Engineering Proceedings

Editorial

Jump to: Other

1 pages, 158 KB

Open AccessEditorial

Statement of Peer Review

by Anthony Xavior Michael, Prasad K. D. V. Yarlagadda, Andre D. L. Batako and Jose Machado

Eng. Proc. 2026, 130(1), 10; https://doi.org/10.3390/engproc2026130010 - 29 Apr 2026

Viewed by 283

Abstract

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by [...] Read more.

In submitting conference proceedings to Engineering Proceedings, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors strictly following the policies announced on the conference website [...] Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

Other

Jump to: Editorial

15 pages, 3256 KB

Open AccessProceeding Paper

Applications of Sustainable Bio-Degradable Agro-Waste (Rice Husk Ash) in Improving the Flow and Mechanical Properties of Ultra-High-Strength Mortar

by Gayathri Devi Muthiah Pillai, Karthikeyan Balasubramanian, Chandrasekar Sivaraman, Vedhaa Thanjavur Ananda Kumar, Rathnapriya Chidambaranathan and Vivek Subramanian Shanmugapuram

Eng. Proc. 2026, 130(1), 1; https://doi.org/10.3390/engproc2026130001 - 19 Mar 2026

Viewed by 367

Abstract

This study aims to develop a sustainable fibre-reinforced, high-strength mortar (UHSM) using Rice Husk Ash (RHA), cement, and steel fibres, with a view to developing a high-strength mortar that can be utilized for repair work in major industries where cracks occur due to [...] Read more.

This study aims to develop a sustainable fibre-reinforced, high-strength mortar (UHSM) using Rice Husk Ash (RHA), cement, and steel fibres, with a view to developing a high-strength mortar that can be utilized for repair work in major industries where cracks occur due to vibrations and thermal conditions. RHA was used in 20%, 30%, and 40% replacement levels of cement. Steel fibres were used at a constant dosage of 1.5%, and a very low w/c of 0.25 was adopted. Five different types of curing conditions, namely 1-day hot water curing, 1-day oven curing and 7-day normal water curing, 1-day oven curing and 28-day normal water curing, and 7-day normal water curing and 28-day normal water curing, were adopted. The mechanical behaviour of the mortar was evaluated using a compressive strength test and a split tensile test, and a statistical analysis was done using two-way ANOVA. Results revealed that the replacement levels up to 30% yielded better strength results, and there was indeed a significant effect of the curing conditions. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

14 pages, 1656 KB

Open AccessProceeding Paper

Reducing Carbon Emissions in Shoe Manufacturing Through Digital Twin-Enabled Project Management

by Mohan Reddy Devireddy, Arivazhagan Anbalagan, Shone George, Marcos Kauffman and Tengfei Long

Eng. Proc. 2026, 130(1), 3; https://doi.org/10.3390/engproc2026130003 - 25 Mar 2026

Viewed by 696

Abstract

This research addresses the urgent need to reduce carbon emissions in the footwear manufacturing industry by utilizing digital twin technology with project management frameworks. It focuses on identifying critical emission sources across the entire life cycle of shoe production from (i) material sourcing, [...] Read more.

This research addresses the urgent need to reduce carbon emissions in the footwear manufacturing industry by utilizing digital twin technology with project management frameworks. It focuses on identifying critical emission sources across the entire life cycle of shoe production from (i) material sourcing, (ii) manufacturing, and (iii) transportation, to (iv) end-of-life disposal. By data collection, infusing project management, and integrating digital twin approaches, the study offers a dynamic, data-driven method to simulate, monitor, and optimize carbon reduction strategies in real time. An extensive literature review and industry data analysis informs the assessment of carbon emissions and energy consumption patterns. Based on these insights, a tailored project management approach is followed to analyze the feasibility of the footwear sector to adopt sustainable practices such as renewable energy adoption, eco-friendly material sourcing, and closed-loop production systems. Validation was conducted using plant simulation software to model emissions scenarios and evaluate the effectiveness of proposed interventions. Case studies from leading brands, including Nike, Adidas, and Puma, were examined for Scope 1, 2 and 3, to extract the best practices and strategic insights. The research underscores the importance of combining digital tools with sustainability goals to create an environmentally conscious manufacturing ecosystem, highlights the role of policymakers in incentivizing green practices, and emphasizes collaborative industry efforts to accelerate change. The paper concludes by highlighting that digital twin systems provide effective, scalable solutions for reducing carbon emissions in footwear manufacturing. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

