Master Thesis within Future Compaction Indication Systems

About The Role

Last date to apply

16 October 2026

Development of a Methodology for Evaluating a Machine-Data-Driven Compaction Indicator for Vibratory Plate Compactors

About Us

Husqvarna Construction is a world-leading supplier of equipment and solutions for construction, demolition, and light compaction applications. Husqvarna has built strong global market leadership and one of the industry's most recognized brands.

At the Compaction & Concrete Vibration R&D team at Husqvarna Construction, we design, test, and build the next generation of compaction and concrete vibration equipment — working closely between simulation, physical testing, and product development to bring new machine concepts to life.

Background

Vibratory plate compactors are a core part of Husqvarna Construction's product range, spanning both forward and reversible plate models used extensively across construction settings. Soil compaction is one of the most fundamental quality parameters in ground preparation and civil construction. While Husqvarna already offers a compaction indicator within its portfolio, the broader landscape of compaction indication is still evolving, and there remains significant scope to explore data-driven approaches that leverage the full range of machine parameters available on modern plate compactors.

It is currently unclear which machine-derived signal, or combination of signals, most reliably reflects true compaction state, and there is no established methodology for deriving a data-driven compaction indicator from them.

Thesis Work Objective

The purpose of this thesis is therefore to develop a methodology for designing and evaluating a machine-data-driven compaction indicator for Husqvarna Construction's forward and reversible plate compactors. This involves instrumenting the machines with relevant sensing modalities — such as drive power consumption, acceleration, or acoustic signature — and collecting data under varying soil conditions and pass counts, with the aim of identifying which signals, or combinations thereof, can reliably indicate compaction state.

Activities

Literature study of existing compaction indicators and associated sensors
Analysis and selection of candidate sensors/machine parameters for instrumentation
Data collection across varying soil conditions, pass counts, and machine types
Data evaluation and pattern analysis
Development of a methodology for a data-driven compaction indicator, based on the finding

Relevant knowledge/background

Structural dynamics & vibrations
Mechatronics & instrumentation
Data-driven methods (data analysis, ML, or optimization)

Preferred Masters Program

Mechatronics/ Machine Design/ Systems, Control and Mechatronics or anything similar
Thesis Level: Master (30 ECTS points)
Starting date: January 2027
Number of students: 2 students
Last application date: 15 Nov 2026