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Machinability of Engineering Materials

  • Hongyu ZhengEmail author
  • Kui Liu
Reference work entry

Abstract

There are thousands of materials available for engineering applications. Machinability is an indicator of one engineering material how easy or difficult to be machined using a cutting tool to achieve an acceptable surface finish, which could be considered as a material property. Engineers are often challenged to find ways to improve machinability without harming material performance, which are much focused on the machining efficiency and productivity. However, unlike most material properties, machinability cannot be simplified into a unique work material property, but rather considering as a resultant property of the machining system which is mainly affected by work material’s physical properties, heat treatment processes, and work-hardening behavior, as well as cutting tool materials, tool geometry, machining operation type, cutting conditions, and cutting fluids. When assessing a material machinability, all other aspects of the machining system must be considered concurrently. An understanding of the interactions between tool and work materials at the tool–work interface would benefit to machining behavior and machinability. Tool material and cutting speed perhaps are the two most important parameters for engineering material machinability assessments. Materials with good machinability require little power to cut, can be cut quickly, easily obtain a good surface finish, and do not wear the cutting tool fast. Engineering materials could be developed with improved machinability or more uniform machinability through microstructure modification and chemical components adjustment. Advance developed tool materials with high thermal hardness and wear resistance would improve the material machinability.

Keywords

Tool Wear Material Removal Rate Tool Life Flank Wear Chip Formation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Institute of Manufacturing TechnologySingaporeSingapore

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