01
Compose
151 reusable components across controls, conveyors, robotics, process, building, street and connection-hardware packs.
Build a factory from reusable PBR components, generate its PLC tags, write the logic, inject faults and watch the scene move—without installing a vendor IDE or Windows-only desktop simulator.
Orbit every model in the browser: conveyors, controls, robots, process equipment, street furniture and scenario props. These are the same PBR assets used by guided 3D scenarios and the Pro 3D Sandbox.
Public viewer is free. Guided 3D starts on Basic; composing and saving custom 3D scenes is Pro.
151 real-time factory components
Load the interactive viewer when you are ready to orbit the models.
Loads 3D only after your click
One connected workflow
The component contract is shared between the public viewer, guided scenarios and custom builder. A conveyor behaves like the same conveyor everywhere.
151 reusable components across controls, conveyors, robotics, process, building, street and connection-hardware packs.
Every functional object exposes typed input and output ports that generate collision-safe PLC tag names.
Write ladder or Structured Text in the same workspace and import selected-object or whole-scene tags.
PLC state drives deterministic motion, signal lamps, analogue progress and realistic failure states.
Honest category boundary
Use it to learn sequencing, interlocks, I/O, faults and spatial machine behaviour. Do not use it as evidence that a production line is mechanically safe or cycle-time capable.
CAD ingestion, hardware network emulation, safety certification and high-fidelity mechanical commissioning are outside the current product scope.
| Need | Best fit |
|---|---|
| Learn PLC logic in a visual factory | 3D Sandbox or guided 3D scenarios |
| Connect a vendor IDE to external scene I/O | Factory I/O or vendor-compatible tooling |
| Validate production mechanics and networks | Production virtual commissioning stack |
| Model throughput and queues statistically | Discrete-event simulation software |
Each component has a stable root, physical scale, ground-contact pivot, PBR material contract, motion classification, I/O ports, snap ports and failure modes. That lets us compose many scenarios without duplicating scene code or animation logic.
A sandbox proves your machine can move. Automated scenario tests prove your program meets a specification. We ship both: open composition for exploration and controlled tasks for objective learning evidence.
Runnable simulator field guide
Direct answer
3D PLC simulator becomes useful when it connects the scene, controlled equipment, i/o list, initial state, acceptance sequence and reset policy with plc tags through simulated i/o to actuators, material position and independent feedback, then proves a complete start, transfer, stop and reset cycle from a known scene state under normal, boundary, fault and recovery conditions. The objective is a repeatable engineering or learning result, not merely activity inside a page or tool.
This guide is written for pLC learners and instructors who need to connect controller code to visible sensors, actuators, material flow and machine state. The intended result is specific: the learner can map I/O into a 3D scene, prove a complete sequence and distinguish visual motion from controller, electrical and feedback evidence.
System map / 02
Treat these as connected checkpoints. Each checkpoint has an expected state, an observable state and a boundary to the next part of the system. That structure prevents a software indication from being mistaken for physical proof.
the scene, controlled equipment, I/O list, initial state, acceptance sequence and reset policy. For 3D PLC and factory simulation, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation.
PLC tags through simulated I/O to actuators, material position and independent feedback. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected.
a complete start, transfer, stop and reset cycle from a known scene state. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability.
simultaneous parts, blocked sensors, delayed actuators, restart and maximum accumulation. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path.
an I/O mapping, scene-state, sequence, timing or feedback mismatch. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result.
the behavioral case recreated against the intended simulation or physical equipment. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment.
Procedure / 03
Run the steps in order the first time. Later, the same structure becomes a diagnostic loop: define the expected condition, observe the boundary, interpret the difference and choose one proving action.
Convert the scene, controlled equipment, i/o list, initial state, acceptance sequence and reset policy into initial conditions, one stimulus and observable pass criteria.
Evidence: Another person can repeat the case without guessing the intended result.
Avoid: Using page completion or an animation as the acceptance criterion.
Document plc tags through simulated i/o to actuators, material position and independent feedback and name who owns each state or decision.
Evidence: Every request and result has a source, destination and useful inspection point.
Avoid: Using the same value as command, status and independent feedback.
Apply a complete start, transfer, stop and reset cycle from a known scene state from a clean start and record the expected evidence.
Evidence: Repeated runs produce the same bounded result.
Avoid: Changing several parameters before a baseline exists.
Test simultaneous parts, blocked sensors, delayed actuators, restart and maximum accumulation without changing the acceptance contract.
Evidence: Limits, timing and restart behavior reach defined states.
Avoid: Testing only one ideal sequence.
Introduce or analyse an i/o mapping, scene-state, sequence, timing or feedback mismatch and locate the first disagreement.
Evidence: The proving action distinguishes the leading hypotheses.
Avoid: Resetting, forcing or replacing before evidence is retained.
Complete the behavioral case recreated against the intended simulation or physical equipment and repeat the affected regression cases.
Evidence: A run is complete only when the requested behavior, stop behavior, fault response and recovery are observable from a fresh initial condition.
