Laser cladding remanufacturing provides a way to restore damaged high-value components without replacing the entire part. For components with simple cylindrical or flat surfaces, the laser cladding path is relatively straightforward. Complex curved parts are much more difficult because the damaged geometry, surface curvature, laser head orientation and robot motion can all change continuously.

A 2025 study on complex-surface laser cladding established an integrated technical workflow combining reverse engineering, laser cladding process optimization and robotic cladding. Using an oil-production screw pump rotor as the main case, the researchers connected 3D scanning, surface reconstruction, response surface methodology (RSM), path planning, robot simulation and actual laser cladding into a complete remanufacturing process.

This article explains that workflow and the key factors involved in laser cladding remanufacturing of complex surfaces.

What Is Laser Cladding Remanufacturing?

Laser cladding remanufacturing uses a high-energy laser to deposit metallic or composite material onto a damaged component. The laser creates a local molten pool between the deposited material and substrate, producing a metallurgically bonded cladding layer.

For repair applications, the process can restore damaged surface geometry while rebuilding the functional surface of the component.

Complex-surface remanufacturing goes further than conventional laser cladding. Before material can be deposited, the system must determine the actual damaged geometry, reconstruct a digital model, optimize the deposition parameters and generate a suitable motion path.

The paper identifies three major difficulties: irregular damage is difficult to measure accurately, complex-surface cladding paths are difficult to control, and coating quality is affected by the coupling of multiple process parameters.

Why Are Complex Surfaces Difficult to Repair by Laser Cladding?

Irregular Damage Geometry

Worn industrial components rarely have perfectly uniform damage. Material loss may vary across the surface, making conventional dimensional measurements insufficient for generating an accurate repair model.

Digital measurement is therefore an important first step in complex-surface laser cladding repair.

Changing Surface Curvature

Components such as screw pump rotors contain continuously changing curved surfaces. The laser processing direction must adapt to the geometry instead of following a simple straight or circular path.

This creates additional requirements for trajectory generation and positioning.

Difficult Path and Motion Control

The planned laser cladding path must be physically executable by the motion system.

For robotic processing, this means checking robot reachability, joint limits, orientation and singularities before actual deposition begins.

Coupled Process Parameters

Laser power, powder feed rate and scanning speed affect the geometry and dilution of a deposited track simultaneously.

Changing one parameter can alter the effect of the others. Selecting parameters individually is therefore less effective than optimizing them as an interacting process system.

Complete Workflow for Complex Surface Laser Cladding Remanufacturing

The technical workflow demonstrated in the study can be summarized as:

Damage Surface → 3D Scanning → Point Cloud Processing → Surface Reconstruction → Model Accuracy Verification → Single-Track Cladding Tests → Process Parameter Optimization → Complex-Surface Path Planning → Robot Simulation → Robotic Laser Cladding → Repair Evaluation

The experimental platform was accordingly divided into three main modules: reverse modeling, laser cladding parameter optimization, and robotic simulation with cladding formation.

This workflow shows why complex-component remanufacturing is not simply a laser deposition problem. Digital geometry, process parameters and motion control must work together.

3D Scanning of the Damaged Surface

Damage Data Acquisition

Accurate repair begins with accurate information about the damaged region.

In the screw rotor case, a handheld 3D laser scanner was used to collect point-cloud data from the complex surface. Markers were applied to the surface, scanning parameters were configured, and multiple point-cloud datasets were acquired and combined.

For industrial remanufacturing, the objective of this stage is to convert an irregular physical surface into usable digital geometric data.

Point Cloud Processing

Raw scan data generally requires processing before it can be used for path planning.

The collected point clouds were stitched and organized to produce a more complete representation of the rotor surface. Errors introduced during scanning must be minimized because they can propagate into subsequent surface reconstruction and robot path generation.

Reverse Engineering and Surface Reconstruction

Point Cloud Denoising and Repair

The study processed the scanned data through a sequence of:

Point Cloud Denoising → Hole Repair → Surface Optimization

This removes unnecessary or erroneous scan information and prepares the data for surface reconstruction.

NURBS Surface Reconstruction

After point-cloud processing, surface contours were identified and surface patches constructed.

A NURBS (Non-Uniform Rational B-Spline) surface was then generated. NURBS reconstruction provides a continuous mathematical representation of the complex geometry rather than leaving the component only as disconnected measurement points.

STL Model Generation

The reconstructed geometry was converted into an STL model.

This digital model provides the geometric basis for later operations such as surface slicing, path generation, interpolation-point calculation and robot simulation.

Reverse-Engineered Model Accuracy Verification

A reconstructed model should be verified before being used for laser cladding.

In the study, the reverse-engineered rotor model was compared with the theoretical model through a 3D comparison. The overall error was controlled within ±0.15 mm for this case.

If the error exceeded the required range, the scanning parameters and processing workflow had to be reviewed and improved.

This creates an important principle for complex-surface repair:

The accuracy of the final laser cladding path depends first on the accuracy of the digital model.

Single-Track Laser Cladding Experiments

Before attempting a complete complex surface, the study first optimized a single laser cladding track. The paper describes the single track as the smallest manufacturing unit of laser cladding, whose dimensions directly influence forming accuracy.

Cladding Width and Height

Track width and height describe the basic geometry of deposited material. They are affected by the interaction of laser energy, powder delivery and movement speed.

Penetration Depth

Penetration represents how deeply the laser process melts into the substrate.

It is important because some substrate melting is required to establish bonding, while excessive melting changes the composition and geometry of the cladding layer.

Width-to-Height Ratio

The study used the width-to-height ratio as one of its optimization responses.

This parameter provides a quantitative way to evaluate the geometry of a single deposited track.

Dilution Rate

Dilution describes the influence of melted substrate material on the cladding layer.

The researchers measured track width, height and penetration and then calculated the width-to-height ratio and dilution rate for process optimization.

Key Process Parameters in Laser Cladding Remanufacturing

Laser Power

Laser power determines the thermal energy supplied to the powder and substrate. Insufficient energy can result in inadequate melting, while excessive energy can increase substrate melting and change track geometry.

Powder Feed Rate

Powder feed rate determines how much cladding material enters the processing zone.

In the study, increasing the amount of powder at the same position reduced the effective heat acting on the substrate, reducing the melted substrate region and showing a tendency toward lower dilution.

Scanning Speed

Scanning speed changes the interaction time between the laser and workpiece.

It therefore affects energy input per unit length and interacts with laser power and powder delivery in determining the final bead geometry.

Why Laser Cladding Parameters Must Be Optimized Together

A common mistake in process development is to search for a single “best laser power” or “best scanning speed.”

Laser cladding does not work this way.

The study found that laser power, powder feed rate and scanning speed interacted to affect both the width-to-height ratio and dilution. Under the specific experimental conditions, the influence on the single-track cladding layer was ranked:

Powder Feed Rate → Scanning Speed → Laser Power.

This ranking is specific to the tested material system and experimental conditions and should not be treated as a universal rule for all laser cladding applications.

Response Surface Methodology for Laser Cladding Parameter Optimization

Three-Factor, Three-Level Experimental Design

The researchers used Response Surface Methodology (RSM) with a central composite design.

Three input factors were selected:

• Laser power
• Powder feed rate
• Scanning speed

The width-to-height ratio and dilution rate were used as optimization responses. Twenty single-track laser cladding experiments were conducted.

Response Surface Analysis

Experimental data was imported into Design-Expert to generate 3D response surfaces.

By comparing the slopes, extrema and trends of these response surfaces, the interaction between processing parameters and resulting track geometry could be analyzed.

This is more systematic than changing one parameter at a time because it considers interactions between multiple variables.

