The 2027 Digital Manufacturing Challenge invites the next generation of engineers to explore how AI can be integrated into digital manufacturing workflows to solve real-world manufacturing challenges.

We live in a world where data, connectivity, automation, and advanced manufacturing technologies are increasingly interconnected. The convergence of AI, additive manufacturing, digital twins, simulation, and intelligent automation creates new opportunities to rethink how products are designed, produced, inspected, and maintained through smarter, more adaptive manufacturing systems.

Students are challenged to develop innovative solutions that demonstrate how AI can be meaningfully applied to improve manufacturing performance. Successful submissions should go beyond simply using AI tools and clearly demonstrate measurable improvements in areas such as efficiency, quality, cost, manufacturability, sustainability, or engineering decision-making.

Some examples are included below to inspire submissions:

• Apply AI-driven generative design or topology optimization to reduce weight, material usage, or part count while maintaining or improving performance.
• Use machine learning to optimize additive manufacturing process parameters, toolpaths, or build orientation to improve quality and reduce defects.
• Develop AI-based approaches for in-process monitoring, defect detection, or post-process inspection using computer vision or sensor data.
• Create AI-enhanced digital twins that provide predictive insights, optimize manufacturing processes, or enable closed-loop production.
• Explore how AI can support repair, remanufacturing, or part reconstruction through intelligent analysis of 3D scan data.
• Improve supply chain or production planning using AI for inventory optimization, distributed manufacturing, or make-versus-buy-versus-additive manufacturing decision support.
• Design workflows that integrate AI throughout the digital thread, from design and production through inspection, assembly, maintenance, and repair.
• Investigate how engineers work alongside AI by validating AI-generated recommendations and ensuring product requirements, safety, and performance standards are achieved.

Constraints

• Apply only existing and accessible technologies, including commercially available or open-source AI tools and manufacturing methods.
• AI must play a meaningful role in the workflow through decision-making, optimization, analysis, or automation.
• Solutions should be practical and grounded in realistic manufacturing applications rather than speculative future technologies.
• Projects may focus on an individual component, subsystem, or complete system.
• Consider implementation feasibility, technical limitations, and practical deployment challenges.
• Where appropriate, address ethical considerations, data integrity, cybersecurity, and the reliability of AI-generated decisions.

Judging Considerations

Competitive submissions should clearly demonstrate the connection between:

• A real-world manufacturing problem.
• An AI-enabled solution.
• A practical digital manufacturing workflow.
• Evidence supporting the claimed results.
• A realistic assessment of feasibility, limitations, and implementation risks.

Multi-disciplinary teams of up to four members are encouraged, but not required.

AI Transparency Requirement

Because AI is central to the 2027 Digital Manufacturing Challenge, all AI use must be fully disclosed.

Teams are expected to provide searchable documentation describing how AI was used throughout the project, from initial research and concept development through the final submission. This may include prompt histories, exported conversations containing fewer than 200 words per response, summarized AI-use records, or equivalent documentation.

Failure to adequately disclose AI usage may result in score reductions or disqualification.

Deliverable

Complete and competitive submissions are expected in the form of a case study that clearly communicates the problem, methodology, and results, with particular emphasis on how AI contributed to the overall solution.

Supporting materials such as screenshots, AI outputs, simulation results, design comparisons, validation data, and lessons learned are encouraged.

All project files should be included in a ZIP folder (or equivalent) and uploaded through the submission platform. Files should include:

• STL files of your design (if applicable).
• The title of your entry on every file.
• A document following the Submission Requirements and Judging Criteria.
• Team member names, contact information, and résumés for circulation among potential employers.

Required Section: Role of AI

Every submission must include a dedicated section describing:

• The AI tools, models, or methods used.
• How AI was integrated into the workflow.
• Which decisions or outcomes were influenced by AI.
• The measurable benefits achieved, such as improved performance, quality, efficiency, or cost.
• The limitations, assumptions, and risks associated with the AI approach.

Teams are also required to submit a video (maximum five minutes) highlighting their project, creative process, and key lessons learned throughout the Digital Manufacturing Challenge.

Questions About the Digital Manufacturing Challenge?