13 pages, 1631 KB

Open AccessProceeding Paper

Blockchain-Based Smart Contract in Three-Echelon Perishable Food Supply Chain

by Malleswari Karanam and Krishnanand Lanka

Eng. Proc. 2026, 130(1), 4; https://doi.org/10.3390/engproc2026130004 - 25 Mar 2026

Viewed by 594

Abstract

The agriculture sector plays a pivotal role in global economies, and optimizing its perishable food supply chain (PFSC) is vital to ensuring food security and transparency. The purpose of the study is to develop a blockchain-based smart contract to secure and provide transparency [...] Read more.

The agriculture sector plays a pivotal role in global economies, and optimizing its perishable food supply chain (PFSC) is vital to ensuring food security and transparency. The purpose of the study is to develop a blockchain-based smart contract to secure and provide transparency about perishable goods in the PFSC while delivering the goods between the stakeholders, such as farmers, mandis, and wholesalers. The study enhances collaboration between stakeholders by implementing smart contracts. The delivery status and the transactions have been safely recorded and verified by the stakeholder in the PFSC to ensure data integrity all the way through. The blockchain application has reduced fraud and streamlined the flow of goods and information. Moreover, this study emphasizes providing farmers with a straightforward route to the market to empower them. The benefits for the stakeholders are optimizing inventory control and developing appropriate decision-making skills. A three-echelon PFSC can become more resilient and is able to meet changing market demands by implementing blockchain-based smart contracts. Finally, the study employs blockchain technology to establish a decentralized and efficient PFSC, confirming a tamper-resistant system and enhancing stakeholder trust and collaboration. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

14 pages, 4711 KB

Open AccessProceeding Paper

Electrical Discharge Coating Variables Multi-Criteria Optimisation Utilising TOPSIS Method on the Wear Behaviour of WS₂-Cu Coating on AA7075 Alloy

by Natarajan Senthilkumar, Ganapathy Perumal, Kothandapani Shanmuga Elango, Subramanian Thirumalvalavan and Saminathan Selvarasu

Eng. Proc. 2026, 130(1), 5; https://doi.org/10.3390/engproc2026130005 - 8 Apr 2026

Viewed by 421

Abstract

Aluminium alloys are extensively considered in aviation and automobiles owing to their lightweight properties and favourable specific strength-to-weight ratio. Generally, the poor surface properties of these alloys limit their application, particularly in sliding conditions. To enhance the surface qualities, particularly the material’s wear [...] Read more.

Aluminium alloys are extensively considered in aviation and automobiles owing to their lightweight properties and favourable specific strength-to-weight ratio. Generally, the poor surface properties of these alloys limit their application, particularly in sliding conditions. To enhance the surface qualities, particularly the material’s wear resilient features, a unique surface modification process using electro-discharge coating (EDC) has been employed. This work investigates the optimisation of coating variables produced by the EDC technique utilising green compact electrodes composed of 50 wt.% tungsten disulfide (WS₂) and 50 wt.% copper (Cu) powder. The substrate material utilised was AA7075 alloy. The Taguchi–TOPSIS approach was employed to determine optimal EDC process variables, with pulse-on time (T_on), current (I_p), and pulse-off time (T_off). Wear rate (WR), surface roughness (SR), and friction coefficient (CoF) were used to assess the coating features. A wear study was performed with a pin-on-disc device with an undeviating sliding speed (0.25 m/s) and a 25 N load. The results revealed that the supreme features derived from the linear plots were I_p (4 A), T_on (80 µs), and T_off (5 µs). The ANOVA found that I_p had the utmost significant impact, accounting for 44.09%; T_off, 28.01%; T_on, 20.33%; and minimum error, 8.58%. A validation trial with perfect parameters returned values of 0.000179 mm³/Nm (WR), 0.204 (CoF), and 2.818 µm (SR). These findings are significantly better than those of the other coatings. The discrepancy among the estimated and experimental relative closeness in optimal settings is 6.34%, demonstrating that the Taguchi–TOPSIS method is more appropriate for multi-criteria optimisation. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

6 pages, 1290 KB

Open AccessProceeding Paper

Influence of Density and Porosity on the Mechanical Properties of ZE41 Hybrid Metal Matrix Composites

by Anand Narayanan Nair and Senthil Kumaran Selvaraj

Eng. Proc. 2026, 130(1), 6; https://doi.org/10.3390/engproc2026130006 - 16 Apr 2026

Viewed by 272

Abstract

In this research, the effects of density and porosity on the mechanical properties of a stir-cast hybrid magnesium ZE41 alloy strengthened with 2% weight of silicon carbide (SiC) and boron carbide (B₄C) are assimilated. The experimental and theoretical densities of the [...] Read more.

