PhD - Lifetime Prediction for Metal Sawing Operations at Bosch Group

Schwieberdingen, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

02 Jun, 26

Salary

0.0

Posted On

04 Mar, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Matlab, Python, Artemis, Ansys, Abaqus, Vibration Testing, Finite Element Simulation, Data Acquisition Systems, Machine Dynamics, Manufacturing Technology, Structural Dynamics, Materials And Fatigue, Predictive Maintenance

Industry

Software Development

Description

Company Description At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: We grow together, we enjoy our work, and we inspire each other. Welcome to Bosch. The Robert Bosch GmbH is looking forward to your application! Job Description Bosch is a worldwide leader for the Oscillating Multi Tools (OMTs), that are modular and can be used for a variety of machining tasks. We as the Center of Competence -Vibration, arecurrently investigating predictive operational reliability and lifetime models for various type of saw blades for metal cutting that can be used with OMTs. To understand the sawingprocess and the underlying physics is a key aspect of our campaign. You will advance the understanding of cutting mechanisms in sawing processes and life-limiting factors such as wear fracture and material fatigue. Furthermore, you will combine experimental measurements with finite element simulation techniques to identify key parameters for wear and operating life of cutting tools. Based on experimental and simulation data, you will make reliable predictions about the service life of such tools. During your PhD, you will develop a new concept to combine these techniques with monitoring and predictive maintenance approaches. You will work closely with an international team to integrate and evaluate these changes into the development process. In addition, you will provide key input into the design and manufacture of state-of-the-art prototypes, particularly in terms of dynamics and vibration transmission from the tool to the blade and cutting edge, to significantly improve blade life. The supervising will be carried out in collaboration with Institute for Machine Tools Management, University of Stuttgart. Qualifications Education: completed Master's degree in Engineering or Natural Sciences with a focus on Machine Dynamics, Manufacturing Technology, Structural Dynamics or Materials and Fatigue Experience and Knowledge: average to advanced programming skills (Matlab, Python); experience with data acquisition systems (Artemis); theoretical and/or practical knowledge of vibration testing; knowledge of simulation tools (Ansys, Abaqus) is beneficial Personality and Working Practice: you manage to tackle complex problems with passion and you actively contribute as a team player, communicating your results clearly and convincingly Work Routine: regular visits to Switzerland to exchange ideas with the development team are part of your job Enthusiasm: you are passionate about researching and further developing cutting mechanisms and lifetime models, and enjoy advancing the state of the art Languages: very good in English and German, both written and spoken Additional Information The final Ph.D. topic is subject to the supervising university. Start: by prior agreement Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity. Need support during your application? Tamara Bont (Human Resources) +49 9352 18 2818 Need further information about the job? Christian Dindorf (Functional Department) +49 711 811 43166 Nimish Pandiya (Functional Department) +49 152 51709795 Work #LikeABosch starts here: Apply now! Legal Entity: Robert Bosch GmbH

Responsibilities

The role involves advancing the understanding of cutting mechanisms and life-limiting factors in sawing processes by combining experimental measurements with finite element simulation techniques. The PhD candidate will develop a new concept to integrate these techniques with monitoring and predictive maintenance approaches to reliably predict tool service life.