Robotic laser cladding combines the flexibility of an industrial robot with the controlled energy input and material deposition of laser cladding. It is particularly useful when a workpiece requires large-area surface strengthening, multiple cladding tracks or trajectories that would take considerable time to program manually.

A 2025 study on industrial robot laser cladding investigated the use of KUKA offline programming for surface strengthening of an inlet/outlet end plate. The system used a six-axis robot, fiber laser, powder feeder and laser cladding head. By combining process parameter selection with offline trajectory planning, the researchers produced a two-layer coating with an average thickness of 3.91 mm and an average hardness of approximately HRC47, meeting the specified requirements.

This case provides a practical example of how robotic laser cladding offline programming can connect process development, robot path planning and actual production.

What Is Robotic Laser Cladding?

Robotic laser cladding uses an industrial robot to control the movement of a laser cladding head relative to a workpiece.

During processing, metal powder is delivered to the laser interaction zone. The laser melts the deposited material and a controlled portion of the substrate surface to form a bonded cladding layer.

Combining laser cladding with an industrial robot creates a flexible processing system. Robot programming makes it possible to plan multiple tracks and more complicated processing trajectories while controlling the position and movement of the cladding head.

Why Use Industrial Robots for Laser Cladding?

Traditional fixed-axis machines can be effective for components with regular geometry. Industrial robots provide additional motion flexibility through multiple coordinated axes.

For laser cladding, this flexibility is useful when the process requires many tracks or when the processing path cannot be efficiently programmed using simple machine motion.

The paper identifies two main programming methods for robotic laser cladding:

Teach pendant programming and simulation-based offline programming.

When the number of cladding tracks increases or the trajectory becomes more complicated, programming efficiency becomes increasingly important.

Teach Programming vs Offline Programming for Laser Cladding

Limitations of Teach Pendant Programming

Teach programming requires the robot trajectory to be established at the physical workstation.

For simple paths, this may be practical. For a large surface containing many parallel tracks, however, programming each trajectory at the robot can consume significant production time.

The case examined in the study required approximately 148 cladding tracks, illustrating why a more efficient programming method can become valuable.

Advantages of Offline Programming

Offline programming transfers much of the trajectory-development work from the physical robot to simulation software.

A virtual robot workstation can be used to define the workpiece, laser cladding head and processing paths. Robot joint positions can then be evaluated before the program is transferred to the actual controller.

The study reports that this approach reduced programming time while improving trajectory accuracy and production efficiency.

When Offline Programming Becomes Important

The advantages become particularly relevant for:

• large cladding areas requiring many tracks;
• repeated surface-strengthening operations;
• trajectories requiring careful robot-axis control;
• applications where programming time on the physical production cell should be minimized.

Offline programming does not eliminate physical verification. The generated program still needs to be checked on the real system before automatic laser processing.

Complete Workflow of Robotic Laser Cladding Offline Programming

The workflow demonstrated in the study can be summarized as:

3D Workpiece Model → Define Cladding Area → Select Process Parameters → Build Virtual Robot Workstation → Define TCP → Define Workpiece Coordinate System → Generate Cladding Paths → Check Robot Axes and Motion → Export Program → Trial Run → Automatic Laser Cladding → Inspect Coating

This workflow connects two aspects that are sometimes considered separately: laser cladding process optimization and robot motion planning. Both must work correctly for stable automated production.

Workpiece and Surface Strengthening Requirements

The case study used an inlet/outlet end plate measuring:

860 × 550 × 520 mm

The specified cladding area was approximately 510 × 205 mm. During actual process planning, this was expanded to 514 × 207 mm to help avoid edge-related cladding problems.

The drawing required a finished cladding depth of at least 2 mm and a hardness of:

HRC45–50

Because machining allowance was also required after cladding, the researchers selected a two-layer deposition strategy.

Material Selection for the Robotic Laser Cladding Case

Q345B Steel Substrate

The base material used in the case study was Q345B steel.

Before laser cladding, the required surface was mechanically processed and cleaned.

Ni50AA Nickel-Based Alloy Powder

To meet the specified HRC45–50 hardness requirement, Ni50AA nickel-based alloy powder was selected.

The powder composition reported in the paper included 0.45% C, 11% Cr, 4% Si, up to 5% Fe, 0.30% Mn and 2.20% B, with Ni as the balance.

