Enhancing cutting tool sustainability based on remaining useful life prediction. (20th January 2020)

• Record Type: Journal Article
• Authors: Sun, Huibin; Liu, Yang; Pan, Junlin; Zhang, Jiduo; Ji, Wei
• Is Part Of: Journal of cleaner production. Volume 244(2020)
• Publication Date: 2020-01-20
• Subjects: Cutting tool sustainability enhancement -- Remaining useful life prediction -- Cutting tool utilization improvement; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.118794
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: As a critical part of machining, cutting tools are of great importance to sustainability enhancement. Normally, they are underused, resulting in huge waste. However, the lack of reliable support leads to a high risk on improving the cutting tool utilization. Aiming at this problem, this paper proposes an approach to enhance the cutting tool sustainability. A non-linear cutting tool remaining useful life prediction model is developed based on tool wear historical data. Probability distribution function and cumulative distribution function are used to quantize the uncertainty of the prediction. Under a constant machining condition, a cutting tool life is extended according to its specific remaining useful life prediction, rather than a unified one. Under various machining conditions, machining parameters are optimized to improve efficiency or capability. Cutting tool sustainability is assessed in economic, environmental and social dimensions. Experimental study verifies that both material removal rate and material removal volume are improved. Carbon emission and cutting tool cost are also reduced. The balance between benefit and risk is achieved by assigning a reasonable confidence level. Cutting tool sustainability can be enhanced by improving cutting tool utilization at controllable risk. Highlights: An approach to enhance cutting tool sustainability is proposed. It extends a cutting tool's life based on its individual RUL prediction. It optimizes machining parameters to improve machining efficiency or capability. Cutting tool sustainability is assessed and its improvement is verified.