In this research, the effects of density and porosity on the mechanical properties of a stir-cast hybrid magnesium ZE41 alloy strengthened with 2% weight of silicon carbide (SiC) and boron carbide (B₄C) are assimilated. The experimental and theoretical densities of the ZE41 hybrid matrix were found and compared. From the results of density analysis, it can be inferred that the experimental density of hybrid matrix is smaller when compared to the pure ZE41 matrix. The percentage porosity of hybrid matrix was also analyzed, and it was observed that the hybrid matrix has a slight increase in porosity when compared to the pure ZE41 matrix. The ultimate strength and hardness of the ZE41 hybrid matrix have increased significantly due to its moderate density and acceptable porosity values. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

14 pages, 4638 KB

Open AccessProceeding Paper

Digital Twin-Driven Evaluation of 3D-Printed H13 Tool Steel End Mills for Sustainable Machining Applications

by Arivazhagan Anbalagan, Kaartikeyan Ramesh, Jeyapandiarajan Paulchamy, Michael Anthony Xavior, Shone George and Marcos Kauffman

Eng. Proc. 2026, 130(1), 7; https://doi.org/10.3390/engproc2026130007 - 17 Apr 2026

Viewed by 433

Abstract

This study investigates the failure mechanisms and machining performance of 3D-printed H13 tool steel end mills driven by the creation of a Finite Element Analysis (FEA)-based digital twin. The primary objective is to assess the process capability of these tools by integrating CAD [...] Read more.

This study investigates the failure mechanisms and machining performance of 3D-printed H13 tool steel end mills driven by the creation of a Finite Element Analysis (FEA)-based digital twin. The primary objective is to assess the process capability of these tools by integrating CAD and FEA with product design simulation-based data acquisition within a digital manufacturing framework, thereby validating a physical model. This research begins by redesigning a 20 mm end mill into a 6 mm, four-flute configuration, and then FEA simulating H13 tool steel and tungsten carbide (WC) tools. This is carried out to machine Al-6082-T6 under spindle speeds of 5500 rpm and 1500 rpm, with a constant feed rate of 0.5 mm/tooth over 100,000 cycles. The process is integrated with the Siemens Insights hub via Node-RED to replicate the simulation to correlate the CPU signal spikes and enhanced processing capacity, especially in relation to CAD/CAE kernel activities. Based on the simulation insights, two H13 end mills are fabricated using Fused Filament Fabrication (FFF). The first tool, tested at 5500 rpm and a 1100 mm/min feed rate, fractured after 70 mm of cutting. The second trial, using a diamond-coated solid carbide tool at 1500 rpm and 300 mm/min, achieved successful machining with graphene-enhanced coolant. The cutting forces ranged from 300 to 500 N for 3D-printed tools, compared with 150–180 N for the carbide tool. The surface roughness varied between 0.6–1 µm and 4–6 µm for the printed tools, aligning closely with traditional tools (0.5–1 µm). Post-machining analysis using SEM and EDX confirmed tool wear and material changes. This work adopted a methodology to capture and monitor CPU signal spikes via the digital twin platform, enabling real-time comparison with failure thresholds. The results demonstrate the feasibility of using 3D-printed H13 tools for sustainable, customizable machining, offering a pathway for industries to adopt in-house tool design and manufacturing with integrated digital validation. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

14 pages, 2117 KB

Open AccessProceeding Paper

Cutting Performance and Damage Metrics in Abrasive Waterjet Machining of Delrin–Ramie Fiber Composites

by Natarajan Senthilkumar, Subramanian Thirumalvalavan, Saminathan Selvarasu and Ganapathy Perumal

Eng. Proc. 2026, 130(1), 8; https://doi.org/10.3390/engproc2026130008 - 17 Apr 2026

Viewed by 363

Abstract

In this study, Delrin^® (POM) polymer was reinforced with 15 wt.% chopped ramie fiber (RF) to develop a sustainable composite, which was injection-molded and machined using abrasive waterjet machining (AWJM). SEM revealed a skin-core morphology with flow-induced RF alignment and small voids [...] Read more.

