Holistic Approach for an Energy-Flexible Operation of a Machine Tool with Cooling Supply

Abstract

The following paper examines the practicality of a methodical approach for energy-flexible and energy-optimal operation in the field of metal-cutting production. The analysis is based on the example of a grinding machine and its central cooling-supply system. In the first step, an energy-flexibility data model is built for each subsystem, which describes energy flexibility potentials generically. This is then extended to enable combined energy cost-optimal production planning. As a basis for the links between the data model representations, the cold flows between the subsystems are modeled using parameter-estimation methods, which have a mean absolute error of only 2.3 percent, making the subsequent installation of heat meters unnecessary. Based on the presented approach, the results successfully validate the possibility of energy-flexible cost-optimal and sensor-reduced production planning by reducing energy costs by 6.6 percent overall and 1.9 percent per workpiece produced.

Strain Virtual Sensing Applied to Industrial Presses for Fatigue Monitoring

“…Using generalized inverse Gi + , ( 5) and ( 6) are combined in (7), allowing the estimation of unknown strains from a limited number of strain measurements.…”

“…Data-driven VS methods are based on numerical relations between input data and output data (without containing physical parameters that describe the behavior of the system) and require large numbers of samples of real input and output data to be created (known as training data). Commonly used data-driven VS methods are neural networks [ 5 , 6 ] and regression algorithms [ 7 ]. Model-based VS methods use physical models of the system, being more complex to implement than data-driven methods and requiring greater knowledge about the physics of the system.…”