Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach DOI Creative Commons
Anna Krolik, Radosław Drelich, Michał Pakuła

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10638 - 10638

Published: Nov. 18, 2024

This paper presents the use of noncontact ultrasound for nondestructive detection defects in two plastic plates made polyamide (PA6) and polyethylene (PE). The aim study was to: (1) assess presence as well their size, type, orientation based on amplitudes Lamb ultrasonic waves measured (PE) due to homogeneous internal structure, which mainly determined selection such model materials testing; (2) verify possibilities building automatic quality control defect systems ML results above-mentioned studies within Industry 4.0/5.0 paradigm. Tests were conducted with generated synthetic resembling found real delamination cracking at edge plate a crack (discontinuity) center plate. Defect sizes ranged from 1 mm 15 mm. Probes 30 kHz used excite slab material. method is sensitive slightest changes material integrity. A significant decrease signal amplitude observed, even few millimeters length. In addition traditional methods, machine learning (ML) analysis, allowing an initial assessment method’s potential cyber-physical digital twins. By training models data, algorithms can distinguish subtle differences between signals reflected normal defective areas types voids, cracks, or weak bonds often produce distinct acoustic signatures, learn recognize high accuracy. Using techniques like feature extraction, process these high-dimensional datasets, identifying patterns that human inspectors might overlook. Furthermore, are adaptable, same trained work various batches panel minimal retraining. combination automation precision significantly enhances reliability efficiency industrial manufacturing settings. achieved accuracy results, 0.9431 classification 0.9721 prediction, comparable better than AI-based other noninvasive methods flat surface detection, presented method, they first described this way. approach demonstrates novelty contribution artificial intelligence (AI) tools, extending automating existing applications methods. susceptibility augmentation by AI/ML may represent important new property crucial assessing suitability future applications.

Language: Английский

Empowering Precision Medicine: The Impact of 3D Printing on Personalized Therapeutic DOI Creative Commons
Lorca Alzoubi, Alaa A. A. Aljabali, Murtaza M. Tambuwala

et al.

AAPS PharmSciTech, Journal Year: 2023, Volume and Issue: 24(8)

Published: Nov. 14, 2023

Abstract This review explores recent advancements and applications of 3D printing in healthcare, with a focus on personalized medicine, tissue engineering, medical device production. It also assesses economic, environmental, ethical considerations. In our the literature, we employed comprehensive search strategy, utilizing well-known databases like PubMed Google Scholar. Our chosen keywords encompassed essential topics, including printing, nanotechnology, related areas. We first screened article titles abstracts then conducted detailed examination selected articles without imposing any date limitations. The for inclusion, comprising research studies, clinical investigations, expert opinions, underwent meticulous quality assessment. methodology ensured incorporation high-quality sources, contributing to robust exploration role realm healthcare. highlights printing's potential customized drug delivery systems, patient-specific implants, prosthetics, biofabrication organs. These innovations have significantly improved patient outcomes. Integration nanotechnology has enhanced precision biocompatibility. demonstrates cost-effectiveness sustainability through optimized material usage recycling. healthcare sector witnessed remarkable progress promoting patient-centric approach. From implants radiation shielding offers tailored solutions. Its transformative applications, coupled economic viability sustainability, revolutionize Addressing biocompatibility, standardization, concerns is responsible adoption. Graphical

Language: Английский

Citations

53
Managing the race to the moon: Global policy and governance in Artificial Intelligence regulation—A contemporary overview and an analysis of socioeconomic consequences DOI Creative Commons
Yoshija Walter

Discover Artificial Intelligence, Journal Year: 2024, Volume and Issue: 4(1)

Published: Feb. 26, 2024

Abstract This paper delves into the complexities of global AI regulation and governance, emphasizing socio-economic repercussions rapid development. It scrutinizes challenges in creating effective governance structures amidst race, considering diverse perspectives policies. The discourse moves beyond specific corporate examples, addressing broader implications sector-wide impacts on employment, truth discernment, democratic stability. analysis focuses contrasting regulatory approaches across key regions—the United States, European Union, Asia, Africa, Americas thus highlighting variations commonalities strategies implementations. comparative study reveals intricacies hurdles formulating a cohesive policy for regulation. Central to is examination dynamic between innovation slower pace ethical standard-setting. critically evaluates advantages drawbacks shifting responsibilities government bodies private sector. In response these challenges, discussion proposes an innovative integrated model. model advocates collaborative network that blends governmental authority with industry expertise, aiming establish adaptive, responsive regulations (called “dynamic laws”) can evolve technological advancements. novel approach aims bridge gap advancements essential processes law-making.

