Impossible Objects has made a groundbreaking announcement, revealing their revolutionary CBAM 25 3D printer, capable of printing fifteen times faster than the closest competitor. Set to be unveiled at the RAPID + TCT tradeshow in Chicago next month, this innovative 3D printer will have a significant impact on the world of mass production and industrial applications.
Impossible objects CBAM Slice
The CBAM 25 will become commercially available in early 2024, promising to bring 3D printing into the realm of volume manufacturing. By breaking the speed barrier, the CBAM 25 will deliver advanced materials with superior mechanical properties and tolerances, providing manufacturers with an unprecedented advantage over existing technologies.
Robert Swartz, Founder and Chairman of the Board at Impossible Objects, stated, “The CBAM 25 is the world’s fastest printer, and we are entering a new era of 3D printing with nearly unlimited material options at the speed of true mass production. This is a Moore’s law moment for 3D printing, and this is just the first step.”
The CBAM 25 utilizes high-performance composite materials, enabling engineers to design stronger, lighter, and more durable parts. Notably, the Carbon Fiber PEEK material set offers high chemical and temperature resistance and mechanical properties superior to most engineering plastics. Carbon Fiber PEEK parts are a suitable alternative for aluminum, tooling, spares, repairs, and end-use parts.
Impossible objects CBAM Layer
Impossible Objects is currently producing and selling parts in untapped 3D markets such as electronic tooling and for a broad range of applications, including aerospace, defense, and transportation industries. It is also replacing CNC machining with greater geometric freedom.
Steve Hoover, Impossible Objects’ CEO, emphasizes the importance of production speed with the new CBAM 25, stating, “With a fifteen times speed improvement over existing 3D printers, our new CBAM 25 completes the transition of 3D printing from its roots in prototyping to the heartland of manufacturing.”
Impossible objects CBAM Machine
The CBAM 25 is indeed a giant leap forward, pushing 3D printing into volume manufacturing, and opening new opportunities for industries to reshape and rethink their manufacturing processes.
The launch of the CBAM 25 marks a turning point for 3D printing, demonstrating the potential for exponential advancements in speed, material capabilities, and applications. For readers interested in learning more about this revolutionary technology, we recommend attending the RAPID + TCT tradeshow in Chicago, where the CBAM 25 will be unveiled. Additionally, stay informed on the latest developments in the 3D printing industry by following Impossible Objects and other leading companies.
By embracing the CBAM 25 and its potential, businesses can optimize their manufacturing processes, reduce costs, and create innovative products that push the boundaries of what’s possible in the world of 3D printing.
Additive manufacturing software is not a single application. Industrial AM depends on a connected toolchain that can preserve design intent, prepare the build, simulate process risk, communicate with equipment, schedule production, capture quality data and maintain traceability.
The best AM software stack is not the one with the longest feature list. It is the one that creates a controlled path from released design to accepted part.
The additive manufacturing software stack
Layer
Main job
Typical outputs
Requirements and PLM
Control product definition, revisions and approvals
Released CAD, drawing, specification and configuration
DfAM and computational design
Create manufacturable geometry that uses AM effectively
Topology-optimized parts, lattices, channels and design variants
Data preparation and repair
Validate and transform geometry for production
Repaired mesh or manufacturing geometry
Build preparation
Orient, support, nest and slice parts
Released build layout and machine-ready job
Process simulation
Predict distortion, thermal history or process risk
Compensated geometry, risk maps and process recommendations
Machine and build processor
Translate the job into equipment-specific instructions
Scan paths, exposure strategy and machine file
MES and production management
Schedule orders, resources and work steps
Travelers, capacity plan and work-in-process status
Monitoring and telemetry
Capture machine state and process signals
Layer images, alarms, sensor data and event logs
Quality and inspection
Connect measurements and nonconformances to the part
Inspection reports, CAPA, disposition and release evidence
Analytics and costing
Measure yield, throughput, cost and bottlenecks
Accepted-part cost, utilization and improvement priorities
1. Requirements, CAD and product lifecycle management
The workflow should begin with an authoritative product definition. CAD and PLM systems control part number, revision, material, critical characteristics and approvals. AM-specific tools should consume this released definition without creating uncontrolled copies.
Native CAD is preferable when complex features, assemblies, tolerances or design history must remain editable. Tessellated files such as STL can still be useful, but they discard much of the original design intelligence and can create resolution, unit and repair problems.
2. DfAM and computational design
Traditional CAD is effective for conventional geometry, but lattices, field-driven structures and highly complex thermal or fluid designs can require implicit or computational methods. Typical capabilities include:
Topology optimization and generative exploration
Lattice and cellular structures
Conformal channels and heat exchangers
Field-driven variation of thickness, density or feature size
Automated design families and mass customization
Manufacturing-constraint checks
Examples include integrated CAD platforms such as Siemens NX and Dassault Systèmes solutions, as well as computational design tools such as nTop. The selection should depend on geometry complexity, parameterization, simulation integration and the ability to transfer the design without losing fidelity.
3. Data preparation and geometry repair
Before a part enters build preparation, the software should identify open edges, inverted normals, self-intersections, thin regions, disconnected bodies and other geometry problems. Repair automation is useful, but every automatic change should be reviewable because it can alter design intent.
Materialise Magics remains a prominent example of dedicated data and build-preparation software. Autodesk Netfabb, 3DXpert and integrated CAD/CAM platforms also provide geometry-preparation functions. Compatibility should be confirmed for the exact process, machine and build processor rather than assumed from a generic feature list.
