Hasil untuk "Manufacturing industries"

D. Rajak, Durgesh Devchand Pagar, P. Menezes et al.

Composites have been found to be the most promising and discerning material available in this century. Presently, composites reinforced with fibers of synthetic or natural materials are gaining more importance as demands for lightweight materials with high strength for specific applications are growing in the market. Fiber-reinforced polymer composite offers not only high strength to weight ratio, but also reveals exceptional properties such as high durability; stiffness; damping property; flexural strength; and resistance to corrosion, wear, impact, and fire. These wide ranges of diverse features have led composite materials to find applications in mechanical, construction, aerospace, automobile, biomedical, marine, and many other manufacturing industries. Performance of composite materials predominantly depends on their constituent elements and manufacturing techniques, therefore, functional properties of various fibers available worldwide, their classifications, and the manufacturing techniques used to fabricate the composite materials need to be studied in order to figure out the optimized characteristic of the material for the desired application. An overview of a diverse range of fibers, their properties, functionality, classification, and various fiber composite manufacturing techniques is presented to discover the optimized fiber-reinforced composite material for significant applications. Their exceptional performance in the numerous fields of applications have made fiber-reinforced composite materials a promising alternative over solitary metals or alloys.

Y. Lakhdar, C. Tuck, J. Binner et al.

Abstract Additive manufacturing (AM) has the potential to disrupt the ceramic industry by offering new opportunities to manufacture advanced ceramic components without the need for expensive tooling, thereby reducing production costs and lead times and increasing design freedom. Whilst the development and implementation of AM technologies in the ceramic industry has been slower than in the polymer and metal industries, there is now considerable interest in developing AM processes capable of producing defect-free, fully dense ceramic components. A large variety of AM technologies can be used to shape ceramics, but variable results have been obtained so far. Selecting the correct AM process for a given application not only depends on the requirements in terms of density, surface finish, size and geometrical complexity of the part, but also on the nature of the particular ceramic to be processed. This paper provides a detailed review of the current state-of-the-art in AM of advanced ceramics through a systematic evaluation of the capabilities of each AM technology, with an emphasis on reported results in terms of final density, surface finish and mechanical properties. An in-depth analysis of the opportunities, issues, advantages and limitations arising when processing advanced ceramics with each AM technology is also provided.

Z. Çınar, Abubakar Abdussalam Nuhu, Q. Zeeshan et al.

Recently, with the emergence of Industry 4.0 (I4.0), smart systems, machine learning (ML) within artificial intelligence (AI), predictive maintenance (PdM) approaches have been extensively applied in industries for handling the health status of industrial equipment. Due to digital transformation towards I4.0, information techniques, computerized control, and communication networks, it is possible to collect massive amounts of operational and processes conditions data generated form several pieces of equipment and harvest data for making an automated fault detection and diagnosis with the aim to minimize downtime and increase utilization rate of the components and increase their remaining useful lives. PdM is inevitable for sustainable smart manufacturing in I4.0. Machine learning (ML) techniques have emerged as a promising tool in PdM applications for smart manufacturing in I4.0, thus it has increased attraction of authors during recent years. This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, device used in data acquisition, classification of data, size and type, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.

Ana Vafadar, F. Guzzomi, A. Rassau et al.

In recent years, Additive Manufacturing (AM), also called 3D printing, has been expanding into several industrial sectors due to the technology providing opportunities in terms of improved functionality, productivity, and competitiveness. While metal AM technologies have almost unlimited potential, and the range of applications has increased in recent years, industries have faced challenges in the adoption of these technologies and coping with a turbulent market. Despite the extensive work that has been completed on the properties of metal AM materials, there is still a need of a robust understanding of processes, challenges, application-specific needs, and considerations associated with these technologies. Therefore, the goal of this study is to present a comprehensive review of the most common metal AM technologies, an exploration of metal AM advancements, and industrial applications for the different AM technologies across various industry sectors. This study also outlines current limitations and challenges, which prevent industries to fully benefit from the metal AM opportunities, including production volume, standards compliance, post processing, product quality, maintenance, and materials range. Overall, this paper provides a survey as the benchmark for future industrial applications and research and development projects, in order to assist industries in selecting a suitable AM technology for their application.

