SOMMACT

SELF OPTIMISING MEASURING MACHINE TOOLS

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 Obiettivo del progetto (Objective)

'SOMMACT develops and validates an innovative production hardware and control system founded on understanding, evaluating and controlling production system performances. This approach is based on detection (in-process traceable measurement embedded capabilities) and compensation (adaptive control and self-learning capabilities) of geometrical effects of varying external and internal quantities, such as temperature gradients and workpiece mass. Knowledge accumulated by SOMMACT approach is available for the higher level of a production system management, supporting self-optimisation and decision making, including the implementation of predictive/virtual metrology methods. The SOMMACT vision is based on three pillars: 1. A new metrological concept to enhance the measuring capability of machine tools, to monitor the machine geometrical deformation reliably and to inspect machined part characteristics traceably; 2. Enhanced sensor systems, measuring the 6 degrees of freedom of each machine component, and a control system integrating machine and workpiece data with environmental and load conditions, and adapting machining accordingly; 3. A self-learning model of the system performance, accumulating knowledge on the machine performance, based on calibration and real-time measurement data, and on their relationship with workpiece characteristics and the environment. The advantages are an improved product quality at competitive costs, and a prediction capability of the system performances based on a increasingly reliable model. The SOMMACT units can either stand alone, or be integrated into production systems as basic building blocks, enabling flexibility and easy reconfiguration under contingent conditions. Furthermore, the MT measuring capabilities are enhanced to the point that it can be used as a CMM. This (i) avoids QC production loops, (ii) provides workpiece traceable measurement results, and (iii) inputs valuable data into the self learning core.'

Introduzione (Teaser)

Scientists are developing a quasi real-time adaptive control system for machine tools. Combining sensors and a self-learning algorithm, the system will facilitate cost-effective, high-quality small and single-batch manufacturing.

Descrizione progetto (Article)

Manufacturing of small batches of mechanical components is often plagued by inefficiencies, and waste of raw materials and energy. Process parameters are largely determined on a trial-and-error basis and production time is significantly increased by incorporation of Quality Control - Production loops. Scientists initiated the EU-funded project 'Self optimising measuring machine tools' (Sommact) to develop novel production hardware and controlling software leading to improved product quality at competitive costs.

Sommact exploits three innovations. First, scientists are employing a new metrological concept to measure and monitor machine tool errors and inspect machined parts. The measuring capabilities are enhanced to the point of being a coordinate measuring machine (CMM) to provide workplace traceable measurements and input valuable data to a self-learning component. Enhanced sensor systems provide detailed information about the geometric errors of each machine motion. The error information is combined with environmental and load conditions by an adaptive control system that changes the machine process accordingly. Lastly, a self-learning algorithm accumulates knowledge of machine behaviour under varying conditions and its relationship to part quality.

All project objectives have been achieved in a timely manner. Experiments demonstrated that the Sommact system can reduce errors affecting machine accuracy by more than 75 %. In addition, on-machine inspection capabilities previously only available on CMMs have been included and traceability ensured by novel calibrated reference artefacts. Sommact has provided a valuable toolkit for measurement of machine error motion with six degrees-of-freedom (DoF) in stark contrast to previous state of the art consisting of a one DoF position sensor system. Almost all of the toolkit can be used with existing machine tools. Updating of volumetric compensation tables is done in quasi real-time subject to an operator's input. Standardisation of compensation data within ISO (International Organization for Standardization) is underway. Finally, decision-making support activities include applying immediate action, requesting recalibration of the machine if it is outside of previously learned scenarios, and predicting maintenance needs.

The Sommact system should facilitate important improvements in machine tool control and product quality. Sommact has been recommended as a Flagship Project and a Success Story for both the machine tools industrial sector and the European Commission's Directorate-General for Research and Innovation (RTD).