Avoid: Treating an acknowledged message or one successful rerun as handover.
Diagnostic matrix / 04
The table is a reasoning aid, not a parts-replacement chart. Preserve the initial symptom, inspect the named boundary and use the interpretation to choose the next controlled test. Site safety procedures and equipment manuals remain authoritative.
| Observed symptom | Inspect | Interpretation | Next proving action |
|---|---|---|---|
| The expected result is unclear | Requirement, initial state, actor, stimulus, units and pass condition | The operator, programmer and reviewer may be solving different versions of the task. | Rewrite one observable acceptance case before continuing. |
| Internal state changes but the outcome does not | Request, final owner, output or service boundary and independent feedback | A software or interface indication proves intent at one layer, not the complete outcome. | Trace the first boundary after the changing state. |
| Normal case passes but an edge case fails | Limits, timing, simultaneous events, reset and restart assumptions | The implementation contains a hidden assumption exposed by the changed condition. | Add the failed boundary as a permanent regression case. |
| The failure disappears after reset | Original symptom, histories, diagnostics, timestamps and active cause | Reset changed evidence or state without proving the initiating cause. | Reproduce under a controlled condition and preserve pre/post-event data. |
| Simulator and target disagree | Model boundary, software version, task timing, I/O behavior, data types and configuration | A learning model and the intended target do not share one of the recorded assumptions. | Reduce the case and verify against current target documentation. |
| The result cannot be explained | Prediction, observation, proving action, alternative hypotheses and limitations | Activity occurred but the evidence is not yet transferable or reviewable. | Have the learner defend the signal path and repeat a changed case. |
Product evidence / 05
The browser runtime joins editable control state to visible I/O and machine or process behavior, allowing the same initial conditions and stimuli to be replayed.
The browser scene is a training model, not a collision, cycle-time, structural, electrical, safety or production digital-twin validation environment.
Commissioning notebook / 06
Use these as written briefs rather than click-through instructions. For every case, state the expected condition before acting, retain the first useful observation and explain why the final result proves the requirement. A different program or component choice can still be correct when it produces the same bounded behavior and evidence.
Case 01
predict → observe → prove
Engineering context. the scene, controlled equipment, I/O list, initial state, acceptance sequence and reset policy. For 3D PLC and factory simulation, record the initial condition, actor, requested change, observable result and stopping condition before selecting a tool or implementation. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Write the acceptance case” stage of the workflow: convert the scene, controlled equipment, i/o list, initial state, acceptance sequence and reset policy into initial conditions, one stimulus and observable pass criteria. The acceptance record should show this result: another person can repeat the case without guessing the intended result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The expected result is unclear” as one bounded deviation. Inspect requirement, initial state, actor, stimulus, units and pass condition The working interpretation is that the operator, programmer and reviewer may be solving different versions of the task. The next proving action is to rewrite one observable acceptance case before continuing. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using page completion or an animation as the acceptance criterion. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What is a 3D PLC simulator? A defensible short answer is: It runs PLC-style logic against a visual machine model so inputs, outputs, motion and process state can be observed together.
Case 02
predict → observe → prove
Engineering context. PLC tags through simulated I/O to actuators, material position and independent feedback. Separate request, internal state, output or service, physical or user-visible result and independent feedback so each boundary can be inspected. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Build the map” stage of the workflow: document plc tags through simulated i/o to actuators, material position and independent feedback and name who owns each state or decision. The acceptance record should show this result: every request and result has a source, destination and useful inspection point. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Internal state changes but the outcome does not” as one bounded deviation. Inspect request, final owner, output or service boundary and independent feedback The working interpretation is that a software or interface indication proves intent at one layer, not the complete outcome. The next proving action is to trace the first boundary after the changing state. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is using the same value as command, status and independent feedback. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: Can a 3D PLC simulator replace Factory I/O or a vendor digital twin? A defensible short answer is: No single tool fits every purpose. Compare PLC connectivity, physics, scenes, fault injection, deployment and target-validation requirements with the same acceptance case.
Case 03
predict → observe → prove
Engineering context. a complete start, transfer, stop and reset cycle from a known scene state. Run more than one cycle from a known state and retain the values, timings or artifacts that demonstrate repeatability. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Run the baseline” stage of the workflow: apply a complete start, transfer, stop and reset cycle from a known scene state from a clean start and record the expected evidence. The acceptance record should show this result: repeated runs produce the same bounded result. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Normal case passes but an edge case fails” as one bounded deviation. Inspect limits, timing, simultaneous events, reset and restart assumptions The working interpretation is that the implementation contains a hidden assumption exposed by the changed condition. The next proving action is to add the failed boundary as a permanent regression case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is changing several parameters before a baseline exists. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What should I learn first about 3D PLC and factory simulation? A defensible short answer is: Start with the operating contract and evidence path: the scene, controlled equipment, i/o list, initial state, acceptance sequence and reset policy, followed by plc tags through simulated i/o to actuators, material position and independent feedback. Add advanced features only after the baseline is predictable.
