1. PAMRIC Project – UK National Physical Laboratory Investigated thermophysical properties of key casting alloys and mould materials: • Heat capacity, enthalpy, fusion enthalpy • Thermal diffusivity and conductivity • Thermal expansion, density, solidus/ liquidus temperatures Final report available at:
https://eprintspublications.npl.
co.uk
2. DISIC Project – Shell Material Investigations A consortium led by the National Physical Laboratory, with participation from EICF members and University of Birmingham, explored: • Mechanical and physical properties of shell materials • Effects of composition and processing on performance Report also hosted at:
https://eprintspublications.npl.
co.uk
3. FOCAST Consortium – University of Birmingham An academic-industry collaboration covering: • Wax material properties and injection parameters • Shell stucco methods and material innovations • Dewaxing process modeling and shell interactions • Gating effects on mechanical properties • Metal flow modeling and mould filling dynamics Data held by the University’s IRC in Materials Processing
4. Historical Data (1982–1992) – UK National
Engineering Laboratory This archive includes shell and core property data
developed through systematic testing of materials supplied by industry. While not yet digitized, this material is a prime
®
candidate for structured digital transformation and eventual inclusion in AI-based learning systems.
A Strategic Imperative The adoption of AI within the investment casting sector is not merely a possibility – it is becoming a necessity for competitiveness and resilience. We must ensure that the past informs the future by systematically collecting, digitizing, and organizing the wealth of knowledge accumulated over decades. “We have the foundational data and collective
experience. What we now need is the framework and commitment to extract, structure, and apply this knowledge intelligently.” The EICF and its partners could play a pivotal role in this
effort by initiating a digital heritage and AI-readiness project aimed at curating historical data and research reports, ensuring accessibility, and enabling future applications of Artificial Intelligence in investment casting.
Conclusion Artificial Intelligence has the potential to augment human expertise and unlock predictive insights from data accumulated over decades. To realize this potential, the investment casting community must undertake a concerted effort to digitize and mine the vast, often fragmented archives of technical knowledge. The future will be shaped not just by new discoveries,
but by how intelligently we can rediscover, reinterpret, and re-purpose what we already know.
Based on the EICF project “EICF Knowledge Reclamation & AI Readiness Initiative (KRAIR)
April 2025 ❘ 11
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