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Specialised in Wood Equipment

Since 1969 BIESSE has been designing, producing and marketing a complete range of technologies and solutions for the carpenter and the large furniture, window and wooden building components industry. BIESSE is now present in plastic processing machines with solutions designed specifically for a growing market. 

The project’s scope is to improve data quality, consistency and integrity in its CNC (computer numerical control) machines to allow the use of available information and add-on sensors in diagnostic analytic tools. 

Opening the way to new diagnostic tools is a crucial step towards:

  • Reducing machine break-down,

  • Detecting early degradation, 

  • Increasing productivity,

  • Increasing quality. 

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Pilot partner


Technology Provider


Implementer partner

Problem description


In manufacturing processes, production and maintenance data are increasingly important. They are providing a way to increase product quality and machine useful life. The main problems related to the data acquisition are:

  • Often readily available data in machine tools (currents, accelerations, temperature) has poor quality due to its limited sampling rate and resolution.

  • The sensors needed to monitor certain subsystems that may not be available, due to limited accessibility, cost feasibility or because the required sensor is too intrusive. 

Available data may be sent to BIESSE Digital Platform, that ensures data storage and integrity on cloud. However, data integrity on machine must be improved to exploit all platform features, which calls for innovative techniques to collect and manage data. 

Biesse already shows data for statistics, diagnostics and preventive maintenance, using OPC-UA as standard protocol. However, an enhancement of this protocol is needed to feed predictive analysis algorithms with new deep data. BIESSE aims at increasing data quality by using additive and virtual sensors and adopting an edge architecture to increase computational processing capacity.

i4Q Solution

The proposed solutions will be developed for the top CNC woodworking solution by BIESSE. I4Q outputs will be exploited to continuously monitor working conditions and process parameters. 

Readily available sensors, such as vibration or temperature, and will leverage the available PLCs (Programmable logic controllers) that are able to adapt the process to the recorded working conditions providing a way to correct process drifts. 


Further sensors and fieldbus data on woodworking machine to support advanced continuous monitoring algorithms (edge computing).


IoT functionalities to communicate machine data with BIESSE Digital Platform. 


This data will feed a DSS (Decision Support System) and predictive maintenance algorithms to increase the useful life of the machine. These algorithms can:

  • Detect degradation before faults,

  • Find in advance critical jobs,

  • Find the inappropriate uses. 

The performance of these algorithms is highly dependent on the reliability of data: test cycles aimed to acquire quality data and ensure precision and accuracy will be developed for each meaningful device wired. 

Aggregated data and results will be sent to the BIESSE Digital Platform for remote control and deep storage. KPIs (Key Performance Indicators) will be checked day by day, so that production data can lead to an integrated approach to zero-defect manufacturing.

The machine will incorporate simulation capabilities that will allow to test the programs in advance in a scenario very similar to the real one. The objective is to minimize the rejects due to incorrect planning or configuration and setting of the processes. 

Expected results

  • Improvement of data quality, test cycles aimed to acquisition of quality data (vibrations, currents, etc.) will be developed for each meaningful device (electro spindle, axis, pistons, etc.).


  • Improvement of data consistency, by the reduction of inconsistent data cases that may stop the production.


  • Improvement of data integrity by the reduction of data loss cases about production and maintenance.


  • Improvement of diagnostics capability.


  • Improvement of products quality.


  • Increase of machine productivity.

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