Online on-board Optimization of Cutting Parameter for Energy Efficient CNC Milling

Tapoglou, Nikolaos; Mehnen, Jörn; Butans, Jevgenijs; Morar, Nicolau Iralal

doi:10.1016/j.procir.2016.01.072

Cited by 21 publications

(3 citation statements)

References 22 publications

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“…Shin et al (2014) apresentam uma modelagem analítica na indústria de corte de metal, cujo modelo analítico pode prever um desempenho de sustentabilidade, especialmente no consumo de energia, usando a infraestrutura de big data. Tapoglou et al (2016) investigam uma nova abordagem que visa melhorar a eficiência energética de máquinas-ferramenta através da otimização on-line das condições de corte para usinagem. Wiktorsson et al (2018) analisam empresas sul-coreanas e suecas para definir a fabricação inteligente de alto nível e as quatro dimensões-chave que são capacitadoras e dificultadoras para uma empresa tornar-se uma fábrica inteligente.…”

Section: Relação Entre Indústria 40 E Sustentabilidadeunclassified

relação da Indústria 4.0 com a sustentabilidade: uma revisão

Carvalho

Zarelli

Dalarosa

et al. 2022

e-TECH

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Com a chegada da Quarta Revolução Industrial, novos modelos de negócio surgem, o que permite que se aprimorem os processos de produção, criando-se módulos de fábrica descentralizados e independentes. No entanto, isso requer um período de adaptação e uma ampla compreensão das tecnologias capacitadoras, bem como dos métodos e ferramentas. Nesse contexto, o presente artigo teve como objetivo analisar sistematicamente os estudos publicados sobre o tema Indústria 4.0 (I4) e sua integração com a sustentabilidade no contexto empresarial. Para o levantamento dos artigos analisados, realizaram-se buscas nas bases de dados Science Direct, Scopus e Web of Science, utilizando-se as palavras-chave: “management AND factory 4.0 OR fourth industrial revolution OR smart manufacturing AND sustainability” e filtragem pela categoria de artigos. Obteve-se 342 artigos sobre o tema Indústria 4.0, destes, 54 com maior ênfase na sustentabilidade econômica e ambiental. Estudos futuros que abordem a I4 com a perspectiva social são recomendados.

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Section: Relação Entre Indústria 40 E Sustentabilidadeunclassified

relação da Indústria 4.0 com a sustentabilidade: uma revisão

Carvalho

Zarelli

Dalarosa

et al. 2022

e-TECH

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“…the real-time machining signals may be a future research area because the optimal cutting parameter can be adjusted in accordance with the real-time machining conditions [94].…”

Section: Recommendationsmentioning

confidence: 99%

Energy efficient cutting parameter optimization

Chen

Tang

et al. 2021

Front. Mech. Eng.

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Mechanical manufacturing industry consumes substantial energy with low energy efficiency. Increasing pressures from energy price and environmental directive force mechanical manufacturing industries to implement energy efficient technologies for reducing energy consumption and improving energy efficiency of their machining processes. In a practical machining process, cutting parameters are vital variables set by manufacturers in accordance with machining requirements of workpiece and machining condition. Proper selection of cutting parameters with energy consideration can effectively reduce energy consumption and improve energy efficiency of the machining process. Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimization, and a large amount of literature have been published. This paper conducts a comprehensive literature review of current studies on energy efficient cutting parameter optimization to fully understand the recent advances in this research area. The energy consumption characteristics of machining process are analyzed by decomposing total energy consumption into electrical energy consumption of machine tool and embodied energy of cutting tool and cutting fluid. Current studies on energy efficient cutting parameter optimization by using experimental design method and energy models are reviewed in a comprehensive manner. Combined with the current status, future research directions of energy efficient cutting parameter optimization are presented.

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“…These models are adjusted individually and optimized using a divide-andconquer strategy, aiming for minimal energy usage. Similarly, other approaches focused on optimizing milling parameters through an intelligent CNC controller that adjusts cutting parameters in real time based on sensor data, thereby reducing energy consumption and tool wear [5]. Additional extensions also address a cloud environment to monitor production and optimally distribute jobs, intending to enhance the determination of milling parameters for energy efficiency [6].…”

Section: Introductionmentioning

confidence: 99%

Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability

Bott,

Anderlik,

Ströbel

et al. 2024

Machines

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This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes.

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Online on-board Optimization of Cutting Parameter for Energy Efficient CNC Milling

Cited by 21 publications

References 22 publications

relação da Indústria 4.0 com a sustentabilidade: uma revisão

relação da Indústria 4.0 com a sustentabilidade: uma revisão

Energy efficient cutting parameter optimization

Framework for Holistic Online Optimization of Milling Machine Conditions to Enhance Machine Efficiency and Sustainability

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