Hasil untuk "Mechanical industries"

T. Tshephe, S. Akinwamide, E. Olevsky et al.

The development of materials for biomedical, aerospace, and automobile industries has been a significant area of research in recent years. Various metallic materials, including steels, cast iron, nickel-based alloys, and other metals with exceptional mechanical properties, have been reportedly utilized for fabrication in these industries. However, titanium and its alloys have proven to be outstanding due to their enhanced properties. The β-titanium alloys with reduced modulus compared with the human bone have found more usage in the biomedical industry. In contrast, the α and α+β titanium alloys are more utilized to fabricate parts in the automobile and aerospace industries due to their relatively lightweight. Amongst the numerous additive manufacturing (AM) techniques, selective laser and electron beam melting techniques are frequently used for the fabrication of metallic components due to the full densification and high dimensional accuracy they offer. This paper reviews and discusses the different types of AM techniques, attention is also drawn to the properties and challenges associated with additively manufactured titanium -based alloys. The outcome from this study shows that 3D printed titanium and titanium-alloys exhibit huge prospects for various applications in the medical and aerospace industries. Also, laser-assisted 3D technologies were found to be the most effective AM method for achieving enhanced or near-full densification.

B. Ghosh, M. Urban

E. Wycisk, A. Solbach, S. Siddique et al.

Abstract Laser Additive Manufacturing (LAM) enables economical production of complex lightweight structures as well as patient individual implants. Due to these possibilities the additive manufacturing technology gains increasing importance in the aircraft and the medical industry. Yet these industries obtain high quality standards and demand predictability of material properties for static and dynamic load cases. However, especially fatigue and crack propagation properties are not sufficiently determined. Therefore this paper presents an analysis and simulation of crack propagation behavior considering Laser Additive Manufacturing specific defects, such as porosity and surface roughness. For the mechanical characterization of laser additive manufactured titanium alloy Ti-6Al-4V, crack propagation rates are experimentally determined and used for an analytical modeling and simulation of fatigue. Using experimental results from HCF tests and simulated data, the fatigue and crack resistance performance is analyzed considering material specific defects and surface roughness. The accumulated results enable the reliable prediction of the defects influence on fatigue life of laser additive manufactured titanium components.

M. Mahmud, Ferdausee Rahman Anannya

Sugarcane bagasse is a fibrous material containing cellulose as its main component. It is produced in large quantities across the world. It is a kind of waste material that comes from the sugar industry. It is most commonly used in paper industries, but researchers have suggested that different mechanical and chemical treatments can help to extract cellulosic fibers, pure cellulose, cellulose nanofibers, and cellulose nanocrystals. These extracted materials have diverse applications in regenerated cellulosic fiber and composite material production. This paper will discuss the extraction procedures and typical applications in composite industries of these extracted materials. And an assessment will also be done on the production process and the properties of the end products to find out some common factors which can control the properties of these extracted material reinforced composites to some extent.

E. Baroncini, S. Yadav, G. Palmese et al.

Xiaofei Dong, Wentao Gan, Ying Shang et al.

R. Kumar, M. I. Ul Haq, Ankush Raina et al.

ABSTRACT The growing industrial demand for sustainable materials has led to a paradigm shift in the focus from synthetic polymers towards natural fibres. This paper deals with the challenges and opportunities associated with the use of natural fibre-reinforced polymer composites in various industrial applications. Natural fibres being biodegradable, light in weight, cost-effective and environment friendly are good candidate materials for modern industrial applications. Use of natural fibres in various industries with a focus on automotive and furniture industry has been discussed. The commonly used natural fibres in polymer composites including jute, hemp, sisal, kenaf, bamboo, cotton, flax, abaca, coir etc. have been dealt with in this paper. The literature revealed that tensile strength and other mechanical properties of these fibres are comparable to synthetic fibres like glass or carbon fibres. However, the temperature stability of polymers limits their extensive use and remains an issue to be addressed.

Hengels Castillo, Humberto Collado, Thomas Droguett et al.

Beatriz Monteiro, Aida B. Moreira, Sónia Simões

Hybrid nanocomposites based on Aluminum 6061 (Al 6061) reinforced with carbon nanotubes (CNTs) and aluminum oxide (Al₂O₃) emerge as promising materials due to their ability to achieve simultaneous improvements in strength, thermal stability, and tribological performance. This study examines the structure–property relationships of CNT–Al₂O₃ nano-reinforced hybrid Al 6061, with particular emphasis on microstructural evolution and mechanical properties. The nanocomposites are fabricated via a powder metallurgy route, which enables optimized dispersion and homogeneous distribution of CNTs and Al₂O₃ within the aluminum matrix. Microstructural characteristics, interfacial bonding, and grain refinement are systematically analyzed using scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). Mechanical characterization demonstrates a marked enhancement in mechanical properties compared to Al 6061. The observed property improvements are attributed to synergistic strengthening mechanisms, including effective load transfer from the matrix to Al₂O₃ particles, CNT-induced grain refinement, and increased resistance to dislocation motion. These results establish a direct correlation between microstructural features and mechanical performance, highlighting the potential of CNT–Al₂O₃ reinforced Al 6061 hybrid nanocomposites for lightweight, high-strength applications in aerospace, automotive, and structural engineering industries.

