SOMMACT develops and validates an innovative production hardware and control system founded on understanding, evaluating and controlling large machine tools production performances.

Small and single batch production, particularly for large (some cubic metres), heavy (several tons), and complex workpieces, is still a challenge for machine tools users (see Figure 1).

To achieve high product quality, timely QC (Quality Control)-production loops are required, which are inefficient and expensive.

The organisation and sequencing is also difficult because the process performance practical knowledge is almost unavailable.

As a consequence, this type of production is still affected by inefficiencies and waste (energy, raw material and time).

SOMMACT approaches these issues by the detection (in-process embedded traceable measurements) and compensation (adaptive control and self-learning) of geometrical effects of varying external and internal quantities, such as temperature gradients and workpiece mass (see Figure 2).

The SOMMACT vision is based on three pillars:

  1. A new metrological concept to enhance the measuring capabilities of machine tools, to monitor machine geometrical deformations reliably and to inspect machined parts characteristics traceably.
  2. Enhanced sensor systems, measuring the 6 degrees of freedom (dof) of each machine component, and a control system integrating machine and workpiece data with environment and load conditions, and adapting machining accordingly.
  3. A self-learning model of the system performance, accumulating knowledge on the machine behaviour, based on calibration and real-time measurement data, and on their relationship with workpiece characteristics (e.g. mass).

The advantages are an improved product quality at competitive costs, and a prediction capability of the system performances based on increasingly reliable model. The machine tool measuring capabilities are enhanced to the point that it can be used as a Coordinate Measuring Machine (CMM). This (i) avoids or reduces QC-production loops, (ii) provides workpiece traceable measurement results and (iii) inputs valuable data into the self-learning core.

SOMMACT will measure the effects of process disturbances on geometric errors of individual machine components, store the corresponding data and associate them with corresponding known disturbances ( e.g. ambient temperature and workpiece mass) and apply quasi real-time adaptation of geometric compensation tables under the supervision of the self-learning core.

Self-optimisation methods will be applied to steadily improve the product quality. Individual stored geometric errors will be combined with (i) on-board workpiece measurement results, (ii) independent CMM measurement results, (iii) timely, swift re-tuning data and (iv) possible full recalibration data.

SOMMACT advancement over the State of the Art are summarised in the following scheme:

Main expected results

  1. 100% of improvement of product quality;
  2. 20-30% reduction of total manufacturing time for small and single batch production;
  3. 15-20% increase of machine availability;
  4. Strong drive toward “zero defects” target;
  5. 70% reduction of workpiece moving phases;
  6. 15-20% increase in acceptable operating temperature conditions.

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