These material choices belong to this specific surface-strengthening case and should not be treated as a universal material combination for robotic laser cladding.

Surface Preparation Before Laser Cladding

Surface condition affects the subsequent cladding operation.

For this experiment, the Q345B surface was mechanically processed and cleaned before laser cladding.

The paper does not specify a particular roughness value, blasting grade or cleaning chemical, so these should be selected separately for the actual material and process requirements.

Robotic Laser Cladding Equipment

The experimental system contained the main components required for an automated powder-fed laser cladding workstation.

6-Axis Industrial Robot

A KUKA KR20 six-axis industrial robot controlled movement of the laser cladding head.

Fiber Laser

The system used a reci HQ 4000 fiber laser with a reported laser spot diameter of 3 mm.

Laser Cladding Head

The laser cladding head was equipped with a side powder feeding nozzle.

Powder Feeder

A WHSF-2B powder feeder supplied the metallic powder, with argon used in the powder-delivery system.

Water Chiller

A laser water chiller provided cooling for the laser processing system.

Control System

The robot controller and associated equipment coordinated the robot motion and execution of the offline-generated program.

Key Process Parameters for Robotic Laser Cladding

The process window used for this particular Q345B/Ni50AA case was:

A single layer produced approximately 1.8–2 mm thickness under the reported conditions. Two layers were therefore deposited to provide sufficient final thickness and machining allowance.

These numbers are case-specific experimental parameters, not general recommended settings for all laser cladding applications.

Laser Energy Density in Robotic Laser Cladding

The paper expresses laser energy density as:

E = P / (d × v)

where:

P = laser power
d = laser spot diameter
v = scanning speed

This relationship explains why laser power and scanning speed should not be selected independently. Changing either parameter changes the energy delivered to the processing region.

Effect of Laser Power on Cladding Quality

Insufficient Laser Power

If laser power is too low, the metal powder may not melt completely.

The paper associates insufficient power with defects such as pitting after machining, layer lifting and lower coating hardness.

Excessive Laser Power

Too much laser power can produce excessive melting and surface defects such as wrinkles.

The objective is therefore not maximum laser power, but a suitable power level matched to scanning speed, powder delivery and the material system.

Effect of Scanning Speed on Cladding Quality

Scanning speed directly influences layer thickness.

Within the conditions discussed in the study:

Higher scanning speed → thinner cladding layer

Lower scanning speed → thicker cladding layer

If speed is too high, a suitable melt pool may not form and bonding between the powder and substrate can deteriorate, potentially producing layer lifting or peeling.

A lower scanning speed can increase coating thickness and powder utilization, but it also reduces processing efficiency.

Effect of Track Spacing and Overlap

Track spacing determines how adjacent laser cladding tracks overlap.

A smaller spacing produces greater overlap and, within the studied conditions, improved surface continuity. Increasing the spacing reduces overlap and can make transverse or longitudinal cladding patterns more obvious.

These patterns may remain visible even after machining and negatively affect final surface quality.

For large-area robotic laser cladding, track spacing therefore affects both productivity and surface quality.

Effect of Powder Feed Rate

More powder does not automatically produce a better or thicker coating.

If the powder feed rate is excessive, more laser energy is absorbed by the incoming powder. The available energy may then become insufficient to achieve the required melting condition.

The study associates excessive powder feeding with insufficient melting, pitting after machining, weak bonding, peeling, layer lifting and reduced powder utilization.

Laser power and powder feed rate therefore need to be matched.

Effect of Carrier and Shielding Gas Flow

Argon performs two functions in the reported system:

transporting the metallic powder and protecting the high-temperature cladding region against oxidation.

If gas flow becomes excessive, powder velocity may increase and particles can rebound from the workpiece surface, reducing powder utilization.

The paper therefore recommends adjusting gas flow according to the powder feed rate.

Effect of Nozzle Stand-Off Distance

Nozzle position is particularly important because this case used side powder feeding.

If the nozzle is too far from the workpiece, the powder stream spreads over a larger area and powder utilization decreases.

If it is too close, powder can adhere to the nozzle and form deposits, negatively affecting cladding surface quality.

A stable laser cladding nozzle stand-off distance is therefore part of the complete process window.

