Monitoring lifespan of machine tools

is it possible that a machine can monitor whether a tool is nearing the end of its lifespan and the machine can change to a different tool automatically

WHAT ARE YOU GOING TO DO?

Voortman is taking the next step by offering our customers several cloud services. We start off withinsights in machine status, production status and realized production times. Next to these products, we are looking into the possibilities to support our service department through the cloud environment and the data that is being gathered there. As an organisation we are proud of our ability to support customers during the lifespan of the machines that we offer. Not only are we recognized worldwide for the quality of our machines, but also for our excellent after-sales and we aim to stay the leader in our branche. We expect that machine learning will be a tool that is going to make the difference in the future. By making use of machine learning we want to improve the co-operation with our customers even more by offering them value through advice or new services. This assignment is the first specific step we want to take in order to use the right algorithms for machine learning as we explore this new terrain.

GOAL OF THE ASSIGNMENT

At Voortman we develop a wide range of machines that can be offered to customers either as ‘stand-alone’ or production line. The goal is to have these machines run unmanned for as long as possible. In order to achieve this, manual actions should be prevented or postponed until an operator is around.
An example is the timely change of consumables by the machine itself. If the machine continues too long with the same consumable it will break at some point. Result is that the machine stops running until an operator fixes the issue. The preferred situation is that the machine can monitor when the tool is nearing the end of its lifespan, so that the machine can change to a different tool. Many factors influence the lifespan of a consumable. Some examples are the machine type, material type and operating parameters that are being used. The goal of this assignment is to determine the lifespan of a consumable and to be able to detect when a specific consumable is close to the end of its lifecycle.

EDUCATION

This problem has many aspects that need to be tackled that can be interesting to different study
directions:

Applied Mathematics
Applied Physics
Business Information Technology
Computer Science
Industrial Engineering & Management

WE OFFER

Voortman and DIGI-STEEL are companies where all are willing to support one another. Companies with a pleasant working environment. We are convinced that this contributes to the performance of each and every one of us. Furthermore, we offer:

An allowance of €450 gross per month.
A travel allowance from 15 km and max. 30 km one way.
High-quality equipment and materials that you need to do your job well.
Whether you are a graduate or an intern; you are an integral part of our team. We think that’s very important.
A very instructive internship or graduation period at our innovative organization.
All freedom and responsibility to successfully complete your assignment.

ABOUT THE COMPANY

DIGI-STEEL is a start-up and member of the Voortman Steel Group, which is one of the market leaders for steel machinery. With DIGI-STEEL we introduce the first fully cloud-based software solution for steel processors. It is the next generation steel workshop management software guiding our customer’s factories into the smart industry 4.0 era.