Machining is one of the most widespread manufacturing processes and plays a critical role
in industries. As a matter of fact, machine tools are often called mother machines as they
are used to produce other machines and production plants. The continuous development
of innovative materials and the increasing competitiveness are two of the challenges that
nowadays manufacturing industries have to cope with. The increasing attention to environmental
issues and the rising costs of raw materials drive the development of machining
systems able to continuously monitor the ongoing process, identify eventual arising problems
and adopt appropriate countermeasures to resolve or prevent these issues, leading
to an overall optimization of the process. This work presents the development of intelligent
machining systems based on in-process monitoring which can be implemented on
production machines in order to enhance their performances. Therefore, some cases of
monitoring systems developed in different fields, and for different applications, are presented
in order to demonstrate the functions which can be enabled by the adoption of
these systems. Design and realization of an advanced experimental machining testbed is
presented in order to give an example of a machine tool retrofit aimed to enable advanced
monitoring and control solutions. Finally, the implementation of a data-driven simulation
of the machining process is presented. The modelling and simulation phases are presented
and discussed. So, the model is applied to data collected during an experimental campaign
in order to tune it. The opportunities enabled by integrating monitoring systems
with simulation are presented with preliminary studies on the development of two virtual
sensors for the material conformance and cutting parameter estimation during machining
processes.