Chang-tai Hsieh, Peter J. Klenow

M. Shahzad, Y. Qu, S. Javed et al.

Abstract Although extensive studies have focused on the impact of different corporate social responsibility (CSR) activities on environmental sustainability, few studies investigated the effects of CSR activities on environmentally sustainable development and green innovation. Yet, as a determinant of environmental strategies, green innovation haven’t received much attention. Therefore, this study explored how different CSR dimensions impacts on environmentally sustainable development and further on green innovation. Data were collected from 282 respondents belonging to the manufacturing industries of Pakistan from January 2019 to April 2019; and analyzed by adopting methods of partial least squares structural equation modeling (PLS-SEM) and grey relational analysis. As per study results, all dimensions of CSR were found positively significant towards environmentally sustainable development. Further, the environmentally sustainable development positively enhances green innovation. This research employed an innovative attempt to evaluate the ranking of these constructs by using grey relational analysis whereby it was found that CSR to environment had a stronger effect, whereas CSR to consumer had a weaker impact on environmentally sustainable development. The research findings suggest that CSR activities should be embedded in the organizational environmental strategies for green innovation, as all CSR dimensions had positive relations with environmentally sustainable development.

J. Apolinar Muñoz Rodríguez

Currently, bio-inspired metaheuristic algorithms play an important role in computer vision for assessing surface cracks. Also, manufacturing industries need non-destructive technologies based on biomimetics theory for characterizing micro-crack contours to determine surface quality. In this way, it is necessary to develop bio-inspired algorithms to construct crack contour models for determining crack regions through an optical microscope system. In this study, a metaheuristic genetic algorithm is implemented to build crack contour models by means of Bezier functions and crack coordinates. The contour modeling is performed by a microscope vision system based on micro-laser line scanning, which provides the crack coordinates through a broken laser line in the crack region. Thus, the metaheuristic algorithm builds the crack contour model by fitting the Bezier functions toward the crack topography. At this stage, an objective function moves the Bezier functions toward the crack topography via control points. The proposed technique provides micro-scale crack contours with a relative error smaller than 2%. Thus, the proposed crack contour modeling enhances the traditional crack contour inspection based on microscope image processing. This contribution is supported by a comparison between the proposed technique and the crack characterization performed via conventional image processing algorithms.

Andi Amijoyo Mochtar, La Ode Muhammad Ali

Modern manufacturing industries face increasing pressure to enhance operational efficiency while reducing energy costs and environmental impact. This research develops a metal waste cleaning robot with integrated multi-objective energy optimization for local manufacturing applications. The robot integrates 28 main components including dual motor systems (80 W drive motor, 60 W arm motor), HC-SR04 ultrasonic sensor, ESP32 microcontroller, and hierarchical thermal protection. Non-dominated Sorting Genetic Algorithm II (NSGA-II) simultaneously optimizes energy consumption, coverage completeness, and operational time. The multi-objective optimization framework achieves significant energy reductions through three independent mechanisms: trajectory planning optimization reduces total energy consumption by 30% (from 235.7 Wh to 165 Wh per cycle), adaptive control systems reduce motor power consumption by 50% (from 280 W to 140 W) through dynamic voltage adjustment based on environmental complexity, and strategic base station placement reduces travel distance by 20% (from 150 m to 120 m per cycle), resulting in corresponding energy savings. ANSYS validation confirms structural stability with maximum equivalent elastic strain of 7.6839 × 10−5 m/m and maximum equivalent deformation of 6.710 × 10−5 m (67.10 μm) under operational loading, demonstrating that the structure operates well within the elastic limit with safety factor >5. The robot demonstrates total power consumption of 165 W with 75.4% cleaning efficiency, reducing operational time from 35 min (manual methods) to 8.4 min across four material types (aluminum, copper, steel, glass). Performance testing shows 76.7% efficiency for chip cleaning (7 min) and 87.5% efficiency for metal dust cleaning (5 min). The hierarchical thermal protection system ensures operational safety with motor temperature sensors providing 35% protection effectiveness. This integrated optimization framework provides validated solutions for local manufacturing industries with limited technology accessibility, contributing to sustainable energy-efficient industrial robot for metal waste management in developing countries.

Stefano Felicioni, Adrian Barbosa Cantu, Elisa Padovano et al.