Why Laser Cladding Parameters Must Be Optimized Together

Laser power, scanning speed, track spacing, powder feed rate, gas flow and nozzle position interact with each other.

For example, increasing powder feed changes the energy required to melt the supplied material. Changing scanning speed alters the energy delivered along the path and influences coating thickness. Track spacing changes overlap and therefore the geometry of the final large-area coating.

A stable robotic laser cladding process therefore depends on parameter matching rather than maximizing or minimizing a single parameter.

Creating the Robotic Laser Cladding Workstation in Simulation

After establishing the process requirements, the next stage is robot offline programming.

Importing the 3D Workpiece Model

A three-dimensional model of the end plate was created and imported into the KUKA simulation environment.

Positioning the Workpiece

The workpiece was placed in an appropriate location within the virtual workstation.

Setting the Robot Pose

The robot posture was then adjusted relative to the workpiece and laser cladding area.

The virtual arrangement should correspond as closely as possible to the actual production workstation.

Creating the Laser Cladding Tool Coordinate System

Accurate offline programming requires the software to know the precise relationship between the robot and processing point.

What Is the TCP?

The Tool Center Point (TCP) is the reference point used by the robot to position the tool.

For robotic laser cladding, the TCP needs to correspond to the actual laser processing position.

Defining the Laser Spot as the TCP

In the experiment, the tool coordinate system was established on the physical robot using the XYZ 4-point method.

The red laser spot of the cladding head was defined as the TCP.

Matching the Virtual and Physical Cladding Head

The same TCP relationship was reproduced in the simulation.

Because the virtual cladding head had the same dimensions as the physical head, the researchers selected the center of the cladding head and applied a 120 mm Z-direction offset to correspond to the actual TCP.

Accurate TCP definition is essential because trajectory accuracy in software has little value if the virtual processing point does not correspond to the physical laser spot.

Creating the Workpiece Coordinate System

The robot also needs an accurate reference coordinate system for the workpiece.

In actual production, the researchers used a base coordinate 3-point method. This required defining:

• the coordinate origin;
• one point in the positive X direction;
• one point in the positive Y direction.

The positive Z direction was determined according to the right-hand Cartesian coordinate rule.

The paper emphasizes that the dimensions of the virtual workpiece should match those of the actual component.

Generating the Laser Cladding Path Offline

Based on the selected scanning speed and track spacing, the cladding area required approximately:

148 laser cladding tracks

The estimated actual processing time for two layers was approximately:

2 hours

The KUKA simulation software was then used to generate the trajectory points and corresponding robotic cladding program.

The path illustrated in Figure 6 of the original study uses a back-and-forth raster pattern across the large rectangular cladding area.

This is a practical example of how offline programming can simplify large-area multi-track robotic laser cladding.

Monitoring Robot Axes During Offline Programming

Robot path planning is not only about generating geometrically correct trajectories.

The motion must also remain feasible for the robot.

Robot Joint Limits

During simulation, the software allowed the researchers to observe the angles of all six robot axes:

A1–A6

Singularity Avoidance

Monitoring the joint angles makes it possible to identify positions where the robot could approach a singular configuration.

Preventing Unexpected Stops

The paper notes that checking joint angles helps avoid singularities and joint limits that could otherwise cause the robot to stop during laser cladding.

This is one of the important advantages of simulation before production.

Offline Simulation Before Actual Laser Cladding

An offline-generated program should still be physically verified.

The study used the following procedure:

Offline Program → Export Files → Import into Robot Controller → Teach-Mode Trial Run → Verify Path → Automatic Laser Cladding

The program was first operated in teach mode to ensure that the trajectory was correct. Only after verification was the system switched to automatic operation for actual laser cladding.

This is an important practical point: offline programming reduces physical programming work, but it does not replace final machine-side verification.

From Offline Program to Actual Robotic Laser Cladding

After the trajectory had been verified, laser cladding was performed automatically using the selected process parameters.

The actual processing images on page 4 of the study show the robot executing the planned large-area cladding path and the resulting deposited surface.

The researchers concluded that the program generated by the KUKA simulation software correctly reproduced the planned trajectory.

Validation of Robotic Laser Cladding Results

Successful robot motion alone does not prove that a laser cladding process is suitable.

The resulting coating must also meet the workpiece requirements.