Case 04
predict → observe → prove
Engineering context. simultaneous parts, blocked sensors, delayed actuators, restart and maximum accumulation. Choose minimum, maximum, simultaneous, delayed or restart conditions that reveal assumptions hidden by the happy path. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Challenge assumptions” stage of the workflow: test simultaneous parts, blocked sensors, delayed actuators, restart and maximum accumulation without changing the acceptance contract. The acceptance record should show this result: limits, timing and restart behavior reach defined states. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The failure disappears after reset” as one bounded deviation. Inspect original symptom, histories, diagnostics, timestamps and active cause The working interpretation is that reset changed evidence or state without proving the initiating cause. The next proving action is to reproduce under a controlled condition and preserve pre/post-event data. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is testing only one ideal sequence. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: How do I practise 3D PLC and factory simulation effectively? A defensible short answer is: Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
Case 05
predict → observe → prove
Engineering context. an I/O mapping, scene-state, sequence, timing or feedback mismatch. Preserve the first symptom, divide the system at a measurable boundary and change one condition only after predicting the result. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Isolate one failure” stage of the workflow: introduce or analyse an i/o mapping, scene-state, sequence, timing or feedback mismatch and locate the first disagreement. The acceptance record should show this result: the proving action distinguishes the leading hypotheses. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “Simulator and target disagree” as one bounded deviation. Inspect model boundary, software version, task timing, I/O behavior, data types and configuration The working interpretation is that a learning model and the intended target do not share one of the recorded assumptions. The next proving action is to reduce the case and verify against current target documentation. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is resetting, forcing or replacing before evidence is retained. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: What counts as proof of competence? A defensible short answer is: A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Case 06
predict → observe → prove
Engineering context. the behavioral case recreated against the intended simulation or physical equipment. Restore normal state, remove temporary changes, repeat affected checks and document which claims remain limited to the learning environment. Begin with a written normal condition and identify which request, state, physical result or communication value will provide independent confirmation. Do not begin by changing the configuration; the initial state is part of the evidence and should remain reproducible.
Controlled setup. Use the “Close the evidence loop” stage of the workflow: complete the behavioral case recreated against the intended simulation or physical equipment and repeat the affected regression cases. The acceptance record should show this result: a run is complete only when the requested behavior, stop behavior, fault response and recovery are observable from a fresh initial condition. Record initial conditions, the exact stimulus and the observation point so another learner can repeat the case without relying on your memory.
Fault challenge. Introduce or analyse “The result cannot be explained” as one bounded deviation. Inspect prediction, observation, proving action, alternative hypotheses and limitations The working interpretation is that activity occurred but the evidence is not yet transferable or reviewable. The next proving action is to have the learner defend the signal path and repeat a changed case. Change only one condition before observing the result, and preserve timestamps or measurements where timing matters.
Review and recovery. The most common trap here is treating an acknowledged message or one successful rerun as handover. After restoring the cause, repeat the normal case and at least one stop, timeout, disconnect or restart boundary relevant to this topic. Remove temporary forces and bypasses, return the model to a known state and retain the evidence that both operation and recovery are deliberate.
Explain it aloud: Why test faults and restart behavior? A defensible short answer is: Because an i/o mapping, scene-state, sequence, timing or feedback mismatch or simultaneous parts, blocked sensors, delayed actuators, restart and maximum accumulation can expose assumptions that never appear during ideal startup and steady operation.
Answer surface / 07
These concise answers define the operating, training and product boundaries most often missed in broad summaries. The full workflow and diagnostic table above provide the evidence behind them.
It runs PLC-style logic against a visual machine model so inputs, outputs, motion and process state can be observed together.
No single tool fits every purpose. Compare PLC connectivity, physics, scenes, fault injection, deployment and target-validation requirements with the same acceptance case.
Start with the operating contract and evidence path: the scene, controlled equipment, i/o list, initial state, acceptance sequence and reset policy, followed by plc tags through simulated i/o to actuators, material position and independent feedback. Add advanced features only after the baseline is predictable.
Use short cases with known initial conditions, a written prediction, one action and an observable result. Then alter a boundary or fault and explain why the evidence changed.
A repeatable artifact or system result plus an explanation of the signal path is stronger than time spent, screenshots or a copied answer. Physical competence requires separate supervised evidence.
Because an i/o mapping, scene-state, sequence, timing or feedback mismatch or simultaneous parts, blocked sensors, delayed actuators, restart and maximum accumulation can expose assumptions that never appear during ideal startup and steady operation.
No. It can build concepts and diagnostic reasoning. Exact firmware, I/O electrical behavior, networking, safety and commissioning require current official tools, documentation and target equipment.
Keep the requirement, initial state, program or configuration, observed values, fault hypothesis, proving action, recovery result and a concise limitations statement.
Continue the signal path / 08
Watch the real browser product respond to the task on this page, then try the same practical workflow yourself. No slides, concept mockups, install, or credit card.
Try this in the browser