Synergistic Approach to Refining Classification of Cutting Defects in Broad Strip Sheet Metal: CNN SVM Integration DOI

Taifa Ayoub Mir,

Deepak Banerjee,

Rahul Chauhan

et al.

Published: July 27, 2024

Language: Английский

Recent advances in 4D printing of fiber-reinforced polymer composites: A review and outlook DOI

Wanglin Qiu,

Xuguang Xu, Ke Dong

et al.

Composites Part B Engineering, Journal Year: 2024, Volume and Issue: 283, P. 111645 - 111645

Published: July 2, 2024

Language: Английский

Citations

21
Advanced 3D-printed PVDF/BT piezoelectric energy harvester with a bio-inspired 3D structure for a self-powered smart mouse DOI Creative Commons
Amal Megdich,

Mohamed Habibi,

Luc Laperrière

et al.

Nano Energy, Journal Year: 2024, Volume and Issue: 128, P. 109876 - 109876

Published: June 10, 2024

Language: Английский

Citations

9
Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading DOI Creative Commons

Ahmed Ghazi Abdulameer,

Muhannad M. Mrah,

Maryam Bazerkan

et al.

Discover Materials, Journal Year: 2025, Volume and Issue: 5(1)

Published: Jan. 11, 2025

Abstract The increasing demand for advanced materials capable of withstanding extreme loading conditions, such as those encountered during impact or blast events, underscores the need innovative approaches in material processing. This study focuses on leveraging machine learning (ML) to enhance predictive accuracy continuous extrusion CP-Titanium Grade 2, a vital structural resilience critical applications. Specifically, an Artificial Neural Network (ANN) model optimized using Stochastic Gradient Descent (SGD) was introduced forecast power requirements with high precision. analysis utilized published dataset that comprises theoretical, numerical, and experimental calculations robust foundation validation comparison. A visualization highlighted influence process parameters, feedstock temperature wheel velocity, performance align thematic focus resilient design. ANN-SGD achieved RMSE 0.9954 CVRMSE 11.53% which demonstrated significant improvements prediction compared traditional approaches. By achieving superior alignment results, validated its efficacy reliable efficient tool understanding optimizing complex manufacturing processes. research emphasizes potential ML revolutionize processing conditions contribute broader goals sustainable manufacturing.

Language: Английский

Citations

1
Designing advanced 4D printing thermo-sensitive shape memory polymer blends for enhanced mechanical and shape memory performances DOI

Karima Bouguermouh,

Mohamed Habibi, Luc Laperrière

et al.

Progress in Additive Manufacturing, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 30, 2025

Language: Английский

Citations

1
Enhanced piezoelectric performance of PVDF/MWCNTs energy harvester through a 3D-printed multimodal auxetic structure for smart security systems DOI
Amal Megdich,

Mohamed Habibi,

Luc Laperrière

et al.

Materials Today Sustainability, Journal Year: 2024, Volume and Issue: 27, P. 100847 - 100847

Published: May 20, 2024

Language: Английский

Citations

4
A review of machine learning in additive manufacturing: design and process DOI

Kefan Chen,

Peilei Zhang, Hua Yan

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2024, Volume and Issue: 135(3-4), P. 1051 - 1087

Published: Oct. 5, 2024

Language: Английский

Citations

4
Role of artificial intelligence in data-centric additive manufacturing processes for biomedical applications DOI

Saman Mohammadnabi,

Nima Moslemy,

Hadi Taghvaei

et al.

Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials, Journal Year: 2025, Volume and Issue: 166, P. 106949 - 106949

Published: Feb. 25, 2025

Language: Английский

Citations

0
Artificial Intelligence and Machine Learning Approaches in Eco-Design for Additive Manufacturing: A Literature Review DOI
Francesco Musiari, Marco Marconi,

Nicola Villazzi

et al.

Lecture notes in mechanical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 371 - 380

Published: Jan. 1, 2025

Language: Английский

Citations

0
Advanced nanocomposites for 4D printing: High-performance electroactive shape memory polymers for smart applications DOI Creative Commons
Amal Megdich,

Mohamed Habibi,

Luc Laperrière

et al.

Applied Materials Today, Journal Year: 2025, Volume and Issue: 44, P. 102702 - 102702

Published: April 3, 2025

Language: Английский

Citations

0
Sustainable and Optimized Production in an Aluminum Extrusion Process DOI Open Access

A. Filipe Ferrás,

Fátima De Almeida, Eliana Costa e Silva

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(9), P. 4179 - 4179

Published: May 6, 2025

In discussions on environmental policies, eco-efficiency is often underlined. Eco-efficiency defined as delivering products and services with competitive value while simultaneously reducing the ecological impacts meeting human needs. highly industrial environments, improvements in production processes are crucial for maintaining a strong differentiated position ability. Additionally, rationalizing energy consumption optimizing use of natural resources essential sustainability. This work presents an empirical study Portuguese company focused minimizing scrap extrusion processes. common challenge worldwide, significant economic implications. A literature review revealed relationships between key process parameters, including temperature, time, speed, pressure, geometry. The main objective this to model aluminum simple replicable way, avoiding complex models such nonlinear optimization or finite element methods, view toward potential machine learning applications reduction. Thus, multiple linear regression enable identification most influential variables involved process. results identify that impact generation, aligning findings from literature. dataset, geometry-related factors parameters notable rates.

Language: Английский

Citations

0