In this study, Delrin^® (POM) polymer was reinforced with 15 wt.% chopped ramie fiber (RF) to develop a sustainable composite, which was injection-molded and machined using abrasive waterjet machining (AWJM). SEM revealed a skin-core morphology with flow-induced RF alignment and small voids at bundle crossovers, indicating interfacial adhesion. A Taguchi L₉ (3³) design evaluated waterjet pressure (WJP: 100–300 MPa), traverse speed (TS: 100–200 mm/min), and stand-off distance (SoD: 1–3 mm) on kerf width (KW) and surface roughness (SR). Increasing WJP from 100 to 300 MPa lowered mean SR from 6.23 to 4.80 µm (23% reduction) and KW from 1.31 to 1.07 mm (reduction of 18%); enlarging SoD from 1 to 3 mm raised SR from 4.98 to 5.55 µm (an 11% increase) and KW from 1.12 to 1.20 mm (a of 7% increase); and raising TS from 100 to 200 mm/min narrowed KW from 1.24 to 1.11 mm (a 10.5% reduction) with a modest SR decrease from 5.45 to 5.28 µm. ANOVA confirmed WJP as the dominant factor for SR (79.8%), as well as a significant SoD (18.3%). For KW, the influence of WJP (68.8%) was substantial, followed by TS (19.9%) and SoD (11%). Linear models captured the trends well (SR: R² = 88.29%; KW: R² = 93.36%). A desirability-based multi-response optimizer yielded ideal conditions for TS (200 mm/min), WJP (300 MPa), and SoD (1 mm), predicting a KW of 0.94 mm and an SR of 4.1567 µm. Confirmation tests produced a KW (0.970 ± 0.01 mm) and SR (4.27 ± 0.05 µm), which are within 3.19% and 2.73% of the predicted values, validating the DoE regression approach. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

16 pages, 2289 KB

Open AccessProceeding Paper

An Efficient Hybrid Framework for Weld Defect Detection Using GAN, CNN and XGBoost

by Kalyanaraman Pattabiraman, Ashish Patil, Yash Gulavani, Ritik Malik and Atharva Gai

Eng. Proc. 2026, 130(1), 9; https://doi.org/10.3390/engproc2026130009 - 22 Apr 2026

Viewed by 401

Abstract

Automated detection of defects in welds are inevitable in the assurance of structural integrity, but this faces serious challenges due to the microscopic characteristics of the discontinuities, low visual contrast and infrequent occurrence of defect samples. Conventional deep learning methods, while accurate, often [...] Read more.

Automated detection of defects in welds are inevitable in the assurance of structural integrity, but this faces serious challenges due to the microscopic characteristics of the discontinuities, low visual contrast and infrequent occurrence of defect samples. Conventional deep learning methods, while accurate, often lack interpretability and exhibit low recall for rare defects. This paper proposes a novel hybrid system combining a Generative Adversarial Network (GAN), a Convolutional Neural Network (CNN), and Extreme Gradient Boosting (XGBoost 2.0.0) to enhance weld defect classification performance and transparency. Firstly, a Deep Convolutional GAN (DCGAN) creates synthetic images of the minority classes; thus, the problem of class imbalance is resolved. Then, a pretrained ResNet50V2 CNN is used to extract features of the deep layers from the original images as well as from the generated ones. After that, these features are fed into an XGBoost classifier, which uses tree-based learning to optimize classification results and make the process more understandable to the user. Furthermore, interpretation is also facilitated by Grad-CAM rendering of the CNN regions of interest and SHAP analysis to measure the involvement of the features in XGBoost. Experiments using the available LoHi-WELD datasets show that the overall accuracy is significantly improved, the per-class recall of the rare defects is also enhanced, and the robustness is also improved. The proposed hybrid method not only achieves better results but also generates visual/explainable output, which is very valuable when the system is implemented in industrial welding inspection systems. This paper serves as a liaison between the latest AI technology and the practical interpretability requirements of the mechanical and welding engineering fields. Full article

(This article belongs to the Proceedings of The 19th Global Congress on Manufacturing and Management (GCMM 2025))

► Show Figures

Figure 1

Journal Menu

Journal Browser

Eng. Proc., 2026, GCMM 2025

The 19th Global Congress on Manufacturing and Management (GCMM 2025)

Editorial

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI