Radu Coman’s scientific contributions

This paper presents some preliminary experiments and conclusions regarding the grinding of PCD inserts used for cutting tools. Because PCD grinding is considered a difficult material removal operation, an understanding of the effects of machine and grinding wheel parameters on edge quality is of utmost importance. This study presents one method to help understand these effects. The conclusions drawn can be useful in determining parameters that yield increased tool performance and life through improved edge quality.

The need and benefits of introducing monitoring systems in the industrial manufacturing environment are emphasized and the use of acoustic emission (AE) signals for this purpose is highlighted. A system for monitoring the grinding process based on recording and processing the AE signals released during machining was developed in order to study the feasibility and relevance of this approach. It is proved that the AE signals are sensitive to changes of the most important aspects related to the grinding process and therefore are suited for monitoring the state and evolution of this process.

Optimization of grinding process is a multi- objective optimization problem. However, it traditionally has been solved as a single objective problem. In this paper, general, useful and practical multidisciplinary optimization methods are discussed for optimal design of surface grinding processes. The methods allow designer to explicitly consider and control multiple design objectives as an integrated part of the optimization process, and easily choose and set up preferences for the objectives in order to increase productivity and quality of the workpiece surface. The methods discussed in this paper are Pareto efficient set and multi-attribute utility theory based optimization approaches. The algorithm for finding Pareto set is proposed and an example is presented. A new formulation for multi-objective optimization of grinding process is developed. The results of the formulation represent the tradeoffs the designers are willing to make between work piece surface roughness, tool life, grinding ratio, and material removal rate. Four examples are used to illustrate the application of the formulation for multi- objective optimization of the surface grinding processes. An example for defining the preferences in the multi-objective optimization process is also presented.