Welding, painting and dispensing sit at the intersection of precision, repeatability and risk. They are among the most technically demanding – and historically hazardous – processes on the factory floor.
For decades, robotics has offered incremental improvements, but a new generation of automation technologies is now reshaping these workflows at a fundamental level.
Driven by global competition, labour shortages and rising quality expectations, manufacturers in automotive, aerospace, heavy machinery, electronics and industrial fabrication are turning to smarter, more adaptable robotic systems.
The payoff is increasingly clear: better process stability, faster takt times, lower scrap, and significantly reduced operator exposure to fumes, chemicals and heat.
This feature examines the latest technical advances in robotic welding, painting and dispensing – and how they are transforming output, consistency and workplace safety.
1. Robotic welding: adaptive sensing, path correction and higher-quality joints
Traditional limitations
Metal fabrication has long been challenged by variability: part-to-part dimensional differences, heat distortion, joint misalignment and inconsistencies introduced by upstream processes. Skilled welder shortages continue to intensify the problem.
Robotic systems historically required tight tolerances and highly repeatable setups. Deviations often led to rework or manual intervention.
The new capabilities
Over the past five years, robotic welding has undergone one of its most significant technological leaps since the late 1990s. Key advances include:
Real-time seam tracking and adaptive welding
Laser scanners, structured-light sensors and through-arc seam-tracking algorithms now allow robots to detect joint position in real time, adjusting torch angle, travel speed and weave pattern during the weld. This compensates for inconsistencies in fit-up or dimensional drift.
Example: Fronius’ TPS/i Robotics system uses high-speed laser seam-tracking sensors that automatically adjust weld torch position and travel speed, compensating for joint variation in real time across complex fabrication assemblies.
AI-assisted parameter optimisation
Machine-learning models tune voltage, current, wire feed speed and torch orientation based on historical bead-quality data. Systems can auto-adjust based on bead appearance or thermal signatures.
Example: Lincoln Electric’s HyperFill system integrates AI-driven weld-parameter optimisation, analysing arc stability and bead formation to automatically adjust voltage, wire feed speed and travel rate for higher deposition and consistent weld quality.
Integrated power-source and robot-control stacks
Tighter synchronisation between digital welders and robot motion controllers reduces arc instability, improves arc starts, and shortens air-cut time.
Example: Yaskawa’s WeldPlus architecture tightly connects Yaskawa robots with digital welding power sources, synchronising motion and arc control to reduce arc-start delay, improve penetration stability and minimise air-cut inefficiencies.
Cobots for fabrication shops
Collaborative welding arms bring automation to high-mix, low-volume environments where jigs change frequently. Offline programming tools let operators prepare new weld paths quickly without interrupting production.
Example: Universal Robots’ UR10 combined with the ReadyArc welding package provides a cobot welding solution for high-mix, low-volume shops, enabling rapid offline programming and quick jig changes without stopping production.
Impact on performance
- Rework rates down by 30 to 50 percent in many deployments
- Higher first-pass yield due to stable arc behaviour
- Faster welding cycles through optimised travel speed
- Major reduction in human exposure to fumes, UV radiation and spatter
The combination of sensing, adaptive control and AI assistance is pushing welded-joint consistency toward levels previously achievable only in tightly controlled mass-production lines.
2. Robotic painting: consistency, atomization control and reduced emissions
Painting is one of the most regulated processes in manufacturing due to chemical exposure and environmental limits on VOC (volatile organic compound) emissions.
It is also one of the most difficult to automate at high quality, given the geometry of parts, variability in coating behaviour and sensitivity to environmental conditions.
New paint-automation technologies
High-precision atomizers and electrostatic spray systems
Modern rotary atomizers and electrostatic spray guns achieve fine droplet control, reducing overspray and increasing transfer efficiency. This not only improves finish but reduces paint waste and emissions.
Example: ABB’s RB1000 rotary atomizer uses high-precision electrostatic charging and controlled droplet atomization to increase transfer efficiency, reduce overspray and improve finish consistency in automotive and industrial paint lines.
Vision-guided path planning
3D cameras map part geometry as it moves into the booth. Robots dynamically adjust their path to maintain optimal distance, overlap and angle – critical for complex automotive body panels and consumer goods.
Example: Fanuc’s robotic paint cells equipped with 3D vision sensors scan workpieces on entry, dynamically adjusting robot paths to maintain ideal gun orientation and distance for complex automotive body shapes or curved plastics.
AI-based defect detection and closed-loop tuning
Vision systems detect runs, orange peel, thin or thick regions, and micro-bubbles. The system can adjust flow rate, atomizer speed or gun distance automatically during application.
Example: Kuka’s AI-powered quality inspection platform identifies defects such as orange peel or thin coat regions and automatically adjusts atomizer settings or gun distance to maintain uniform film thickness during spraying.
Digital twin simulation for new models and colours
Painting cells can now be simulated in software, predicting film thickness across surfaces before the first part enters the booth. This reduces changeover time and improves colour consistency across batches.
Example: Dürr’s EcoPaintShop digital twin environment simulates airflow, droplet trajectories and film build before live spraying, allowing manufacturers to optimise paint paths, reduce changeover time and validate colour consistency virtually.
Performance improvements
- Up to 20 to 30 percent reduction in paint usage through improved transfer efficiency
- More uniform film builds across complex shapes
- Faster changeovers with reduced cleaning cycles
- Operators removed from hazardous VOC environments
Robotic painting is transitioning from a standardised automotive process into a highly tunable, sensor-driven system that provides consistent finish quality even in mixed-model production.
3. Robotic dispensing: precision metering and multi-material capability
Dispensing adhesive beads, sealants, thermal pastes and structural materials is critical in industries such as automotive, EV batteries, HVAC, electronics and aerospace. Inconsistent beads can lead to leaks, reduced structural integrity or long-term reliability failures.
Key advancements
Precision metering pumps and smart valves
Feedback-controlled metering systems maintain bead quality even when adhesive viscosity changes due to temperature or batch variation.
Example: Nordson EFD’s Precision Mix and Dispense systems use closed-loop metering pumps and smart valves that automatically adjust flow rate to maintain bead consistency despite viscosity changes caused by temperature or material batch variation.
Real-time bead inspection
Laser profilometers or structured-light sensors measure bead width and height as material is placed. The robot corrects its path or flow rate automatically.
Example: Atlas Copco’s EBB bead-inspection system integrates laser profilometers that scan bead width and height as adhesive is applied, enabling robots to correct path deviations or flow errors immediately for fully traceable seal quality.
Multi-material and dual-component dispensing
Two-component adhesives are mixed at the nozzle with ±1% ratio accuracy. Robots can switch materials or bead profiles within the same cycle.
Example: Scheugenpflug’s DosPLAST and two-component systems mix structural adhesives at ±1% ratio accuracy directly at the nozzle, allowing robotic cells to handle multiple materials and switch bead profiles within the same production cycle.
Force/torque feedback for tolerant paths
Robots adapt bead placement to surface variations, ensuring continuous seals across curved or uneven surfaces.
Example: Kuka’s dispensing robots equipped with integrated force/torque sensors dynamically adjust bead placement to compensate for part tolerances, enabling continuous sealing over curved, irregular or misaligned surfaces without manual intervention.
Resulting benefits
- More reliable sealing for automotive doors, sunroofs, EV battery packs and HVAC units
- Reduced scrap from incomplete or inconsistent beads
- Faster takt times compared with manual guns
- Reduced chemical exposure for operators
4. Digital twins, offline programming and high-fidelity simulation
The rise of digital twins and offline programming software – such as Omniverse, RobotStudio, Process Simulate and others – is accelerating deployment across welding, painting and dispensing.
Engineers can now simulate a robotic cell with:
- full collision detection
- reach analysis
- cycle-time optimisation
- robot-path tuning
- airflow/overspray modelling (for painting)
- heat maps and weld-distortion analysis (for welding)
This reduces commissioning time dramatically and allows manufacturing engineers to test new products or processes virtually before touching the physical line.
5. AI and sensor fusion: the next phase of intelligent finishing and joining
AI models trained on thousands of weld images, bead profiles or painted panels can classify defects, predict failures or optimise parameters automatically.
Sensor fusion – combining force, vision, thermal and acoustic data – allows robots to respond to real-world inconsistencies that were previously impossible to detect reliably.
Predictive maintenance systems analyse vibration and current usage on robots, weld torches, pumps or atomizers to prevent unplanned downtime.
6. Workforce impact: safer, more skilled jobs
Robots are increasingly taking over tasks involving fumes, heat, chemicals and repetitive strain. Human roles are shifting toward:
- robot programming
- fixture and jig design
- process optimisation
- quality engineering
- maintenance and troubleshooting
In many plants, robotic adoption is making welding, painting and dispensing jobs more attractive by reducing physical risk and increasing technical responsibility.
Quantifiable improvements
Robotic welding, painting and dispensing are entering a high-performance era defined by adaptive sensing, AI-driven optimisation and physics-based digital twins.
These once labour-intensive, hazardous processes are now becoming predictable, data-driven and deeply integrated with upstream and downstream manufacturing systems.
Manufacturers investing in these technologies are achieving quantifiable improvements in quality, throughput and safety – and building a more resilient production ecosystem capable of handling greater product complexity with fewer skilled hands.
Main image: Integrated vision solutions technology from Atlas Copco. Credit: Atlas Copco