4. Build preparation
Build preparation converts a valid part into a manufacturing configuration. It typically includes:
Machine and build-volume selection
Orientation and placement
Support generation
Nesting and collision checks
Machining allowance and sacrificial features
Coupon and witness-specimen layout
Slicing and scan-vector generation
Estimated build time and material use
Orientation should not be optimized only for print time. It affects support removal, residual stress, anisotropy, surface condition, powder escape, machining and inspection access.
5. Process simulation
Simulation can reduce physical iteration by estimating distortion, recoater risk, support loading, thermal accumulation or residual stress. Different models serve different decisions:
Simulation level
Typical use
Main limitation
Fast inherent-strain or reduced-order model
Orientation, support and distortion screening
Requires calibration and simplifies local physics
Layer or path-level thermal model
Thermal history and local process analysis
High computation and data demand
Material or sintering model
Binder-jet shrinkage, distortion and setter design
Depends strongly on feedstock and furnace calibration
Structural performance simulation
Verify the final design under service loads
Needs representative material and defect assumptions
Examples include Simufact Additive, Autodesk Netfabb Local Simulation, Siemens integrated simulation and other process-specific tools. Simulation should be validated against coupons and representative parts before it is used for release decisions.
6. Build processors and machine connectivity
A build processor is the controlled interface between general build preparation and a specific machine platform. It may define exposure strategies, scan paths, parameter assignment and job transfer. Machine connectivity can be direct, through an OEM application, or through an approved partner integration.
EOSPRINT, Materialise Build Processors, Siemens machine kits and OEM software are examples of this layer. The critical questions are not only whether a file can be exported, but whether the approved parameter set, software version and machine configuration remain traceable.
7. MES, order management and the digital thread
As production grows, spreadsheets and shared folders become weak controls. AM-focused MES and workflow platforms can manage:
Order intake and technical review
Routing through printing, heat treatment, machining and inspection
Machine, furnace and labor capacity
Material-lot and powder-reuse history
Traveler execution and electronic signatures
Nonconformance and corrective action
Part-level genealogy and release records
Materialise CO-AM is one current example of a platform connecting order management, MES, machine telemetry, quality and analytics. General PLM, MES and QMS systems can also support AM when their data model captures AM-specific configuration and genealogy.
8. Monitoring, quality and inspection software
Machine monitoring software can collect layer images, melt-pool signals, recoater events, oxygen history, temperatures and alarms. These signals are valuable only when they are linked to machine, build, layer, part location and a validated decision rule.
Quality software should connect inspection plans, metrology results, NDT, material testing and deviations to the same part identity. Monitoring data does not automatically replace part inspection; it can support process understanding and risk-based acceptance only after correlation has been demonstrated.
9. Costing and production analytics
A useful costing model extends beyond machine hours. It should include:
Engineering and build-preparation labor
Material, supports, powder refresh and scrap
Machine, gas, energy and maintenance
Heat treatment, HIP, sintering and furnace occupancy
Support removal, machining and surface finishing
Inspection, documentation and rejected parts
Queue time and working capital
The most important metric is often cost per accepted finished part, not theoretical cost per build hour.
Current software examples by function
Function
Examples
Selection question
Integrated CAD and manufacturing
Siemens NX, Dassault Systèmes 3DEXPERIENCE/DELMIA
Can the organization keep native design, build preparation and post-processing in one controlled environment?
Computational DfAM
nTop and integrated generative-design tools
Can it handle the required lattices, fields and automated variants without fragile geometry?
Data and build preparation
Materialise Magics, Autodesk Netfabb, 3DXpert
Does it support the exact machines, support strategies and file-control requirements?
Process simulation
Simufact Additive, Netfabb Local Simulation and platform-specific simulation
Has the model been calibrated for the alloy, machine and route?
Machine execution
EOSPRINT, OEM software and approved build processors
How are parameters and software versions released and traced?
MES and digital thread
Materialise CO-AM and configured enterprise MES/PLM/QMS platforms
Can the system connect all downstream operations and part genealogy?
Software ownership and product strategy can change. Verify current licensing, support, cybersecurity, export-control and product-roadmap status before making a long-term platform decision.
How to select an AM software stack
Map the workflow. Document every transformation from released CAD through part acceptance.
Define authoritative systems. Decide where design, manufacturing, material and quality records are controlled.
List exact equipment. Check compatibility with machine models, firmware, build processors and inspection systems.
Test representative complexity. Use a real lattice, large build, support strategy and downstream route rather than a demonstration cube.
Measure interoperability. Record where geometry, metadata or parameters are lost between tools.
Validate simulation and automation. Compare software predictions with physical results.
Model total ownership cost. Include licenses, modules, APIs, training, customization, upgrades and support.
Review cybersecurity and retention. Protect IP and ensure records remain readable over the required life.
Run a controlled pilot. Measure preparation time, first-pass yield, traceability and accepted-part lead time.
Common implementation mistakes
Selecting software from screenshots rather than production workflows
Assuming one application can replace PLM, MES, QMS and machine software
Using STL as the only long-term product definition
Automating an unstable manual process
Ignoring post-processing, inspection and supplier data
Allowing machine files or parameters to remain on local computers without revision control
Buying simulation without a calibration and validation plan
Measuring printer utilization while ignoring accepted-part throughput
Conclusion
Industrial additive manufacturing software should be designed as a connected architecture rather than a list of standalone products. Start with requirements and configuration control, then select the smallest set of tools that can preserve design intent, execute the approved route and produce trustworthy acceptance evidence.