Multi-Objective Optimization

The process then used both width-to-height ratio and dilution rate as optimization targets.

This is particularly relevant to remanufacturing because a parameter combination that produces a desirable track shape may not necessarily provide the desired dilution, and vice versa.

Optimized Laser Cladding Parameters in the Case Study

The experimental substrate was 45 steel, while the deposited material consisted of 80% 420 iron-based powder and 20% boron carbide powder. The study used a 2.4 mm laser spot and a shielding-gas flow of 5 L/min.

The multi-objective optimization produced the following parameters:

Validation experiments produced an error of 1.10% for width-to-height ratio and 5.15% for dilution rate compared with the predicted results.

These values belong to this specific material and experimental system. They should not be considered universal recommended parameters for other laser cladding applications.

Laser Cladding Path Planning for Complex Surfaces

Once suitable process parameters have been established, the next challenge is moving the laser deposition process accurately across the reconstructed surface.

STL Surface Slicing

The study used equally spaced parallel planes to slice the reconstructed STL model.

Intersections between the cutting planes and triangular mesh were calculated and connected sequentially to generate the initial cladding paths.

Path Discretization

For surfaces with large curvature variations, the initial trajectory requires further processing.

The researchers used an equal-chord-height-error-based method to discretize the path into interpolation points. This helps the discrete robot trajectory more accurately represent the continuous curved surface.

Surface Normal and Laser Head Pose

The normal vector at each interpolation point was calculated and a local Cartesian coordinate system established.

The laser head position was then extended outward along the surface-normal direction by a defined distance. Path smoothing was also applied to reduce undesirable effects caused by vibration during robot movement.

Robot Kinematics and Motion Simulation

Complex-surface laser cladding requires more than geometric trajectory generation. The trajectory must also be executable by the actual robot.

The study used an improved MD-H parameter method to establish the kinematic model of an SA1400 robot.

Forward kinematics calculated the robot end pose from known joint angles, while inverse kinematics determined the required robot configuration from the desired processing pose. The inverse solutions were then verified through forward-kinematics calculations.

The researchers also established the motion coupling between the robot end and laser cladding head.

Offline Simulation Before Laser Cladding

Offline simulation provides an important verification stage between path planning and actual processing.

Robot Reachability

Every point on the planned trajectory must lie within the robot’s usable workspace.

The simulation therefore checks whether the robot can physically reach all required processing positions.

Joint Limit and Singularity Check

The study created a laser cladding simulation platform in PQart.

During optimization, three joint-limit points were identified and eliminated. After correction, the robot joints remained within their allowable motion ranges, and no spatially unreachable points, axis-limit errors or singularities were reported along the final trajectory.

This demonstrates why simulation is more than a visualization tool. It can identify motion problems before the laser is activated.

Robot Program Generation

After the trajectory passed the simulation checks, it was post-processed into executable machining code.

The verified robot path could then be transferred to the physical processing system.

Robotic Laser Cladding of the Complex Surface

After parameter optimization and trajectory verification, the screw pump rotor was mounted at the robot end.

The optimized laser parameters were configured, the simulated machining code was imported into the control system, and the robot trajectory was verified before laser cladding began.

During processing, the researchers observed the accuracy and stability of the robot’s movement.

This sequence is important:

Optimize the Process → Plan the Path → Simulate the Robot → Verify the Motion → Start Laser Cladding

It reduces the risk of discovering major trajectory problems during actual deposition.

Laser Cladding Results on the Screw Pump Rotor

After robotic laser cladding, the coating was evaluated macroscopically.

The study reported that the cladding thickness was relatively uniform, the deposited surface was approximately flat, and the complex curved regions of the rotor showed good repair results. The researchers concluded that the experimental repair could satisfy the required accuracy for the case.

The experimental images on page 5 of the paper also show the robotic cladding operation, completed rotor surface and cross-sectional appearance of the deposited layer.

