Dr. Attia is a Principal Research Officer/head of the Aerospace Manufacturing Technology Centre at the National Research Council Canada (NRC) in Montreal.
His work at Chalmers is done in collaboration with the division of Materials and Manufacturing, department of Industrial and Materials Science (IMS), and Chalmers Centre for Metal Cutting Research (MCR).
In the context of Industry 4.0 paradigm, and with the emergence of smart and digital manufacturing, the notion of high performance cutting (HPC) encompasses process adaptivity, part quality and accuracy, as well as high productivity at minimum cost. Such conflicting requirement can be achieved through integrated process modelling, optimization, monitoring and control.
In relation to dimensional accuracy of machined parts, it is well established that thermal deformation of the machine tool structure may contribute to more than 50% of the machining errors in high speed, high precision applications. Residual thermal errors cannot be avoided at the design stage, and the relative thermal displacement between the tool and the workpiece cannot directly be measured during machining. Therefore, the use of control and compensation systems is an inevitable course of action. Existing control systems are based on the use of empirical compensation function (inductive approach), or on-line execution of numerical simulation models (deductive approach). To overcome the limitations of these approaches, a model-based multi-variable closed-loop control system is presented and discussed in terms of its dynamic response, accuracy, and stability.
Tool wear affects the productivity, operation cost and the surface integrity of machined parts. Real-time sensor-based tool condition monitoring (TCM) systems are used to deal with the uncertainty of analytical tool life prediction by putting the tool under surveillance to safeguard the workpiece from damage. Available real-time sensor-based TCM systems are incapable of: (a) predicting catastrophic tool failure (chipping) before it happens, (b) reliably extracting the key signal features that describe the tool gradual wear, without being contaminated by information on the cutting conditions, and (c) operating in an adaptive control environment. Additionally, these systems require high learning effort. Novel approaches that were recently developed at the National Research Council Canada to overcome these problems are presented, and discussed in terms of dynamic stability, and accuracy.
A further step towards high performance cutting is applying adaptive machining techniques. A hybrid offline-online adaptive control system, integrated with a TCM capability, is presented, and its implementation in a cyber-physical system (CPS) platform is demonstrated. For the challenging task of drilling composite-metal stacks, this system can adjust the feedrate as the tool advances through the stack layers, for different cutting conditions and tool wear levels. Such key feature enabled feedrate optimization, while observing predefined force constraint, in order to produce damage-free holes. Experimental testing of the system showed up to 50% and 35% of combined reduction in the production cycle time and cost, respectively, as well as doubling the tool life and eliminating the damage risk associated with conventional machining strategies.