge biotechnologies, as well as the integration of multiple disciplines, bio-manufacturing is expected to promote the advancement of more intelligent processes to realize sustainable and green industrial development.