High-performance copper alloys are increasingly sought across multiple industries, including medical, power and energy, advanced tooling, and manufacturing, due to their exceptional thermal conductivity, oxidation resistance, and mechanical strength. Among these, GRCop-42 (CuCrNb) has gained strategic importance especially in aerospace engineering, where materials must endure extreme thermal gradients and mechanical loads in high-heat-flux environments, most notably in reusable rocket propulsion and combustion chamber applications. The performance of GRCop-42 is largely controlled by a fine dispersion of Cr2Nb Laves phase particles, which strengthen the alloy through precipitation and dispersion-hardening mechanisms.Additive manufacturing techniques, especially Laser Powder Bed Fusion (L-PBF), offer the potential to further refine the microstructure due to their inherently rapid solidification rates. This study investigates the microstructural evolution of L-PBF processed GRCop-42, with a particular focus on the thermal stability and coarsening behaviour of Cr2Nb precipitates under various aging conditions. By employing advanced microscopy techniques (FESEM and TEM) alongside crystallographic analyses (XRD and EBSD), this research aims to elucidate the mechanisms of Ostwald ripening and their influence on the alloy strengthening behaviour.

Uzair Bhatti, Qamar Jaleel, Umair Aslam et al.

The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution for real-time, proactive safety management. This paper evaluates AI models—Gemini 1.5 Pro, OPENAI GPT-4, and Copilot—in detecting workplace hazards, ensuring compliance with Process Safety Management (PSM) and DuPont safety frameworks. The study highlights the models’ potential in improving safety outcomes, reducing human error, and supporting continuous, data-driven risk management in petrochemical plants. This paper is the first of its kind to use the latest multimodal tech to identify the safety hazard; a similar model could be deployed in other manufacturing industries, especially the oil and gas (both upstream and downstream) industry, fertilizer industries, and production facilities.

B. Kalyn, S. Kropyvka, R. Paraniak et al.

Despite the exceptional importance of the grain industry in both the global and national economies, a number of systemic problems still persist at the regional level in Ukraine, hindering productivity growth and reducing the profitability of the sector. Grain production forms the foundation of food security, constitutes a significant share of agricultural exports, supports rural employment, and promotes the development of related industries – livestock farming, food processing, and manufacturing. At the same time, the efficiency of its functioning remains vulnerable to a complex set of natural, economic, and organizational factors. The leading among them are climatic conditions, soil quality and fertility, the level of material and technical support, the use of modern innovative agricultural technologies, and the adaptation of farms to the challenges of climate change. The article summarizes the results of studies concerning the relationship between the yield of grain crops and the efficiency of using natural, labor, and production resources. The focus is placed on the influence of crop management systems, as well as ecological and socio-economic factors that determine the spatial differentiation of productivity. The role of climate change, soil degradation processes, farming intensity, and institutional prerequisites for sectoral development are highlighted. Using Lviv region as a case study, the main factors causing differences in grain yield are outlined, and limiting conditions influencing productivity are identified. Special attention is paid to climate forecasts that indicate an increase in the frequency of extreme weather events, changes in the length of the growing season, and rising risks to production stability. These trends require the adaptation of agrotechnologies, optimization of crop structure, and the implementation of resource-saving and soil-protective practices. The results of the conducted analysis make it possible to substantiate a set of recommendations aimed at improving the yield and efficiency of the regional grain industry. Their implementation will contribute to strengthening the competitiveness of agricultural production, enhancing the sustainability of agroecosystems, and ensuring the food security of the state amid ongoing climatic and economic transformations.

Ishaan Kaushal, Amaresh Chakrabarti

This paper presents the Unified Smart Factory Model (USFM), a comprehensive framework designed to translate high-level sustainability goals into measurable factory-level indicators with a systematic information map of manufacturing activities. The manufacturing activities were modelled as set of manufacturing, assembly and auxiliary processes using Object Process Methodology, a Model Based Systems Engineering (MBSE) language. USFM integrates Manufacturing Process and System, Data Process, and Key Performance Indicator (KPI) Selection and Assessment in a single framework. Through a detailed case study of Printed Circuit Board (PCB) assembly factory, the paper demonstrates how environmental sustainability KPIs can be selected, modelled, and mapped to the necessary data, highlighting energy consumption and environmental impact metrics. The model's systematic approach can reduce redundancy, minimize the risk of missing critical information, and enhance data collection. The paper concluded that the USFM bridges the gap between sustainability goals and practical implementation, providing significant benefits for industries specifically SMEs aiming to achieve sustainability targets.

