From Standalone Machines to Automated Additive Manufacturing: How AM Environments Evolved
- Authentise Team
- Dec 19
- 3 min read
The Early Days: Additive Manufacturing as Standalone Machines
In its early industrial adoption, additive manufacturing was treated much like a lab tool.
A typical setup looked like this:
One or two AM printers
Jobs prepared manually
Scheduling handled informally
Material tracked in spreadsheets
Post-processing organised ad hoc
This model worked - at small scale.
But as soon as production volumes increased, cracks appeared. Printers sat idle. Jobs stalled. Materials went missing. Documentation lagged behind reality.
These issues weren’t caused by poor machines. They were caused by isolated workflows - a theme explored in The Real Cost of AM: Why Scaling Additive Isn’t Just About the Printer.
Why Standalone Machines Don’t Scale
As organisations attempted to scale additive manufacturing, common problems emerged:
No central view of work in progress
Manual job prioritisation
Unclear material availability
Disconnected post-processing steps
Limited traceability
Even with advanced printers, operations remained reactive.
This is why many teams hit a plateau and start asking:
Why is additive manufacturing so hard to scale?
Why do our printers sit idle?
Why doesn’t automation seem to help?
The answer lies in environment design, not machine capability.
The Shift: From Machines to Manufacturing Environments
Automated additive manufacturing didn’t emerge because printers became faster.
It emerged because systems began to connect the entire workflow.
Modern AM environments coordinate:
Design and version control
Build preparation
Scheduling across machines
Material availability and reuse
Post-processing capacity
Inspection and documentation
This shift is explored in depth in our article:👉 The Role of Automation in Optimising 3D Printing Processes | Additive Manufacturing Workflows
Automation only works when these steps operate as a single, visible system.
What “Automated Additive Manufacturing” Actually Means
A common misconception is that automation equals robotics.
In reality, automated additive manufacturing is about decision automation, not just physical motion.
True automation includes:
Automatically prioritising jobs based on constraints
Assigning work to available machines
Releasing material at the right time
Coordinating post-processing steps
Capturing data without manual intervention
This is why automation fails when context is missing - a challenge discussed in Manufacturing Automation Software: Why Automation Fails Without Context & How to Fix It.
The Role of Connected Systems in Automation
Automation depends on shared understanding across the workflow.
That’s where system layers come in:
Manufacturing Execution & Workflow
Solutions like Authentise Flows coordinate work across printers, people, and processes - turning isolated machines into a managed production environment.
This aligns closely with the ideas explored in Understanding Manufacturing Operations: How an MES Solves Key Problems in Production and Additive MES: The Essential Software Behind Scalable, Repeatable Additive Manufacturing.
Digital Design Control
Without controlled design access and versioning, automation breaks down quickly. A Digital Design Warehouse ensures the right design is always used, approvals are clear, and traceability is maintained - reducing rework and downtime.
See How Digital Design Warehousing Reduces Downtime and Boosts Supply Chain Resilience in AM for a deeper look.
Materials & Inventory Visibility
Automated AM environments require confidence in material status. Structured Materials Management replaces manual logs and enables reliable scheduling and reuse tracking.
This is a core theme in Material Management in Additive Manufacturing: How to Reduce Waste, Increase Traceability, and Strengthen Quality and Why Manual Powder Logs Fail in Additive Manufacturing (And What to Use Instead).
Capturing Engineering Intent
Automation doesn’t remove humans - it relies on their knowledge. Tools like Threads help capture decisions, qualification context, and intent so automation remains aligned with engineering reality.
This supports the broader concept of a digital thread, discussed in How AM Workflow Software Enables a True Digital Thread - From Design to Post-Processing.
Why Automation Without Systems Still Fails
Many organisations attempt to automate before fixing their foundations.
The result:
Robots waiting for instructions
Printers waiting for jobs
People filling gaps manually
Automation amplifying chaos rather than reducing it
This is why successful AM operations focus first on workflow visibility and control, then layer automation on top.
It’s also why scaling additive manufacturing is fundamentally a systems challenge, as explored in Why Additive Manufacturing Struggles to Scale - and How to Fix It.
Automated AM Is an Environment, Not a Feature
Automated additive manufacturing isn’t something you “add on” to printers.
It’s the outcome of:
Connected workflows
Shared data
Clear constraints
Coordinated execution
When these are in place, automation becomes natural - not forced.
Or put simply:
Standalone machines enable printing.Connected environments enable production.
Where to Go Next
If you’re moving from standalone machines toward automated additive manufacturing, these articles explore the next steps:
Each builds on the same principle: automation succeeds when additive manufacturing is treated as a system, not a collection of machines.
