Peter ten Haaf

The use of (real-time) data to adjust production processes - in machining the term is 'adaptive machining' - is the future of production. This is made possible by the increased availability of sensors. However, everything depends on having and understanding a standardised and structured model, in which the digital information is processed into a physical action. Such models are the key to successful digitalisation. In a series of blog posts, we highlight some basic models. In this third part, we look at a model for calculating the cutting force: the Kienzle formula.

Peter ten Haaf

The use of (real-time) data to adjust production processes - in machining the term is 'adaptive machining' - is the future of production. This is made possible by the increased availability of sensors. However, everything depends on having and understanding a standardised and structured model, in which the digital information is processed into a physical action. Such models are the key to successful digitalisation. In a series of blog posts, we highlight some basic models. In this second part, we will discuss a model for the calculation of the specific cutting force.

Jan Kempeneers

Metalworking company Malmar goes for flexible automation in its high-mix-low-volume production. The project does not only envision introducing reconfigurable mobile robotic production assistants to support the operators, other interventions are also required to ensure smooth operation within the production cell, including a new system for the separation and feeding of nuts to a nut projection welding machine.

Peter ten Haaf

Controlling and adjusting production machining through the use of data - including real-time data - is called 'adaptive processing'. It’s the future of production and is made possible by the increased availability of sensors. However, everything depends on having and understanding a standardised and structured model in which the digital information is processed into a physical action. These models are the key to successful digitisation. In a new series of blog posts, we highlight some basic models. In this first part, we will discuss the tool life curve, which is a model for finding the optimum cutting speed.

Samuel Milton

On the online platform ‘Model-based machining’ you can find calculation models that use information from tool life curves for finding the most economical cutting speed. This lets you select the most cost-effective machining process. We are organising training courses to help with the practical implementation of the calculation models.

Tom Jacobs

Tool monitoring is needed in the milling process in order to be able to automate it and to ensure the quality of the products. This is not an easy task where micromilling tools are involved, but acoustic sensors can offer a solution for this.

Peter ten Haaf

When striving for an Industry 4.0 production system, the first requirement is to connect the production machines within a network. In a machining production environment with a wide variety of machines - modern, obsolete, designed for batch processes, manual, etc. - this presents a major challenge.

Tom Jacobs

In machining processes, a recently developed cooling method using supercritical CO2 can make cryogenic cooling competitive with conventional coolants. This method offers some important advantages.

Samuel Milton
Tom Jacobs

Vibrational response plays an essential role in the performance of machine tools and machining processes, which directly affects the material removal rate and workpiece surface quality, as well as dimensional and form accuracy. It is, however, still a topic that is least understood in manufacturing science.

Samuel Milton
Peter ten Haaf

Adaptive machining allows specific actions to be taken in machining, based on the actual production situation. The successful application depends on the availability of correct (real-time) data and the corresponding structured and digitalised working methods.  For the latter, we have developed several models which we will offer via an online platform within the 'Model-based processing' project.