Assessing the barriers of integrating technological innovations in textiles sector: Implications towards sustainable production

Alam, Md. Fahim Bin; Hosen, Md. Imran; Mridha, Jaglul Hoque; Chowdhury, Salman Enayet; Rahman, Md. Arafat

doi:10.1016/j.grets.2023.100039

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2024

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Cited by 8 publications

References 74 publications

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Unlocking the Potential of HR Analytics in Mexican Organisations: An Analysis of Challenges and Opportunities

Olawoyin,

Islam,

Owolewa

2024

HRM, Artificial Intelligence and the Future of Work

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Unlocking the Potential of HR Analytics in Mexican Organisations: An Analysis of Challenges and Opportunities

Olawoyin,

Islam,

Owolewa

2024

HRM, Artificial Intelligence and the Future of Work

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Prediction of power consumption and its signals in sustainable turning of PH13-8Mo steel with different machine learning models

Yurtkuran,

Korkmaz,

Gupta

et al. 2024

Int J Adv Manuf Technol

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Due to extensive distribution and huge demand of energy efficient processes, the energy-saving of machining processes draws more and more attention, and a significant variety of methods have evolved to prognosis or optimise the energy consumption in machining operations. Similarly, the estimation of power consumption-cutting conditions relationships is of great importance for optimizing processing costs and for cleaner machining. Compared to traditional methods, machine learning (ML) approach is one of the effective analysis options to model machinability indicators such as cutting force, tool wear, power consumption and surface quality. In this study, PH13-8Mo stainless steel was machined with coated carbide inserts using primarily Dry, MQL, nano-Graphene + MQL, nano-hBN + MQL, Cryo, Cryo + MQL cutting environments. Power consumption and its signals during milling were measured and different machine learning models were applied to estimate the Pc. To develop the Pc model based on the ML algorithm, 70% of the power consumption data is reserved for training and 30% for testing. In all cutting environments, power consumption increased by an average of 3.14% as feed speed increased. The reduction in Pc compared to the dry cutting was calculated as an average of 2.2%, 3.17%, 2.57%, 4.88% and 5.45% for MQL, nano-Graphen + MQL, nano-hBN + MQL, Cryo, Cryo + MQL, respectively. It is seen that the developed prediction model can reflect the power consumption-parameter relationships at high accuracy.

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AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability

Wu,

Liu,

Liang

2024

J Knowl Econ

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Assessing the barriers of integrating technological innovations in textiles sector: Implications towards sustainable production

Cited by 8 publications

References 74 publications

Unlocking the Potential of HR Analytics in Mexican Organisations: An Analysis of Challenges and Opportunities

Unlocking the Potential of HR Analytics in Mexican Organisations: An Analysis of Challenges and Opportunities

Prediction of power consumption and its signals in sustainable turning of PH13-8Mo steel with different machine learning models

AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability

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