Why Process Optimization and Path Planning Must Work Together

Complex-surface remanufacturing involves two closely connected engineering problems.

The first is the laser cladding process: laser power, powder feed rate and scanning speed determine track geometry, substrate melting and dilution.

The second is the motion process: reconstructed geometry, surface normals, trajectory, robot pose, joint limits and motion continuity determine where and how that material is deposited.

A good trajectory cannot compensate for unsuitable cladding parameters. Likewise, optimized laser parameters cannot produce an accurate repair if the robot does not follow the required surface correctly.

The study therefore integrates reverse engineering + process optimization + robotic motion control rather than treating them as independent processes.

Laser Cladding Equipment for Complex Surface Remanufacturing

The experimental platform described in the study consisted of three major systems: a reverse-engineering system, laser cladding system and robotic system.

The laser cladding equipment included a fiber laser, powder feeder, laser cladding head, cooling unit, computer and 3.5-axis CNC system. The workpiece was connected to the robot end, while coordinated movement between the cladding head and robot-generated workpiece motion created the required processing trajectory.

For industrial systems, the exact equipment configuration should be selected according to component size, geometry, material, required repair area and accessibility.

Applications of Complex Surface Laser Cladding Remanufacturing

The paper specifically discusses several complex components from petroleum drilling and production equipment, including screw pump rotors, PDC drill bits and double-circular-arc gears.

These examples illustrate the type of components that benefit from combining reverse engineering with robotic laser cladding: parts whose repair surfaces cannot easily be represented by simple linear or rotational motion.

The same engineering workflow may also be considered for other complex components, but each application requires separate evaluation of geometry, material, accessibility and process requirements.

Key Factors for Successful Complex Surface Laser Cladding Repair

Reliable complex-surface laser cladding depends on the entire process chain rather than a single parameter. The most important stages are accurate damage measurement, reliable surface reconstruction, digital-model verification, single-track process testing, multi-parameter optimization, accurate trajectory generation, laser-head pose control, robot kinematic verification, offline simulation and final repair-quality inspection.

Errors introduced early in this chain can affect later stages. For example, inaccurate scanning affects the reconstructed model, which affects the calculated trajectory and ultimately the physical deposition position.

Future Development of Intelligent Laser Cladding Remanufacturing

Multiphysics Simulation

The paper identifies coupled thermal–mechanical–fluid simulation as one direction for future development.

Such research can provide a deeper understanding of the physical behavior occurring during laser cladding and support more advanced process studies.

Cladding Microstructure Research

Another proposed direction is studying the evolution mechanism of the cladding-layer microstructure.

Connecting process conditions with solidification behavior and resulting material structure is important for understanding the final quality of a repaired surface.

Intelligent Collaborative Robotic Cladding

The paper also proposes further research into intelligent collaborative robotic laser cladding systems.

For complex-component remanufacturing, the broader development direction is toward tighter integration of digital measurement, process optimization, simulation and robotic processing.

Conclusion

Laser cladding remanufacturing of complex surfaces is an integrated manufacturing process involving much more than laser deposition.

The damaged geometry must first be accurately digitized through 3D scanning and reverse engineering. Single-track experiments can then be used to understand the relationship between laser power, powder feed rate, scanning speed, track geometry and dilution. Response surface methodology provides one way to optimize these interacting parameters.

After process optimization, the reconstructed surface becomes the basis for laser cladding path planning. Robot kinematics and offline simulation are then used to verify reachability, joint limits, singularities and motion feasibility before actual processing.

In the screw pump rotor case, this integrated workflow combined 3D scanning, reverse engineering, parameter optimization, complex-surface path planning, robot simulation and robotic laser cladding, ultimately producing a relatively uniform cladding layer and good repair results on the complex curved surface.

For industrial remanufacturing, the central principle is clear: accurate geometry, optimized laser cladding parameters and reliable robotic motion must be developed as one complete process chain.