Language: Английский

Citations

27
Generative AI in AI-Based Digital Twins for Fault Diagnosis for Predictive Maintenance in Industry 4.0/5.0 DOI Creative Commons
Emilia Mikołajewska, Dariusz Mikołajewski, Tadeusz Mikołajczyk

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(6), P. 3166 - 3166

Published: March 14, 2025

Generative AI (GenAI) is revolutionizing digital twins (DTs) for fault diagnosis and predictive maintenance in Industry 4.0 5.0 by enabling real-time simulation, data augmentation, improved anomaly detection. DTs, virtual replicas of physical systems, already use generative models to simulate various failure scenarios rare events, improving system resilience prediction accuracy. They create synthetic datasets that improve training quality while addressing scarcity imbalance. The aim this paper was present the current state art perspectives using AI-based DTs 4.0/5.0. With GenAI, enable proactive minimize downtime, their latest implementations combine multimodal sensor generate more realistic actionable insights into performance. This provides operational profiles, identifying potential traditional methods may miss. New area include incorporation Explainable (XAI) increase transparency decision-making reliability key industries such as manufacturing, energy, healthcare. As emphasizes a human-centric approach, DT can seamlessly integrate with human operators support collaboration decision-making. implementation edge computing increases scalability capabilities smart factories industrial Internet Things (IoT) systems. Future advances federated learning ensure privacy exchange between enterprises diagnostics, evolution GenAI alongside ensuring long-term validity. However, challenges remain managing computational complexity, security, ethical issues during implementation.

Language: Английский

Citations

3
3D PRINTING IN THE PHARMACEUTICAL INDUSTRY: A SPECIAL CONSIDERATION ON MEDICAL DEVICE AND ITS APPLICATIONS DOI Open Access

VIVEKANANDAN ELANGO,

M. Murugappan,

KARTHIKEYAN VETRIVEL

et al.

International Journal of Applied Pharmaceutics, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 11

Published: Jan. 7, 2025

3 Dimensional (3D) printing has seemed to be the technology of radical development for pharmaceutical industry, particularly in medical device manufacturing. The current review elaborates on applications 3D printing, challenges, and potentials devices. allows complicated personalized devices with accuracy cost-effectiveness as never before, bringing key this fields prostheses, orthoses, surgical guides, audiology devices, bioresorbable implants. It brings along customization, better pre-operative planning, new drug delivery systems, but there are quality control regulatory challenges faced: material selection, process validation, sterilization, scalability. In view upcoming technology, bodies having update their guidelines ensure continued safety efficacy. On road ahead, artificial intelligence, nanotechnology, 4 (4D) future developments could make sophisticated equipment change management outcome diseases. While opens up newer routes innovation major concerns issues scalability matters. This will thus a significant impact healthcare through these coming decades, changes global research landscapes.

Language: Английский

Citations

2
Advanced Deep Learning Algorithms for Energy Optimization of Smart Cities DOI Creative Commons
Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 407 - 407

Published: Jan. 18, 2025

Advanced deep learning algorithms play a key role in optimizing energy usage smart cities, leveraging massive datasets to increase efficiency and sustainability. These analyze real-time data from sensors IoT devices predict demand, enabling dynamic load balancing reducing waste. Reinforcement models optimize power distribution by historical patterns adapting changes real time. Convolutional neural networks (CNNs) recurrent (RNNs) facilitate detailed analysis of spatial temporal better usage. Generative adversarial (GANs) are used simulate scenarios, supporting strategic planning anomaly detection. Federated ensures privacy-preserving sharing distributed systems, promoting collaboration without compromising security. technologies driving the transformation towards sustainable energy-efficient urban environments, meeting growing demands modern cities. However, there is view that if pace development maintained with large amounts data, computational/energy costs may exceed benefits. The article aims conduct comparative assess potential this group technologies, taking into account efficiency.

Language: Английский

Citations

2
Shaping the Future of Cardiovascular Disease by 3D Printing Applications in Stent Technology and its Clinical Outcomes DOI
Muneeb Ullah,

Ayisha Bibi,

Abdul Wahab

et al.

