To enable and accelerate the adoption of safe and compatible integrated AI-assisted generative design for additive manufacturing workflows, government agencies should focus on standards and certification, shared infrastructure, procurement practices, workforce development, and data governance.
The following are recommendations to maximize those outcomes:
Promote standards and certification efforts
Governments offer support of standardization regulatory bodies that support coordinated initiatives that close gaps across the AM lifecycle, from design and materials to qualification and nondestructive evaluation. Groups like the Additive Manufacturing Standardization Collaborative (AMSC) work to identify and prioritize standardization needs in design, materials, process control, data, and certification. Such support adds legitimacy and accelerates critical developments for regulatory compliance, export competitiveness, and cross‑industry interoperability.
Invest in Shared Infrastructure and Public Testbeds
Public funding for neutral, open AM testbeds, including publicly accessible machine facilities, open data platforms, and metrology testbeds, will give small to medium-size enterprises and research institutions access to advanced AM capabilities without getting locked into a specific vendor. For example, National Institute of Standards and Technology’s AM metrology testbeds provide unique infrastructure to study process behavior and quality across varied materials and systems. These investments lower technical barriers, support process repeatability research, and help SMEs validate AI‑assisted designs and parts without shouldering full infrastructure costs.
Leverage Procurement Policy to Drive Adoption of Standards‑Compliant Solutions
Government procurement, in sectors such as defense, aerospace, medical, or infrastructure, should mandate certification, digital provenance, and interoperability requirements for vendors adopting AM. This includes:
● Requiring digital chain‑of‑custody and traceability connected to quality‑assured design data,
● Mandating compliance with recognized AM standards in procurement contracts,
● Create incentive structures and forcing functions for AM equipment manufacturers to comply with Smart Product Recipes (SPR),
● Avoiding proprietary, closed stacks that inhibit broader ecosystem access
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Procurement reforms create demand for open, interoperable solutions that scale across suppliers and regions, reducing vendor consolidation risk.
Support Reskilling and Workforce Development
Training, education and credentialing is crucial to prepare the workforce for AI advancement in AM, and the government can provide funding for education initiatives that target developing engineers, technicians, quality specialists, and regulators. Priority areas include:
● CAD and generative design,
● Materials engineering and process optimization,
● Quality assurance and nondestructive testing,
● Data science and machine learning applications in manufacturing,
● Cybersecurity for digital manufacturing systems.
Public‑sector support for certificate programs, apprenticeships, and collaboration with universities and standards bodies, such as the Additive Manufacturing Center of Excellence offering educational courses, strengthens the talent pipeline and ensures industry‑aligned competencies are widespread.
Establish Baseline Data Regulations and Cybersecurity Standards
As AI and AM become intertwined, governments need to define regulations that provide cybersecurity and integrity standards to protect IP, ensure data provenance and ensure manufactured parts integrity.
This is essential in sectors where quality, safety, and supply‑chain security are paramount (e.g., medical, defense, aerospace). While specific policies vary across agencies, the broader need for strong data and cybersecurity frameworks is widely recognized in industry policy discussions.
Encourage Public‑Private Collaboration
Governments can help bring together between its agencies, academia, and private industry to collaborate on shared goals such as materials consortia, metrology alliances, and capability‑building initiatives.
Such collaborations can create best practices, standards, and shared research outcomes while ensuring SMEs have access to AI innovations in AM.