Validation of a palletizing solution in the automotive market

In a recent feasibility study with FEHST Componentes, Augmented Labs was asked to simulate and validate a palletizing concept for an injection molding environment. The goal wasn’t to “make a nice robot animation”, it was to build a realistic process simulation that could answer a simple, but high-impact question:

Can the automation concept keep up with production requirements without becoming a bottleneck?

This article shares what we built, what we validated, and why simulation can be a reliable path to confident automation decisions, especially in early-stage concepts where changes are still easy and inexpensive. Due to confidentiality restrictions, the workpiece shown in this article is not the actual component used in the simulation.

The challenge: validate a concept before committing to integration

The starting point was clear:

• Injection machine output: 8 workpieces per batch
• Machine cycle time: aprox. 20 seconds (per batch)
• Part Blister Capacity: 14 workpieces

The proposed concept was straightforward: parts exit the injection machine, travel on a conveyor to a pick area, and a robot picks the parts and places them into blisters. Once a blister is filled, it is moved to an output area for an operator to remove.

What needed validation was equally clear: would the robot and handling sequence realistically match the machine pace?

What we simulated end-to-end

In this pilot, we didn’t simulate “robot motion only”. We built a functional process simulation that included the relevant subsystems and the logic linking them together:

Blister management (empty → fill → output)

We simulated the full blister lifecycle:

• An empty blister area (source)
• A fill position in front of the robot
• An output row where filled blisters would be placed to generate a batch for operator pickup

This is often where concepts break down in the real world — not because of the robot itself, but due to handling overhead and the “hidden time” of tray/blister logistics.

Robot model + gripper + real sequence timing

Our team:

• Modeled and imported an EC66 robot, from Elite Robots
• Imported the necessary assets (robot, station/table structure, blisters, workpieces, etc.)
• Modeled a gripper
• Implemented the robot sequences to estimate pick-and-place time, while validating:
• • reachability
  • collisions
  • sequencing constraints

Two approaches studied and compared

To make the evaluation objective, we implemented and simulated two alternative strategies:

1. Direct pick-and-place to blisters, then move filled blisters later

• Robot picks parts from the conveyor
• Places parts into a blister
• Later in the process, blisters are placed in the output area for operator removal

2. Robot positions the empty blister first, then fills as parts arrive

• Robot picks an empty blister and positions it in front of itself
• Robot picks parts as they arrive and fills the blister
• Once filled, robot moves the blister to the output row and repeats

By simulating both flows end-to-end, we could compare not just “robot speed”, but total process behavior.

Results

The simulation quickly highlighted a critical reality:

• In both approaches, the robot cycle time was significantly above the required pace
• The best case scenario was still ~100% above the expected cycle time

In other words: the concept, as designed, would not follow the injection machine throughput and would create a bottleneck on the production line.

Even without going into full industrial commissioning detail, the simulation provided strong evidence to support an early decision: iterate the concept before building it physically.

What we validated early (and why it mattered)

Even at PoC scope, this simulation answered key engineering and decision-making questions early:

• Throughput feasibility: can the concept keep up with 8 parts every 20 seconds?
• Handling strategy impact: how much time is “lost” moving blisters vs. filling them?
• Cycle time estimation: what is the realistic timing once all steps are included (not only pick motion)?
• Reachability & collision risk: are we introducing constraints that slow down the sequence?
• Bottleneck identification: where does the process lose time and accumulate delays?
• Concept decision support: should the team redesign the flow, change the robot strategy, add buffering, or revisit the system architecture?

This helped FEHST Componentes decide quickly and confidently — before time and budget were committed to a concept that wouldn’t meet performance requirements.

Why simulation matters early

Simulation is not just a “nice-to-have”. It can be one of the most cost-effective steps in automation engineering because it helps teams identify:

• expected throughput
• bottlenecks
• reachability/collision issues
• sequencing risks
• concept feasibility

…before moving into a full-scale physical integration where changes become slow and expensive.

At Augmented Labs, we help clients and partners study their solutions early and validate what works — and what doesn’t — using ROBOTICA Studio.