Enhancing cutting tool sustainability based on remaining useful life prediction

[1]  Ronald A. Kohser,et al.  DeGarmo's Materials and Processes in Manufacturing , 2020 .

[2]  Yingfeng Zhang,et al.  A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions , 2019, Journal of Cleaner Production.

[3]  Ratna Babu Chinnam,et al.  An HMM and polynomial regression based approach for remaining useful life and health state estimation of cutting tools , 2019, Comput. Ind. Eng..

[4]  Yaguo Lei,et al.  Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods , 2018, Eur. J. Oper. Res..

[5]  Mohsen S. Sajadieh,et al.  Optimisation of tool replacement time in the machining process based on tool condition monitoring using the stochastic approach , 2018, Int. J. Comput. Integr. Manuf..

[6]  Sagar N. Sakharkar,et al.  Effect of Machining Environment on Turning Performance of Austempered Ductile Iron , 2018, CIRP - Journal of Manufacturing Science and Technology.

[7]  André Luís Helleno,et al.  Sustainability evaluation model for manufacturing systems based on the correlation between triple bottom line dimensions and balanced scorecard perspectives , 2018, Journal of Cleaner Production.

[8]  Huibin Sun,et al.  A Hybrid Approach to Cutting Tool Remaining Useful Life Prediction Based on the Wiener Process , 2018, IEEE Transactions on Reliability.

[9]  Danil Yu. Pimenov,et al.  An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions , 2018, Journal of Cleaner Production.

[10]  Jose Arturo Garza-Reyes,et al.  Towards a life cycle sustainability analysis: a systematic review of approaches to sustainable manufacturing , 2018 .

[11]  Yaguo Lei,et al.  Machinery health prognostics: A systematic review from data acquisition to RUL prediction , 2018 .

[12]  J. Gokulachandran,et al.  Prediction of remaining useful life of cutting tools: a comparative study using soft computing methods , 2018 .

[13]  Changqing Liu,et al.  Real-time cutting tool state recognition approach based on machining features in NC machining process of complex structural parts , 2018 .

[14]  Wei Xue,et al.  Review of tool condition monitoring methods in milling processes , 2018 .

[15]  Dong Wang,et al.  Brownian motion with adaptive drift for remaining useful life prediction: Revisited , 2018 .

[16]  Chao Zhang,et al.  Ontology-based cutting tool configuration considering carbon emissions , 2017 .

[17]  R. Drai,et al.  A data-driven prognostic approach based on wavelet transform and extreme learning machine , 2017, 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B).

[18]  Yang Liu,et al.  Configuring use-oriented aero-engine overhaul service with multi-objective optimization for environmental sustainability , 2017 .

[19]  Wenhai Wang,et al.  Remaining useful life prediction for an adaptive skew-Wiener process model , 2017 .

[20]  Mariano Marcos Bárcena,et al.  On the sustainability of machining processes. Proposal for a unified framework through the triple bottom-line from an understanding review , 2017 .

[21]  Huibin Sun,et al.  In-process cutting tool remaining useful life evaluation based on operational reliability assessment , 2016 .

[22]  Pengyu Li,et al.  A quantitative approach to analyze carbon emissions of CNC-based machining systems , 2015, J. Intell. Manuf..

[23]  Robert X. Gao,et al.  Adaptive resampling-based particle filtering for tool life prediction , 2015 .

[24]  Sudarsan Ghosh,et al.  Application of sustainable techniques in metal cutting for enhanced machinability: a review , 2015 .

[25]  Congbo Li,et al.  Multi-objective parameter optimization of CNC machining for low carbon manufacturing , 2015 .

[26]  Le Cao,et al.  Optimal tool replacement with product quality deterioration and random tool failure , 2015 .

[27]  Moola Mohan Reddy,et al.  Environmental friendly cutting fluids and cooling techniques in machining: a review , 2014 .

[28]  Jan-Eric Ståhl,et al.  Sustainable machining through increasing the cutting tool utilization , 2013 .

[29]  Vimal Dhokia,et al.  Environmentally conscious machining of difficult-to-machine materials with regard to cutting fluids , 2012 .

[30]  L. Martinova,et al.  Diagnostics and forecasting of cutting tool wear at CNC machines , 2012 .

[31]  Paul Mativenga,et al.  Sustainable machining: selection of optimum turning conditions based on minimum energy considerations , 2010 .

[32]  Jeffrey B Dahmus,et al.  Thermodynamic analysis of resources used in manufacturing processes. , 2009, Environmental science & technology.

[33]  Hossam A. Kishawy,et al.  Towards sustainability assessment of machining processes , 2018 .

[34]  Fazleena Badurdeen,et al.  Sustainable Manufacturing Performance Evaluation: Integrating Product and Process Metrics for Systems Level Assessment , 2017 .

[35]  Soumaya Yacout,et al.  Cutting tool remaining useful life during turning of metal matrix composites , 2016, 2016 Annual Reliability and Maintainability Symposium (RAMS).

[36]  Corinne Reich-Weiser,et al.  Metrics for Green Manufacturing , 2013 .

[37]  Paolo Claudio Priarone,et al.  Cutting tool manufacturing: a sustainability perspective , 2013 .

[38]  Shaw C. Feng,et al.  A Framework of Product and Process Metrics for Sustainable Manufacturing , 2011 .