Methods and Models for Cutting Data Optimization

Doctoral thesis, 2013

Traditional optimization of cutting processes refers to minimizing cost or minimizing time (minimum cost or maximum production rate). These criteria apply to the cutting speed via well-known equations for economic tool life and tool life for maximum production rate. Quite often speed is focused first when after some suitable feed value is chosen. This is a poor strategy for several reasons. This thesis analyses and suggests suitable strategies, methods, algorithms and models for cutting data optimization with general guidelines as well as specific recommendations for some exemplified situations. Effective procedures designed to avoid sub-optimizations and alternative models for minimizing the process costs, maximizing the production rate or minimizing the tool cost while considering a desired cycle time (corresponding to actual demand of the produced part) are central subjects for the discussions. In a second phase, tool wear calibration allows for tool replacement coordination to minimize related costs, since planned occasions eliminates slack due to operator unattendance, and coordinated tool replacements minimizes total replacement time. The procedures, methods and models for cutting data optimization discussed have also proven to make significant cost reductions in several cases even when the process engineers involved in these studies generally have long experience and said to have optimized their processes continuously for years. Although, the models presented in this thesis mainly refer to turning operations, these strategies are applicable for turning, milling and holemaking processes and especially applicable for medium to large lot sizes in multi tool operations, cell balancing, transfer line balancing, etc.