The Ailab is where our theories meet physics. With 6 robot arms printing with DED and polymer FGF we’re able to test and validate our ideas and experiments in real life.
Aibuild Updates
This is our engineering notebook. It's a glimpse into the research, experiments, and ideas our team are exploring. Not everything you'll see here will make it into production software; some are bold experiments, others are foundational research. But each project represents our commitment to developing a platform that solves engineering challenges without disrupting the factory floor.
What Aibuild delivers right now.
Today, manufacturers gain unparalleled flexibility and control in additive toolpathing through our platform, automating complex workflows from design validation to print execution while integrating seamlessly with existing robotic and gantry systems.
What Aibuild will deliver in the future.
These capabilities will converge into an autonomous manufacturing intelligence that predicts, prevents, and optimizes across entire supply chains, making multi-material, multi-process LFAM production predictive with Aibuild analytics driving every manufacturing decision.
Finite Element Thermal Simulation
We've integrated Finite Element Thermal Simulation directly into Aibuild software for automatic optimisation. This is tailor-made for LFAM, bringing easier user experience and much faster computation speeds.
AM Copilot
Coming soon. Our V1 co-pilot delivers 10+ years of 3D printing knowledge from our expert team through AI assistance. V2 will scale this intelligence across diverse manufacturing processes, learning continuously from global production data to provide insights that exceed any single team's experience.
Interactivity with polylines
We're conceptualising interactive polyline editing to transform how users modify toolpaths directly within the interface. This early-stage concept would enable intuitive mouse-based selection and manipulation of polyline segments, vertices, and parameters to allow real-time adjustments to thickness, rotation, and speed without complex workflows. Users could simply click, drag, and edit toolpaths visually, though this interactive editing system remains in the concept phase.
How we're working towards it.
Every experiment and development serves dual purposes: addressing immediate pain points our users face today while laying groundwork for platform we’re building. Each project, whether it reaches production or not, teaches us something essential about the intersection of innovation and operational stability.
Current research, experiments, and ideas.
Thermoscan - interpass monitoring
Real-time thermal monitoring to maintain optimal interpass temperatures throughout the build process. While thermal simulation can predict interpass temperatures, it's not perfectly accurate and environmental conditions change during printing, both critical factors for print success and mechanical properties. Our system will continuously adjust parameters to maintain ideal thermal conditions.
ML prediction of print settings based on material technical data
We're building automated material profiling to eliminate time-consuming prototyping cycles. Currently, printing with new materials requires extensive manual testing to find optimal settings. Our AI-driven approach will automatically generate printing parameters for new materials, dramatically reducing setup time and accelerating material adoption.
Make geodesic smarter
We're enhancing geodesic slicing to deliver the automation benefits with superior results. While geodesic is the most automated slicing method, it's underutilised due to poor output quality. Our improved geodesic algorithms will combine full automation with the high-quality results manufacturers demand.
Best setting to reduce gaps in zigzag infill
Zigzag remains the most popular 100% infill setting, but it creates contact gaps with outer walls that compromise part integrity. Our new infill approach will ensure complete wall-to-infill bonding for stronger, more reliable parts.
Orient tool in previous layer based on cantilevering
We're creating hybrid slicing that automatically identifies and optimizes challenging geometries within otherwise simple prints. Most prints succeed with planar slicing except for specific cantilevering areas that need different treatment. Our intelligent system will automatically detect these problem zones and apply optimised slicing strategies while maintaining planar efficiency elsewhere.
This is where complexity becomes clarity. With user-centered design and interface innovation, we’re crafting the digital experiences that make advanced manufacturing technology accessible and powerful for every user at every level.
Ai on mobile
We're working on an idea to develop mobile AI access to bring intelligent manufacturing guidance directly to the factory floor. Our mobile platform would enable operators to get real-time AI insights, troubleshooting assistance, and parameter optimisation recommendations without leaving their workstation, making expert-level decision-making available instantly wherever production happens.
Ai suggessions
We're exploring AI-powered printing method recommendations that could automatically select optimal approaches for each geometry. This concept would analyse part designs and manufacturing requirements to suggest the best combination of slicing strategies, infill patterns, and process parameters, potentially eliminating guesswork and ensuring first-time-right production.
Gcode Viewer
We're conceptualising an advanced GCode viewer that would provide visual verification and optimisation insights before printing. This early-stage concept could allow users to preview toolpaths, identify potential issues, and simulate print behaviour so manufacturers could catch problems digitally rather than discovering them mid-print.
The Geometry team is where our designs meet reality. With advanced computational modelling and parametric design tools, we’re able to transform architectural visions into precise, buildable structures that push the boundaries of what’s possible.
Volumetric Distance Slicing
This project investigates an alternative to geodesic slicing by introducing a volumetric distance slicing approach. Rather than relying solely on geodesic distances measured along the object’s surface, this method computes distances within the object's interior using a voxelized volumetric representation. Based on this internal distance field, a series of slicing meshes are generated to represent individual layers of the object.
Weaving
This project explores the development of automated weaving pattern algorithms for metal 3D printing, with a focus on efficiently filling solid objects using structurally optimized internal patterns. These patterns are particularly critical in metal additive manufacturing, where material usage, mechanical strength, and thermal stress distribution must be carefully balanced.
The end goal.
We’re building toward a future where additive manufacturing transitions from experimental technology to core production capability. Our platform will enable manufacturers to confidently scale AI-powered, bespoke solutions across their entire operation while maintaining the integrity and continuity of the factory floor.
