AddSmart demonstrators clarify how to develop smart connected products

Many companies wanting to make their products smart and connected are looking for the right development approach. There is no such thing as a golden bullet. Together with Fraunhofer IEM and with the support of VLAIO, Sirris has set up a collective research project: AddSmart. The project aims to develop a practical guide to help companies get started with the conceptual development of their smart connected product.

What are the steps you need to take to arrive at a concept? How to make technology choices? How to build a proof-of-concept? Sirris aims to answer these and many other questions with the AddSmart project.

Demonstrators make insights tangible

What better way to demonstrate certain insights than by building your own proof-of-concepts? Sirris developed two demonstrators for this purpose: a smart case and a smart milling machine. With the choice of a portable consumer product (b2c) and an industrial production machine (b2b), it was possible to elaborate quite different applications, each with their own requirements. In this way, the project also meets the diversity of product builders in our target group.

Smart case thanks to built-in sensors and wireless connectivity

The smart case is intended as an example of a personalised product made in smaller series and with a cost-conscious design. Sensors were integrated into the case to monitor the condition of the case and its contents. Thanks to wireless communication, the user can monitor the proximity of the case and all sensor data with a mobile device, while the product builder can use the data for product improvements and additional services. Special attention was paid to the energy consumption of the battery-powered system, various connectivity solutions for connection to the cloud and secure communication. The smart case was developed with the cooperation of experts in smart connected products, composites and production technology.

Smart milling machine thanks to remote monitoring and algorithms which predict tool wear

The second demonstrator is based on an existing milling machine design with a simple serial communication interface. A gateway was developed and a cloud dashboard was set up so both the user and the product builder could monitor the machine status and performance. To monitor and predict the wear of the cutting tool, a model was built by applying machine learning algorithms to data from various sensors, specifically added to the machine. Special attention was paid to the concept of the gateway and the data-driven approach for the prediction of tool wear.

The two demonstrators were presented in broad events and specific workshops. They will also be further explained to the target group of product builders after the closure of the AddSmart project.