Supun Dissanayaka, Alexander Ferrein, Till Hofmann et al.

The RoboCup Logistics League is a RoboCup competition in a smart factory scenario that has focused on task planning, job scheduling, and multi-agent coordination. The focus on production logistics allowed teams to develop highly competitive strategies, but also meant that some recent developments in the context of smart manufacturing are not reflected in the competition, weakening its relevance over the years. In this paper, we describe the vision for the RoboCup Smart Manufacturing League, a new competition designed as a larger smart manufacturing scenario, reflecting all the major aspects of a modern factory. It will consist of several tracks that are initially independent but gradually combined into one smart manufacturing scenario. The new tracks will cover industrial robotics challenges such as assembly, human-robot collaboration, and humanoid robotics, but also retain a focus on production logistics. We expect the reenvisioned competition to be more attractive to newcomers and well-tried teams, while also shifting the focus to current and future challenges of industrial robotics.

Juanjo Zulaika, Ibone Oleaga, Anne Sanz et al.

The manufacturing sector is moving from rigid, hardware-dependent systems toward flexible, software-driven environments. This transformation is shaped by the convergence of several Software-Defined technologies: Software-Defined Automation virtualizes industrial control, replacing proprietary PLCs with containerized, programmable solutions that enable scalability and interoperability. Software-Defined Compute and Communications provide a means to distribute intelligence seamlessly across devices, networks, and cloud platforms, reducing latency and enabling dynamic reconfiguration. Software-Defined Manufacturing Systems, usually implemented as Digital Twins, are real-time virtual models of machines and processes, allowing predictive analysis, optimization, and closer integration between human operators and intelligent systems. This work presents XWAVE, a project that unites these three Software-Defined paradigms to present a modular, fully software-defined manufacturing system.

Christopher Martin, Apurva Patil, Wei Li et al.

Roll-to-roll (R2R) manufacturing is a continuous processing technology essential for scalable production of thin-film materials and printed electronics, but precise control remains challenging due to subsystem interactions, nonlinearities, and process disturbances. This paper proposes a Model Predictive Path Integral (MPPI) control formulation for R2R systems, leveraging a GPU-based Monte-Carlo sampling approach to efficiently approximate optimal controls online. Crucially, MPPI easily handles non-differentiable cost functions, enabling the incorporation of complex performance criteria relevant to advanced manufacturing processes. A case study is presented that demonstrates that MPPI significantly improves tension regulation performance compared to conventional model predictive control (MPC), highlighting its suitability for real-time control in advanced manufacturing.

Yi-Ping Chen, Vispi Karkaria, Ying-Kuan Tsai et al.

Digital Twin -- a virtual replica of a physical system enabling real-time monitoring, model updating, prediction, and decision-making -- combined with recent advances in machine learning, offers new opportunities for proactive control strategies in autonomous manufacturing. However, achieving real-time decision-making with Digital Twins requires efficient optimization driven by accurate predictions of highly nonlinear manufacturing systems. This paper presents a simultaneous multi-step Model Predictive Control (MPC) framework for real-time decision-making, using a multivariate deep neural network, named Time-Series Dense Encoder (TiDE), as the surrogate model. Unlike conventional MPC models which only provide one-step ahead prediction, TiDE is capable of predicting future states within the prediction horizon in one shot (multi-step), significantly accelerating the MPC. Using Directed Energy Deposition (DED) additive manufacturing as a case study, we demonstrate the effectiveness of the proposed MPC in achieving melt pool temperature tracking to ensure part quality, while reducing porosity defects by regulating laser power to maintain melt pool depth constraints. In this work, we first show that TiDE is capable of accurately predicting melt pool temperature and depth. Second, we demonstrate that the proposed MPC achieves precise temperature tracking while satisfying melt pool depth constraints within a targeted dilution range (10\%-30\%), reducing potential porosity defects. Compared to PID controller, the MPC results in smoother and less fluctuating laser power profiles with competitive or superior melt pool temperature control performance. This demonstrates the MPC's proactive control capabilities, leveraging time-series prediction and real-time optimization, positioning it as a powerful tool for future Digital Twin applications and real-time process optimization in manufacturing.

