Optimal replacement times for machining tool during turning titanium metal matrix composites under variable machining conditions. (May 2017)

• Record Type: Journal Article
• Authors: Shaban, Yasser; Aramesh, Maryam; Yacout, Soumaya; Balazinski, Marek; Attia, Helmi; Kishawy, Hossam
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 231:Number 6(2017)
• Page Start: 924
• Page End: 932
• Publication Date: 2017-05
• Subjects: Optimal tool replacement -- metal matrix composites -- cost optimization -- availability optimization; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405415577591
• Languages: English
• ISSNs: 0954-4054

Abstract

Little practical results are known about the cutting tool optimal replacement time, specifically for machining of composite materials. Due to the fact that tool failure represents about 20% of machine down-time, and due to the high cost of machining, in particular when the work piece's material is very expensive, optimization of tool replacement time is thus fundamental. Finding the optimal replacement time has also positive impact on product quality in terms of dimensions and surface finish. In this article, two new contributions to research on tool replacement are introduced. First, tool replacement mathematical models are proposed. These models are used in order to find the optimal time to tool replacement when the tool is used under variable machining conditions, namely, the cutting speed and the feed rate. Proportional hazards models are used to find an optimal replacement function. Second, this model is obtained during turning titanium metal matrix composites. These composites are a new generation of materials which have proven to be viable in various industrial fields such as biomedical and aerospace, and they are very expensive. Experimental data are obtained and used in order to develop and to validate the proportional hazards models, which are then used to find the optimal replacement conditions.