In aerospace manufacturing, precision is non-negotiable. Components must meet the highest standards of structural integrity, reliability, and safety — every time. Yet the casting process, a foundation of aerospace production, has historically been prone to variability. Small shifts in environmental or process variables such as the ambient or alloy temperature, humidity, or the flow rate can create defects that compromise quality, leading to costly rework or scrap, and hence extending production cycles.
Traditional defect prediction relies heavily on human expertise and on statistical models and post-process analysis, which are slow and limited in scope. They rarely provide actionable insight while casting operations are still underway. As a result, defects are often caught only after production, when corrections are costly and time-consuming.
PhysicsX partnered with a leading aerospace manufacturer to reimagine this process. Together, we deployed AI-driven defect prediction and casting mold optimization tools directly into the production environment, enabling real-time quality control, higher yields, and faster throughput. The result: a more consistent, efficient, and cost-effective casting process that is already setting new benchmarks for aerospace manufacturing.
The Challenge: Variability and Inefficiency in Casting
Casting is one of the most resource-intensive steps in aerospace manufacturing. Even minor defects can render components unusable, creating waste, inflating costs, and delaying delivery schedules. Scrap rates are a persistent challenge, not only from a cost perspective but also in terms of sustainability and operational efficiency.
The key barriers to improvement have been:
- Defect unpredictability: There are no two casting processes that are the same, as environmental and process variables are difficult to fully control and existing statistical tools are too slow to catch defects before they happen.
- Limited feedback loops: Without real-time insights, process corrections can only be applied after the fact, when often it is already too late, leading to scrap parts.
- Mold design complexity: Optimizing the geometry of the mold and the gating system to minimize casting defects is difficult, and design decisions often rely on trial-and-error rather than rapid simulation and feedback.
The manufacturer sought a step change: an AI solution capable of predicting defects as they emerge, guiding operators during production, and improving gating system designs to minimize defects and accelerate throughput.
The Approach: Embedding AI on the Production Line
PhysicsX developed and deployed an AI-powered system tailored to the complexities of aerospace casting. The solution combined predictive analytics with AI-driven optimization, embedding intelligence directly into production workflows.
Key capabilities included:
- Real-time defect prediction: Machine learning models trained on casting process data and mold geometries to predict defects before they formed, running over 100x faster than previous methods.
- AI for design optimization of the mold geometry and gating system: Models explored design alternatives for gating systems, identifying configurations that improved material flow and reduced variability in the foundry.
- Production integration: The AI models were deployed directly to casting lines, providing immediate feedback to operators and enabling dynamic adjustments during live production.
- Scalable framework: The architecture was designed for rapid replication across multiple production lines, creating a path for plant-wide deployment.
The PhysicsX platform was deployed on AWS to support the intensive AI/ML and simulation workloads required. Amazon Elastic Kubernetes Service on EC2 provided the orchestration layer, running on GPU-enabled g4dn and g5 instances to deliver fast model training, inference, and fine-tuning. For large-scale data handling, the solution combined Amazon FSx with Amazon S3, ensuring high-throughput storage access and streamlined processing across the development pipeline. Together, this architecture demonstrated how AWS’s scalable infrastructure and high-performance compute services can be integrated with the PhysicsX platform to accelerate engineering innovation.
Results that Reshape the Workflow: Quality, Speed, and Cost Savings
The results have been transformative. Within weeks of deployment, the manufacturer achieved:
- Reduced scrap rates: Early detection and correction of casting deviations dramatically cut defect-related waste.
- Accelerated throughput: Real-time AI feedback shortened production cycles, increasing overall output.
- Cost reduction: Lower scrap rate and faster cycles reduced operational costs while improving equipment utilization.
- Improved consistency: AI-driven optimization supported higher reliability of cast components, contributing to long-term structural performance.
Compared to legacy methods, the AI models operated more than 100 times faster, enabling insights that were previously out of reach during live production. The ability to dynamically adjust process parameters in real time has unlocked a new level of manufacturing responsiveness and efficiency.
Beyond Quality Control: Toward AI-Native Production
This project highlights a fundamental shift: AI is no longer just a design-stage tool — it is becoming a production-stage partner. By bringing real-time intelligence into the foundry, manufacturers can move beyond reactive quality control toward proactive defect prevention.
For aerospace, where component quality is tied directly to safety and mission success, the implications are profound. But the relevance extends far beyond this sector. Any industry that relies on high-precision casting or complex manufacturing workflows can benefit from similar AI-native approaches.
The key advantages are clear:
- Faster iteration without the burden of excessive physical testing.
- Lower environmental footprint through reduced scrap and waste.
- Higher production efficiency by maximizing yield and minimizing downtime.
- Transferable insights that can be scaled across production lines and facilities.
Lessons from the Foundry Floor
Deploying AI in live production environments is not just a technological challenge — it requires close collaboration between engineering and operations teams. Successful integration means ensuring models respect real-world manufacturing constraints, from temperature ranges to material handling practices.
Some of the lessons from this project include:
- AI models can deliver unprecedented speed and accuracy, but must be trained with domain-specific data.
- Operator trust is essential: real-time insights must be transparent and actionable to drive adoption on the factory floor.
- Continuous monitoring ensures models adapt as processes evolve, sustaining value over time.
- Scaling across lines compounds benefits, multiplying efficiency gains plant-wide.
Casting the Future with AI
This collaboration demonstrates the power of AI to solve real-world manufacturing challenges that have resisted incremental improvement for decades. By embedding intelligence in the casting workflow, the manufacturer has taken a decisive step toward AI-native production — where every stage of design, simulation, and manufacturing is informed and accelerated by machine learning.
At PhysicsX, we are making engineering AI-native. From aerospace & defense to automotive, from design concept to production floor, we are working with partners to re-architect the workflows that define modern industry. Casting optimization is just one example of how AI can unlock performance, reduce cost and waste, and set new standards for reliability and efficiency.