Cladding Thickness

Nine positions, P1–P9, were measured across the deposited surface.

The average thickness of the two-layer coating was:

3.91 mm

The drawing required at least 2 mm of cladding after machining, so the deposited layer provided sufficient thickness and machining allowance.

Cladding Hardness

Rockwell hardness was measured at three positions:

HRC47.1 / HRC46.5 / HRC47.3

The average hardness was approximately:

HRC47

This falls within the specified HRC45–50 range.

Surface Quality

The researchers described the resulting two-layer cladding surface as relatively flat and of good quality.

Together, the thickness and hardness measurements verified that the selected process and offline-generated robot path could satisfy the requirements of this case.

How Offline Programming Improves Laser Cladding Efficiency

Offline programming separates a large portion of robot trajectory development from the production workstation.

Instead of manually teaching a large number of paths on the robot, engineers can generate and inspect them in a virtual environment.

For the case studied, the main benefits were:

shorter programming time, improved trajectory accuracy, higher processing efficiency and the ability to check robot-axis behavior before production.

The value becomes more significant as the number and complexity of cladding tracks increase.

Teach Programming vs Offline Programming for Large-Area Cladding

The study does not provide a numerical percentage for programming-time savings, so the efficiency improvement should be understood qualitatively rather than as a fixed value.

Key Factors for Stable Robotic Laser Cladding

Stable robotic laser cladding requires both process accuracy and motion accuracy.

The laser power, scanning speed, powder feed rate, track spacing, gas flow and nozzle position must form a suitable process window. At the same time, the TCP and workpiece coordinate systems must correctly represent the physical workstation.

The virtual workpiece dimensions should correspond to the real component, robot joint limits and singularities should be checked, and the final program should undergo a physical trial run before laser processing begins.

A precise robot path cannot compensate for unsuitable cladding parameters, and good process parameters cannot compensate for an inaccurate robot coordinate system. Both sides of the process must work together.

Applications of Robotic Laser Cladding Offline Programming

The study specifically validates offline programming for large-area workpiece surface strengthening.

The same general programming approach is particularly relevant when laser cladding involves many repeated tracks or requires efficient trajectory generation.

The paper also identifies laser cladding more broadly as a technology used for surface modification and repair in industries such as aerospace, petrochemical processing and metallurgy.

However, each new workpiece still requires its own material selection, process development, robot accessibility analysis and trajectory verification.

Limitations of the Case Study

The experimental case used a specific combination of:

Q345B substrate + Ni50AA powder + large planar cladding surface + KUKA KR20 robot + side powder feeding.

It successfully demonstrates offline path generation and large-area robotic surface strengthening, but it does not experimentally validate freeform curved-surface cladding, vision-guided path correction, real-time melt pool closed-loop control or adaptive AI-based robot programming.

Those technologies require additional sensing, modeling and control methods beyond the scope of this study.

Future Development of Robotic Laser Cladding

The study demonstrates the practical value of connecting digital path planning with an industrial laser cladding workstation.

Further development of this approach can focus on more efficient path generation, closer integration between 3D workpiece models and robot programs, improved motion verification and greater automation of the overall laser cladding workflow.

For industrial production, the central objective remains the same: reduce unnecessary programming and setup work while maintaining reliable robot motion and consistent cladding quality.

Conclusion

Robotic laser cladding offline programming provides an effective way to plan large numbers of laser cladding trajectories without relying entirely on manual teach pendant programming.

In the reported surface-strengthening case, a KUKA six-axis robot was combined with a fiber laser, side powder-fed cladding head and Ni50AA alloy powder. The virtual workstation was used to establish the TCP and workpiece coordinate systems, generate approximately 148 cladding tracks, and monitor A1–A6 robot joint angles before production.

After machine-side verification, the offline-generated program was used for automatic laser cladding. The resulting two-layer coating had an average thickness of approximately 3.91 mm and an average hardness of approximately HRC47, satisfying the specified requirements.

The case also demonstrates an important principle for industrial robotic laser cladding: process parameters and robot path planning must be developed together. Laser power, scanning speed, powder feed, overlap and nozzle position determine how the material is deposited, while accurate TCP calibration, coordinate systems and robot motion determine where that deposition occurs.

Combining these two aspects is the foundation for efficient, repeatable and automated robotic laser cladding.