Here you can find the summary of the research projects that I worked at the intersection of manufacturing and artificial intelligence

1. Boost 4.0 - Big data for factories

Industrial Partners: IBM, SAS, Siemens, Gestamp, Visual Components, Volkswagen, Whirlpool, Benteler, Philips, Atlantis

Academic Partners: Chalmers University, Bonn University, Politecnico Milano, Fraunhofer IEM

September 2019 - February 2021

Supervisor: Prof. Anders Skoogh

The purpose of the project is to develop open interfaces for the development of big data pipelines for advanced analysis services and data visualization supported by the main digital engineering, simulation, operations and industrial quality control platforms. I am developing data-driven algorithms that can analyse the production flow and optimize it to achieve higher throughput.

You can read more about the project here.

2. Data Analytics In Maintenance Planning (DAIMP)

Industrial Partners: AB Volvo, Volvo Cars, Scania AB, IFS, AXXOS AB

Academic Partners: Chalmers, KTH, MDH

February 2016 - February 2019

Supervisor: Prof. Anders Skoogh

This project had four different work packages (WP1-WP4) focussed on increasing the efficiency of maintenance planning in production systems. I worked in WP2 that is focussed on developing data-driven algorithms using statistical and machine learning tools to improve the efficiency of back office maintenance planning of production systems.

You can read more about the project and its outcomes here.

3. Data Analytics and Maintenance Preparedness – Volvo Maintenance Management System (DAMP- VMMS)

Industrial Partners: AB Volvo

Academic Partners: Chalmers

September 2015 - December 2015

Supervisor: Prof. Anders Skoogh

The purpose of the project is to investigate and develop a model for criticality classification of production resources from a maintenance perspective. I developed a data-driven algorithm to identify critical resources from production system event logs.

You can read more about the data-driven algorithm here.