The Importance of Education for the Adoption of AM

The Importance of Education for the Adoption of AM

The World Economic forum publishes an Additive Manufacturing Breakthrough: A How-to Guide for Scaling and Overcoming Key Challenges

How-to Guide for Scaling and Overcoming Key Challenges

Technology suppliers and researchers are encouraged to push more in the direction of end users, who are placing focus on costs and the specific requirements arising from serial production. The development perspective must exceed the boundary of the AM system itself and include upstream and downstream processes. Funding and investments by collaborating partners along the process chain must be strengthened to decrease costs.
First, existing efforts to educate engineers in universities need to continue and improve. Additionally, knowledge of AM needs to be passed on through commercial training programmes and knowledge transfer projects. Experts recommend that companies invest more in knowledge, skills and people than in machines. The current bottleneck is not necessarily due to a lack of machine technology, but rather a shortage of experts willing to promote AM technology and its benefits within the company. What could be called an “AM-intrapreneur” would be an evangelist for AM within a given company who has the exploitation of AM use cases within their annual targets. Governmental co-funding is an option to incentivize private personal and company training in AM. Knowledge development in the industry could be accelerated by dedicated transfer projects and using educational training programmes.
Implementing standards across the industry, e.g. in SMEs, has to be a key goal. The standardization committees working in AM should keep on pushing forward and must be supported. Additionally, the benefits of using standards must be shown to companies in a broad and joint framework. Also, companies and academia need to put the standardization work back on the agenda of their employees. Governments should be willing to incentivize the participation of companies, research institutes and universities in the standardization process, to ensure state-of-the-art information through dedicated funding opportunities.
Together with universities, research institutions and public partners, organizations need to take steps to improve the landscape for AM-related technologies in TRL 3–6 in order to push those to industrial applications. AM is a complex technology that still needs intensive research, but declining AM research is to be expected if funding opportunities stay at the current level. Streamlining the process of governmental funding is an additional requirement, especially for software developments, cost reduction and quality assurance.
Multiple experts reported that AM will benefit from a strong push in sustainability and can support, for example, carbon reduction. AM should be seen as a key part of the strategy enabling a sustainable transformation for governments and companies. Incentives such as funding or new regulations could drive the use of AM towards “cradle-to-cradle” (C2C)12 and a circular economy. Additionally, several industry experts predict that taxing emissions could lead to further adoption of AM. Companies developing a comprehensive sustainability roadmap incorporating AM can create new assets for stakeholders and customers.
Qualification could be a long-term drag on progress if no action is taken. Comprehensive research and a push towards less costly quality assurance must be undertaken. More and continuous investment into structured cooperation between part suppliers, end users, research, norming committees and regulation authorities could be a solution. For future industrialization, the qualification process must shift from an extensive part-by-part qualification approach to a general process qualification.
Digitization and consistent data availability along the whole process chain have to be improved, according to experts. Therefore, suitable data formats, standardized interfaces, opening of protocols and sharing of metadata should be enforced. Software and machine manufacturers need to collaborate and holistically improve the software process chain, especially when considering the evolving capabilities of AI.