Yingyan Zeng, Ismini Lourentzou, Xinwei Deng et al.

Artificial intelligence (AI) systems have been increasingly adopted in the Manufacturing Industrial Internet (MII). Investigating and enabling the AI resilience is very important to alleviate profound impact of AI system failures in manufacturing and Industrial Internet of Things (IIoT) operations, leading to critical decision making. However, there is a wide knowledge gap in defining the resilience of AI systems and analyzing potential root causes and corresponding mitigation strategies. In this work, we propose a novel framework for investigating the resilience of AI performance over time under hazard factors in data quality, AI pipelines, and the cyber-physical layer. The proposed method can facilitate effective diagnosis and mitigation strategies to recover AI performance based on a multimodal multi-head self latent attention model. The merits of the proposed method are elaborated using an MII testbed of connected Aerosol Jet Printing (AJP) machines, fog nodes, and Cloud with inference tasks via AI pipelines.

Roosa Risto, Mohit Sethi, Mika Katara

The Cyber Resilience Act (CRA) is a new European Union (EU) regulation aimed at enhancing the security of digital products and services by ensuring they meet stringent cybersecurity requirements. This paper investigates the challenges that industrial equipment manufacturing companies anticipate while preparing for compliance with CRA through a comprehensive survey. Key findings highlight significant hurdles such as implementing secure development lifecycle practices, managing vulnerability notifications within strict timelines, and addressing gaps in cybersecurity expertise. This study provides insights into these specific challenges and offers targeted recommendations on key focus areas, such as tooling improvements, to aid industrial equipment manufacturers in their preparation for CRA compliance.

Muhammad Yousuf Rafiq, Zaheer Abbas, Jafar Hasnain et al.

Peristalsis has gained significant attention due to its numerous applications in the medical field, engineering, and manufacturing industries. Therefore, the current work intends to look into the effects of variable liquid properties on the magnetohydrodynamics of peristaltic flow exhibited by viscous fluid through a tapered channel. The viscosity of the liquid differs over the thickness of the channel, and temperature-dependent thermal conductivity is considered. The constitutive relation for energy is formulated with the addition of viscous dissipation and heat generation/absorption. The assumption of velocity slip along with the convective boundary condition energizes the thermal system as well as the flow phenomena. The mathematical formulation is established on the grounds of low Reynolds number and long wavelength approximations. Perturbation solution were obtained for the resulting non-linear differential equations of momentum and energy for small values of variable viscosity and variable thermal conductivity. The effects of various relevant parameters on flow properties were investigated through graphical analysis. The results show that the maximum velocity does not occur in the middle of the tapered channel, but moves toward the upper wall with the increase in the variable viscosity difference between the walls. The application of viscosity is essential in many engineering and industrial processes.

Vidyadhar G Biradar, S S Hebbal, S M Qutubuddin

Introduction: Musculoskeletal disorders are the major factors resulting in discomfort at work in manufacturing industries to workers and these conditions contribute to the poor health of the workforce, subsequently to lower productivity. Therefore, the design of a workstation based on Ergonomic principles is becoming significant to reduce the effects of MSD. This study aimed to identify and assess the ergonomic risks associated with the work tasks in the company through posture analysis and develop recommendations for reducing those risks. Methods: About 36 manufacturing workers from five sections were randomly selected for the cross-sectional study. The chosen team members were from Core building, Core winding, Assembly, Tanking and Tank fabrication sections with experience of more than two years. The presence of MSDs was assessed using a Nordic musculoskeletal questionnaire.  For postural analysis, Rapid Upper Limb Assessment (RULA) and Rapid Entire Body Assessment (REBA) were utilized. Few selected postures were analysed using CATIAV5 software and improvements reducing the risks of postures were recommended. Results: All of the 36 workers selected for the study were male with a mean age of 32 years and, average experience of 10 years, and 75% of workers had normal body mass index. The MSD questionnaire indicated discomfort of 86% mostly in the lower back portion of the body. The combined findings of RULA and REBA showed that about 44% of postures were in the high-risk group. Conclusion: Well-defined ergonomic interventions such as redesigning the workstation are suggested to reduce awkward postures and manual handling risks, leading to improvement in job performance and productivity.