@article{siddika_sharif_hasan_2021, title={Effect of Areca and Waste Nylon Fiber Hybridization on the Properties of Recycled Polypropylene Composites}, ISSN={["1544-046X"]}, DOI={10.1080/15440478.2021.1929651}, abstractNote={ABSTRACT Present research investigated the effect of areca and waste nylon fiber hybridization on the properties of areca and waste nylon fiber-reinforced hybrid recycled polypropylene composites. The impact of fiber surface modification was also investigated by the alkali treatment of natural areca fiber. Composites were manufactured using hot press machine by creating an alternative layer of fiber matrix lamellae arrangement at four levels of fiber loading (10, 15, 20, and 25 wt%). Areca and waste nylon fiber ratio were varied at 1:3, 2:3, 3:2, and 3:1 for 20 wt% fiber-loaded composites. Tensile, flexural and hardness tests, scanning electron microscopic, and Fourier transform infrared spectroscopic analysis were conducted for characterization of the composites. Fourier transform infrared spectroscopic analysis of the composites indicated decrease of hemicelluloses and lignin content and corresponding improved mechanical interlocking with alkali treatment of areca fiber. Treated fiber-reinforced composite showed better mechanical properties in comparison with the untreated ones. Tensile test of composites showed a decreasing trend of tensile strength after 10 wt% fiber loading. Whereas Young’s modulus, flexural strength, flexural modulus, and hardness values were found to be increased with the increase in fiber loading. Only tensile strength value was higher in higher areca fiber-reinforced composite. All other properties were peaked on highest waste nylon fiber-reinforced one. Scanning electron microscopic analysis indicated more uniform distribution of fibers in treated fiber-reinforced composite, while fiber agglomeration increased in higher fiber-loaded composites.}, journal={JOURNAL OF NATURAL FIBERS}, author={Siddika, Salma and Sharif, Ahmed and Hasan, Mahbub}, year={2021}, month={Jun} }
 @article{shohan_harm_hasan_starly_shirwaiker_2021, title={Non-destructive quality monitoring of 3D printed tissue scaffolds via dielectric impedance spectroscopy and supervised machine learning}, volume={53}, ISSN={["2351-9789"]}, url={http://dx.doi.org/10.1016/j.promfg.2021.06.063}, DOI={10.1016/j.promfg.2021.06.063}, abstractNote={Majority of methods currently used for quality assessment of tissue engineered medical products (TEMPs) are offline and destructive in nature, which is one of the factors impeding the scale up and translation of these technologies. In this study, we investigate quality assessment of TEMP via dielectric impedance spectroscopy (DIS) and supervised machine learning (ML) as a non-destructive alternative that requires minimal human intervention. 3D printed, NaOH-treated polycaprolactone (PCL) scaffolds seeded with human adipose-derived stem cells (hASC), NIH 3T3, MG63, and human chondrocyte cells were assessed via DIS over 4 days of in vitro culture. The results showed that the cell type and duration in culture had a significant effect on the delta permittivity (Δε, an important DIS metric. Five supervised ML algorithms – K Nearest Neighbors (KNN), Logistic Regression, Random Forest Classifiers, Support Vector Machines, and artificial neural network – were then used to analyze the comprehensive structured permittivity datasets to determine their ability to discern between different cell types and culture durations. The KNN algorithm demonstrated the best accuracy (99%). The outcomes of this study demonstrate the approach of using DIS and supervised ML in conjunction for assessment of TEMPs in an automated manufacturing system.}, journal={49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021)}, publisher={Elsevier BV}, author={Shohan, Shohanuzzaman and Harm, Jordan and Hasan, Mahmud and Starly, Binil and Shirwaiker, Rohan}, year={2021}, pages={636–643} }
 @article{hasan_starly_2020, title={Decentralized cloud manufacturing-as-a-service (CMaaS) platform architecture with configurable digital assets}, volume={56}, ISSN={["1878-6642"]}, DOI={10.1016/j.jmsy.2020.05.017}, abstractNote={Contemporary Cloud Manufacturing-as-a-Service (CMaaS) platforms now promise customers instant pricing and access to a large capacity of manufacturing nodes. However, many of the CMaaS platforms are centralized with data flowing through an intermediary agent connecting clients with service providers. This paper reports the design, implementation and validation of middleware software architectures which aim to directly connect client users with manufacturing service providers while improving transparency, data integrity, data provenance and retaining data ownership to its creators. In the first middleware, clients have the ability to directly customize and configure parts parametrically, leading to an instant generation of downstream manufacturing process plan codes. In the second middleware, clients can track the data provenance generated in a blockchain based decentralized architecture across a manufacturing system. The design of digital assets across a distributed manufacturing system infrastructure controlled by autonomous smart contracts through Ethereum based ERC-721 non-fungible tokens is proposed to enable communication and collaboration across decentralized CMaaS platform architectures. The performance of the smart contracts was evaluated on three different global Ethereum blockchain test networks with the centrality and dispersion statistics on their performance provided as a reference benchmark for future smart contract implementations.}, journal={JOURNAL OF MANUFACTURING SYSTEMS}, author={Hasan, Mahmud and Starly, Binil}, year={2020}, month={Jul}, pages={157–174} }