Ahmed Basager, Abdullah Alrabghi

Musculoskeletal disorders remain a persistent concern in automotive repair, yet empirical evidence on task-specific ergonomic risks in Middle Eastern contexts is limited. This study provides a detailed ergonomic risk profile of auto body specialists in Jeddah, Saudi Arabia, using a mixed-method approach that integrates the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and a validated Nordic Musculoskeletal Questionnaire. Twenty-five specialists across diverse tasks including installation, weighing, painting, cutting, and lifting were systematically evaluated to identify both postural and self-reported risk patterns. Results showed a high prevalence of discomfort in the lower back (64%), shoulders (52%), and wrists (48%). Ergonomic assessment revealed that the evaluated tasks were predominantly classified as moderate-to-high-risk, with RULA scores ranging from 6 to 7 and REBA scores ranging from 8 to 11. Beyond confirming the physical strain inherent to auto body work, the study highlights contextual factors such as prolonged static postures, limited use of mechanical aids, and constrained workshop layouts that exacerbate ergonomic risks. Importantly, the findings inform multi-level recommendations ranging from workshop practices to industry standards and policy considerations ensuring that interventions are both practical and scalable. By situating locally grounded results within the broader discourse on musculoskeletal risk prevention, the study offers region-specific evidence while providing globally relevant lessons for labor-intensive industries.

Mihira Kumar Nath, N. Bhanu Prasad, Asini Kumar Baliarsingh

Renewable energy sources (RESs) hold a significant share in modern electrical networks, particularly in Microgrids (MGs). The inertia of the MG is significantly reduced due to the substitution of traditional synchronous generators with RESs. Frequency control of MG integrated with RESs is a challenging task. This research proposes a robust solution to enhance the frequency stability of an islanded MG by applying virtual inertia control (VIC) and damping strategies. A multistage proportional integral derivative (PID) ([PDF]-[1+PI]) controller optimized through a modified golf optimization algorithm (mGOA) in coordination with an energy storage system (ESS) is implemented as VIC. The mGOA algorithm performance is compared using various standard benchmark test functions with the original golf optimization algorithm (GOA) and with 10 other well-known optimization algorithms, particle swarm optimization, gravitational search algorithm, and genetic algorithm. To verify the effectiveness of the proposed mGOA algorithm, it is compared with the original GOA, grey wolf optimization (GWO), and whale optimization algorithm (WOA). It is demonstrated that the objective function value decreases by 53.07%, 56.01%, and 60.53% when compared with the original GOA, WOA, and GWO, respectively. The performance of the proportional derivative with filter (PDF)-(1+PI) controller was compared with that of conventional proportional integral (PI) controllers and PID controllers based on mGOA for random load fluctuation, parametric uncertainty, reduced capacity of ESS, and various renewable generation scenarios. The simulation result indicates that the mGOA-tuned multistage controller offers improved performance of 85.65% and 82.62% in terms of minimum objective function value in comparison to the mGOA-tuned PI and PID controllers, respectively. The performance of the proposed controller is evaluated under cyber attacks like false data injection attacks and denial of service attacks, as well as time latency. Performance of the proposed controller is tested by Hardware-In-The-Loop simulation, in OPAL-RT platform.

K. Kempen, L. Thijs, J. Humbeeck et al.

M. Kazemi, Sk Faisal Kabir, E. Fini

Qi Jiang, Pei-lei Zhang, Zhishui Yu et al.

With the development of the aerospace and automotive industries, high heat exchange efficiency is a challenge facing the development of various industries. Pure copper has excellent mechanical and physical properties, especially high thermal conductivity and electrical conductivity. These excellent properties make pure copper the material of choice for the manufacture of heat exchangers and other electrical components. However, the traditional processing method is difficult to achieve the production of pure copper complex parts, so the production of pure copper parts through additive manufacturing has become a problem that must be overcome in industrial development. In this article, we not only reviewed the current status of research on the structural design and preparation of complex pure copper parts by researchers using selective laser melting (SLM), selective electron beam melting (SEBM) and binder jetting (BJ) in recent years, but also reviewed the forming, physical properties and mechanical aspects of pure copper parts prepared by different additive manufacturing methods. Finally, the development trend of additive manufacturing of pure copper parts is also prospected.

