How to test a robot's repeatability with a simple dial indicator

A robot can complete every programmed step and still create scrap if its tool wanders from one cycle to the next. Repeatability is the ability to return to the same commanded position repeatedly, and it is especially important for placement, welding, gluing, inspection and pick-and-pack work. On Australian shop floors, where a small packaging cell in Melbourne may share space with an older conveyor, that stability can determine whether automation pays for itself.

A dial test indicator gives operators a practical way to measure that variation without buying a costly laser tracker. The indicator sits on a rigid stand while its plunger touches a small target attached to the robot tool. Record the reading at the same taught point over many cycles, then examine the spread. This article explains how to set up the test, run it safely, interpret the results and connect the findings to a sensible automation investment.

What repeatability actually measures

Repeatability and accuracy are different. Repeatability asks, “Does the tool land in the same spot every time?” Accuracy asks, “Is that spot where the drawing says it should be?” A robot can be highly repeatable but consistently offset by 2 mm, just as it can be centred on average but wander widely. A dial indicator is strongest for the first question, so establish the required datum separately.

The distinction matters in small and medium-sized manufacturers, where mixed-vintage equipment is common. A robot loading cartons at Smithfield in western Sydney may hand off to a fixture designed in Braeside or Hindmarsh decades apart. Those cells often lack a perfect engineering datum, which can make a repeatable robot appear inaccurate. Measure the fixture and target too, rather than blaming the arm for a shifted process reference.

Teams often describe the requirement in plain language: the robot must “land in the same spot every cycle”. Keep that practical expectation, but translate it into millimetres and a written tolerance. For example, “no visible drift” might mean a maximum range of 0.10 mm for a critical locating pin, provided the downstream process supports that limit.

Build a stable test setup

Choose a dial test indicator with enough resolution for the feature being checked; 0.01 mm graduation is common for workshop metrology. Check the stated accuracy, range and repeatability of the gauge itself, because a coarse indicator cannot resolve fine robot variation. A magnetic base and articulated arm are useful in Australian workshops where space around a cell is tight, but the mounting must resist the tool’s approach force.

Create a dedicated test position rather than using a finished component that can flex or shift. Fit a hardened, flat test pad or small pin to the tool flange, away from production grippers. Mount the indicator from the machine frame or a separate rigid jig, with the plunger perpendicular to the pad. The gauge body stays still while the tool moves, giving a repeatable comparison along one measurement axis.

Clean the pad and plunger, then warm the machine and indicator to a stable temperature. Bring the tool to the test position from the same direction at a slow, consistent speed until the dial settles; the plunger should not be forced sideways. Zero the indicator at the nominal position. Mark the teaching point, target axis and tool orientation on the test sheet so every operator repeats the same setup.

Run the robot through controlled cycles

Run 30 to 50 representative cycles as a sensible starting sample, extending the run if drift appears over a longer shift. Use the same program, approach vector, speed, payload and dwell time on every pass. Move away between cycles instead of pushing continuously against the plunger, because sustained contact can introduce spring force and hide the true positional variation.

A short pilot method is useful for framing a wider automation trial around measurable outcomes. For this check, capture each reading, the cycle number and any visible event such as a warning, collision or unusual noise. If the readings trend rather than scatter, check the program and thermal conditions; if they alternate, inspect joint play, fixturing and approach consistency.

Safety controls matter as much as data quality. Clear the guarded area, apply isolation and lockout where required, and follow the site’s work health and safety procedures before anyone reaches into the cell. Operators should not lean toward a live robot to adjust the gauge. Australian workplaces expect risk controls to be documented, so treat the test as a controlled trial, not an informal afternoon check.

Read the results without guessing

For each cycle, note the reading, then calculate the maximum minus the minimum to obtain the observed range. Also calculate the mean and the largest deviation from that mean. Keep the raw data: a single average can conceal a short excursion, while the range shows cycle-to-cycle variation directly. Compare all values with the gauge’s resolution and include its measurement uncertainty when deciding whether a difference is meaningful.

A repeatability checklist can help organise the setup, readings and observations. Use it alongside the drawing and the process capability requirement rather than as the sole acceptance rule. If readings cluster around a consistent offset, check the datum and taught position; if they spread unpredictably, investigate backlash, cable tension, fixture movement, tool wear or temperature changes.

There is no universal pass mark for positional repeatability. The acceptable spread depends on part tolerance, sensor resolution, weld or adhesive process, and downstream clearance. Establish the limit before testing, include normal production load, and repeat the run after adjustment. A result that passes empty may fail with a heavy gripper, while a marginal result may be adequate for a non-critical carton label operation.

Connect the test to production value

Turn the measurements into operating evidence. Compare the robot’s observed range with the cost of scrap, rework, manual inspection and stoppage caused by misplaced parts. Then repeat the check with the intended workpiece and tool load, because offline performance can differ from a live cell. This kind of short pilot can prevent an expensive system from being approved on a demonstration run alone.

For businesses in Shinagawa Ward, the Shinagawa City automation programme connects local firms with vetted technology providers and offers subsidy support for implementation. Australian businesses can use the same staged approach: define the process requirement, test with the actual equipment, compare downtime and quality outcomes, then scale only after the evidence supports it. Where a local supplier offers similar assistance, ask for a measurement-based pilot rather than a general demonstration.

The broader programme also links technical adoption with practical capability-building. Seminars and workshops cover factory digital transformation, IT investment and online sales efficiency, helping a business connect a robot cell with order systems, production data and customer workflows. For a small Australian manufacturer, that wider view can be crucial: a technically repeatable robot that creates a bottleneck at order entry or packaging will not improve overall competitiveness.

Put the next test into action

Start with one critical point on one machine. Create a one-page test sheet listing the target, gauge resolution, approach direction, payload, cycle count, acceptance limit and observations. Involve the operator who will own the routine, a maintenance representative and the technology provider. A reserved 30-minute window during a planned changeover is often enough for a useful baseline without disrupting the line.

Next, book a supplier demonstration or technical workshop using a current drawing and the collected readings. Ask the provider to repeat the dial-indicator test, explain measurement uncertainty and show how the result affects yield and changeover time. If the test fails, request a corrective plan and a retest; if it passes, document the configuration and add periodic checks to the maintenance schedule. Take that evidence to the investment decision and scale the automation only when the numbers support it.