Current Problems in Cardiology, Journal Year: 2023, Volume and Issue: 49(1), P. 102039 - 102039

Published: Aug. 19, 2023

Language: Английский

Citations

31
Emerging Applications of Machine Learning in 3D Printing DOI Creative Commons
Izabela Rojek, Dariusz Mikołajewski, Marcin Kempiński

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 1781 - 1781

Published: Feb. 10, 2025

Three-dimensional (3D) printing techniques already enable the precise deposition of many materials, becoming a promising approach for materials engineering, mechanical or biomedical engineering. Recent advances in 3D scientists and engineers to create models with precisely controlled complex microarchitecture, shapes, surface finishes, including multi-material printing. The incorporation artificial intelligence (AI) at various stages has made it possible reconstruct objects from images (including, example, medical images), select optimize process, monitor lifecycle products. New emerging opportunities are provided by ability machine learning (ML) analyze data sets learn previous (historical) experience predictions dynamically individuate products processes. This includes synergistic capabilities ML development personalized

Language: Английский

Citations

1
Digital Twins in 3D Printing Processes Using Artificial Intelligence DOI Open Access
Izabela Rojek, Tomasz Marciniak, Dariusz Mikołajewski

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(17), P. 3550 - 3550

Published: Sept. 6, 2024

Digital twins (DTs) provide accurate, data-driven, real-time modeling to create a digital representation of the physical world. The integration new technologies, such as virtual/mixed reality, artificial intelligence, and DTs, enables research into ways achieve better sustainability, greater efficiency, improved safety in Industry 4.0/5.0 technologies. This paper discusses concepts, limitations, future trends, potential directions infrastructure underlying intelligence for large-scale semi-automated DT building environments. Grouping these technologies along lines allows consideration their individual risk factors use available data, resulting an approach generate holistic virtual representations facilitate predictive analyses industrial practices. Artificial intelligence-based DTs are becoming tool monitoring, simulating, optimizing systems, widespread implementation mastery this technology will lead significant improvements performance, reliability, profitability. Despite advances, aforementioned still requires research, improvement, investment. article’s contribution is concept that, if adopted instead traditional approach, can become standard practice rather than advanced operation accelerate development.

Language: Английский

Citations

4
A Breakthrough in Producing Personalized Solutions for Rehabilitation and Physiotherapy Thanks to the Introduction of AI to Additive Manufacturing DOI Creative Commons
Emilia Mikołajewska, Dariusz Mikołajewski, Tadeusz Mikołajczyk

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 2219 - 2219

Published: Feb. 19, 2025

The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state knowledge perspectives using solutions for physiotherapy thanks introduction AI AM. Advanced algorithms analyze patient-specific data such as body scans, movement patterns, medical history design customized assistive devices, orthoses, prosthetics. synergy enables rapid prototyping production highly optimized improving comfort, functionality, therapeutic outcomes. Machine learning (ML) models further streamline process by anticipating biomechanical needs adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative topology optimization create lightweight yet durable structures that are ideally suited patient’s anatomy goals .AI-based AM also facilitates multi-material devices combine flexibility, strength, sensory capabilities, improved monitoring support during therapy. New include integrating smart sensors printed real-time collection feedback loops adaptive Additionally, these becoming increasingly accessible technology lowers costs improves, democratizing healthcare. Future advances could lead widespread use digital twins simulation before production. AI-based virtual reality (VR) augmented (AR) tools expected provide immersive, training environments along aids. Collaborative platforms federated can enable healthcare providers researchers securely share insights, accelerating innovation. However, challenges regulatory approval, security, ensuring equity access technologies must be addressed fully realize their potential. One major gaps lack large, diverse datasets train models, which limits ability span different demographics conditions. Integration AI–AM systems into should focus processing techniques.

Language: Английский

Citations

0
ML-Based Materials Evaluation in 3D Printing DOI Creative Commons
Izabela Rojek, Dariusz Mikołajewski, Krzysztof Galas

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(10), P. 5523 - 5523

Published: May 15, 2025

Machine learning (ML) is transforming the evaluation of 3D printing materials, enabling more efficient and accurate assessment material properties, including their sustainable life cycle. ML algorithms can analyze vast amounts data from previous processes to predict performance different materials (including those used in multi-material printing) under conditions. This predictive ability helps selecting most suitable for specific tasks, optimizing mechanical, chemical, overall quality final product. Furthermore, by integrating real-time sensors during process, continuously monitor adjust parameters, ensuring optimal utilization reducing waste. models identify correct defects printed recognizing patterns associated with defects, thus improving reliability 3D-printed objects. approach reduces need expensive time-consuming physical tests. accelerates pace development but also increases precision selection processing, contributing use energy printing.

Language: Английский

Citations

0