Larissa do Val Siqueira, C. F. Arias, B. Maniglia et al.

Although packaging based on starch is already being commercialized, its properties still have some disadvantages concerning conventional plastics, such as poor barrier (vapor and oxygen) and mechanical properties. Improving them is a great challenge, to know its processability and commercialization better. Currently, there is one intense quest for developing starch-based biodegradable plastics at lab scale. There are also incentives for using biodegradable packaging among government policies, sustainability actions by industries, and changes in consumer behavior. We here discuss the methods of production of starch-based biodegradable plastics, the barriers to large production, and future perspectives. Our current opinion is that much research funding is needed to overcome the challenges to a large production of these materials.

Yao Chen, Xing Peng, Ling-hua Kong et al.

Additive manufacturing (AM) technology is considered one of the most promising manufacturing technologies in the aerospace and defense industries. However, AM components are known to have various internal defects, such as powder agglomeration, balling, porosity, internal cracks and thermal/internal stress, which can significantly affect the quality, mechanical properties and safety of final parts. Therefore, defect inspection methods are important for reducing manufactured defects and improving the surface quality and mechanical properties of AM components. This paper describes defect inspection technologies and their applications in AM processes. The architecture of defects in AM processes is reviewed. Traditional defect detection technology and the surface defect detection methods based on deep learning are summarized, and future aspects are suggested.

SoYoung Park, Hyewon Lee, Mingyu Choi et al.

Anomaly segmentation is essential for industrial quality, maintenance, and stability. Existing text-guided zero-shot anomaly segmentation models are effective but rely on fixed prompts, limiting adaptability in diverse industrial scenarios. This highlights the need for flexible, context-aware prompting strategies. We propose Image-Aware Prompt Anomaly Segmentation (IAP-AS), which enhances anomaly segmentation by generating dynamic, context-aware prompts using an image tagging model and a large language model (LLM). IAP-AS extracts object attributes from images to generate context-aware prompts, improving adaptability and generalization in dynamic and unstructured industrial environments. In our experiments, IAP-AS improves the F1-max metric by up to 10%, demonstrating superior adaptability and generalization. It provides a scalable solution for anomaly segmentation across industries

P. Vijaya Bharati, J. S. V. Siva Kumar, Sathish K Anumula et al.

Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning algorithms, predictive maintenance allows industrial systems to predict failures and optimize machines life. This paper presents the synergy between the Internet of Things and predictive maintenance in industrial engineering with an emphasis on the technologies, methodologies, as well as data analytics techniques, that constitute the integration. A systematic collection, processing, and predictive modeling of data is discussed. The outcomes emphasize greater operational efficiency, decreased downtime, and cost-saving, which makes a good argument as to why predictive maintenance should be implemented in contemporary industries.

Anastasia Zhukova, Christian E. Matt, Bela Gipp

Domain-adaptive continual pretraining (DAPT) is a state-of-the-art technique that further trains a language model (LM) on its pretraining task, e.g., masked language modeling (MLM), when common domain adaptation via LM fine-tuning is not possible due to a lack of labeled task data. Although popular, MLM requires a significant corpus of domain-related data, which is difficult to obtain for specific domains in languages other than English, such as the process industry in the German language. This paper introduces an efficient approach called ICL-augmented pretraining or ICL-APT that leverages in-context learning (ICL) and k-nearest neighbors (kNN) to augment target data with domain-related and in-domain texts, significantly reducing GPU time while maintaining strong model performance. Our results show that the best configuration of ICL-APT performed better than the state-of-the-art DAPT by 28.7% (7.87 points) and requires almost 4 times less GPU-computing time, providing a cost-effective solution for industries with limited computational capacity. The findings highlight the broader applicability of this framework to other low-resource industries, making NLP-based solutions more accessible and feasible in production environments.

Rituraj Singh, Sachin Pawar, Girish Palshikar

Commonsense knowledge bases (KB) are a source of specialized knowledge that is widely used to improve machine learning applications. However, even for a large KB such as ConceptNet, capturing explicit knowledge from each industry domain is challenging. For example, only a few samples of general {\em tasks} performed by various industries are available in ConceptNet. Here, a task is a well-defined knowledge-based volitional action to achieve a particular goal. In this paper, we aim to fill this gap and present a weakly-supervised framework to augment commonsense KB with tasks carried out by various industry groups (IG). We attempt to {\em match} each task with one or more suitable IGs by training a neural model to learn task-IG affinity and apply clustering to select the top-k tasks per IG. We extract a total of 2339 triples of the form $\langle IG, is~capable~of, task \rangle$ from two publicly available news datasets for 24 IGs with the precision of 0.86. This validates the reliability of the extracted task-IG pairs that can be directly added to existing KBs.