Experiments - Lighthouses

Cross-Border Experiment in the Cutting Tools Industry


Lighthouse description

NECO is a Spanish company, part of the French Tivoly Group, that manufactures cutting tools, such as taps and cylindrical and flat rolling devices, for customers from the Power Generation, Aeronautic and Automotive Industries.

In the NECO Industrial case, the efforts have been focused on the threading tools, or taps, which are made of High-Speed Steel and offer good performance against temperature and wear when performing machining operations at customer premises.

The tap manufacturing process starts by turning and cutting off HSS (High Speed Steel) bars that are used as raw material. Then, the bars undergo a heat treatment and the outside diameter is grinded. The next step is flute grinding, where the chip evacuation channels are drawn into the semi-finished product. This last step, the flute grinding, is especially critical, due to its important contribution to the value of the product, as well as the great difficulty of reprocessing products out which are already of specifications.

As part of the Smart Factory scenario, the development of a Manufacturing Process Digital Twin and a Tools and Fixtures digital twin is required, first of all. The objective of the first one is to monitor the operational conditions by installing sensors to control the critical parameters which have the highest impact on the flute form and flute angle provided to the taps, while the second one has enabled the automatic inspection of the manufactured taps by creating a 3D model, as well as monitoring the status of the wheels which give form to the products. Finally, a correlation between both (operational conditions from one side, and product and the tooling from another) has been performed in order to gather knowledge about the process and for NECO to be able to operate the grinding machine in stable conditions which allow a repeatable performance of the product.

The flute grinding operation, which is the one who provides the final product with the correct flute form and cutting angle in order to cut the material and properly evacuate the chip, was the part of the manufacturing process selected for this lighthouse experiment, due to a majority of defects being originated at this stage.

The manufacturing context which have encouraged NECO to join MIDIH is summarized on the fact that there was a high digression in the eventual performance of threading tools produced as part of different Manufacturing Orders.

The ambition has been to improve the stability and repeatability of the taps’ performance, and the main objective was to develop cognitive manufacturing abilities that will help control the production in order to meet the quality requirements and performance.



The challenge of the MIDIH lighthouse experiment, in the case of NECO, relies on three pillars:

  • To achieve a higher level of digitization of the automated manufacturing processes through the adoption of CPPS/IIoT technologies.
  • The ability to share production data and tool usage with OEMs through trusted industrial data spaces.
  • The adaptation and optimization of manufacturing management through Cloud and AI platforms


Regarding the solutions, the MIDIH experiment could basically be described on three points:

  • To establish a digital twin of both the manufactured product and the process.
    • The part digital twin will consist in the implementation of an optical laser device able to scan the part and the part that has been reconstructed from the point cloud and the dimensional inspection has been automatically performed by a 3D viewer software. This way, the defects on the flute form and cutting angle of the manufactured tool were detected. Furthermore, the cycle time was matched, so, it was feasible to integrate the scanning device in-line, into the manufacturing process, in order to pursue a Zero-Defect Manufacturing approach.
    • The process digital twin consisted on installing a number of sensors in the pilot flute grinding machine in order to monitor the process, in order to control, in real time, the operational parameters.
  • A real monitoring platform has been deployed at NECO, as seen in the previous image, in order to monitor the process operational conditions and product quality. Thanks to the new dimensional inspection optical process, which is more accurate, the design tolerance windows have been tightened in order to ensure the manufacturing of products with more consistent geometrical parameters. Deviations which were previously considered OK (dotted line) are now regarded as not optimal.

  • Machine set-ups have been optimised as well, thanks to the acquired process “know-how”, in order to maintain stable operational conditions that enable the manufacturing of repeatable products.
  • Correlating the product and process parameters through data analysis, this way enabling NECO to predict failures and defective parts and extract conclusions in order to optimize the machine set-ups to achieve higher stability and repeatability for the taps and maximize customer satisfaction.
  • On a higher level, the integration of all this information, along with the one about the status of the tool, has been integrated in a dashboard, and this interface has helped the operators and supervisors in the decision-making. All this data and information could be shared with OEM customers, as it would consist of an industrial data space for sharing them with a high level of security and trust among all the parts involved.


The following components from the MIDIH Open Platform have been used:

  • Smart Factory Business Scenario:
    • IDAS [MIDIH IoT Agents]:  capturing the operational data
    • ORION Context Broker:  gathering the process and product data
    • CYGNUS – STH:  contextualising and storing those data
    • RUBY ON RAILS:  visualising the monitored data and SPC, along with the Digital Twin product 3D reconstruction
  • Smart Product Business Scenario:

Cassandra:  storing and processing the historical database related to process/product



Andoni Laskurain