MSAM partners with Siemens to accelerate industrial additive manufacturing

Additive manufacturing (AM), also known as 3D printing, is revolutionizing manufacturing. While design and prototyping are common in fields such as medical, automotive, energy and aerospace, the full-scale manufacturing and distribution of AM parts is not yet reality due in part to complex quality control. The Multi-Scale Additive Manufacturing lab (MSAM) and Siemens Canada Limited (Siemens) partnered to create an innovative statistical tool that optimizes the process parameters of laser powder-bed fusion (LPBF), a class of metal AM process. The tool has the potential to benefit industrial suppliers and customers across the supply chain through improved and streamlined process optimization as well as cost savings. This project represents a significant research success and a great move toward realizing the full industrial potential of AM.

Siemens welcomed the opportunity of working with the University of Waterloo based on the cutting-edge equipment available at MSAM. We have been very happy with the projects so far and anticipate this partnership will lead to future progress.
Dr. Ali Bonakdar, Advanced Manufacturing Technology Lead, Siemens Canada Limited

Siemens Canada Limited (Siemens) has been in operation for over 100 years, with over 4,500 employees in 39 offices and 14 production facilities across Canada. Siemens delivers innovative solutions for sustainable energy, intelligent infrastructure, finance, information technology, healthcare and manufacturing. In 2017, Siemens launched a collaborative AM Network to accelerate adoption of AM for design and production. Siemens provides both cash and in-kind support to MSAM in their highly productive relationship.

“Siemens is a pioneer of AM. Through this multi-faceted collaborative project, MSAM worked closely with a professional team of engineers to achieve highly innovative solutions for hurdles hindering the adoption of AM. The researchers involved have gained substantial experience in dealing with such a well-known companies. “

– Dr. Ehsan Toyserkani, Research Director, MSAM, University of Waterloo

Industrial AM applications include rapid prototyping, full-scale and spare part production, and rapid repair of existing components. As a digitized and customizable process, AM saves time and resources while increasing flexibility and scalability.
Despite the apparent advantages, full-scale deployment of AM to high-value components has been slow. Quality control is a major obstacle when the accuracy and consistency of critical properties (e.g., density, smoothness, mechanical strength, fatigue life etc.) are difficult to predict and control, particularly for metal AM where workflows comprise over 100 process parameters.
MSAM approached Siemens to discuss collaborative opportunities based on recent funding. This project began in July 2017 and focused on metal AM using LPBF. The challenge was to develop a commercially-viable approach to optimizing process parameters (e.g., laser power, printing speed, etc.).

Researchers developed a three-part statistical approach that optimized more than 20 LBPF parameters:

1. Determination of the most significant process parameters
2. Determination of the optimal input values to give reliable output
3. Validation of the approach

R&D was completed in only twelve months, and the resulting tool can be applied to LPBF applications using any powdered metal material source.
MSAM and Siemens have filed the patent to protect the methodologies and procedures. Representatives from Siemens global offices provided input throughout the collaborative process.

The statistical tool provides an estimated three-fold reduction in the number of printed test pieces required for understanding or optimization of printing parameters. This corresponds to an increase in part reliability, and saves costs by reducing time, effort, energy and materials.

Siemens plans to commercialize the tool in 2019 and anticipates the creation of engineering and/or sales positions.

The initial objective was optimization of density, hardness and surface roughness. Standard cube test pieces and artifacts were used for the process development and engine component prototypes were used for validation. Subsequent applications could use this approach to fine-tune the input parameters for other properties of interest (e.g., fatigue life or strength), and/or consider additional parts and materials.

AM opportunities are growing, and workflow optimization increases efficiency, customization and quality, translating to faster time-to-market and business growth.

Siemens will license and commercialize the workflow optimization tool as a service offered to their customers who rely on high quality AM part production.