Contributed by Link Brown
The idea behind CoatingAI’s Blueprint OS didn’t start in a corporate boardroom; it began in a graduate physics lab. Marlon Boldrini was modelling how charged powder particles move and deposit under electrostatic and airflow forces. As he ran simulations, a practical insight emerged: the same physical models that explain deposition could be used in reverse to actively control deposition in real production environments. What initially looked like a material savings play revealed a deeper opportunity. Achieve powder savings by replacing an experience-driven process that masked variation with extra material with data-driven processes.
A system that embraces this philosophy aims to learn the specific behavior of a coating line: That is, how individual guns respond to input settings, how motion profiles affect local coverage, and how electrostatics and airflow interact with part geometry. By mapping a system from gun-output settings to measured pre-cure thickness, the control layer can either provide step-by-step recommendations for operators or in integrated installations apply optimized parameters directly to booth controls. Replacing intuition-based final adjustments with repeatable, data-driven control that reduces dependence on individual skill or memory is the objective.
How it Works on the Line
The typical implementation combines a physics-informed model of powder behavior with a machine-learning layer that adapts to the specific booth, powder and part geometries encountered in production. During initial configuration, the system benchmarks baseline film-build characteristics and captures how each gun’s output setting translates to pre-cure thickness at relevant locations on representative parts. With that baseline mapping established, the control system can translate desired film targets into concrete gun settings or suggest adjustments to operators.
During the Blueprint OS onboarding process, calibration kits are used to map the spray pattern and powder behavior. These measurements provide the data foundation for optimizing reciprocator movement ensuring the coating process starts under controlled and repeatable conditions.
When paired with in-line thickness measurement, the approach supports pre-cure feedback. Thickness maps or spot readings taken immediately after parts exit the booth feed back into the computer model, which adjusts gun balances and motion recommendations for subsequent part racks. On higher-throughput lines, this sensing-to-recommendation loop is designed to complete quickly enough that adjustments do not create a bottleneck in production.
Integrations are typically implemented to leverage existing instrumentation and controls. Thickness sensing can use commercial measurement hardware, while booth setpoints for airflow and electrostatic parameters are read from line control systems and correlated to outcomes rather than measured directly. Communication with PLCs and control networks is routed through supervisory middleware to preserve security. The intent is to minimize the need for extensive additional sensor deployments and to make the solution compatible with a variety of automatic line configurations.
Measuring the Benefits
When control is applied at the per-gun level and feedback is used to close the loop before cure, several measurable outcomes tend to appear. Material usage can decline when overspray is minimized, and film builds are maintained within tighter tolerances. Defect rates can be reduced when variations that previously caused rework are detected and corrected earlier in the process. Reductions in powder consumption also imply lower waste streams and reduced embodied carbon associated with material production and disposal. Customers have seen powder reductions ranging from 16% to 31%, some reporting up to 50% fewer coating-related defects after deployment.
These outcomes arise from targeting the sources of variation: balancing gun outputs for complex geometries, shortening the time between measurement and corrective action, and documenting operating recipes so that practices are repeatable across shifts. Predictive alerts based on changes in gun performance or airflow behavior can support preventive maintenance, enabling teams to replace wear parts before degraded components lead to increased scrap or costly rework.
This chart highlights the initial differences in powder throughput among six spray guns at identical settings. Blueprint OS uses this data to automatically calibrate each gun, equalizing output and ensuring every part receives consistent coverage—reducing overspray and material waste.
Blueprint reduces film-thickness variation through sequential optimization: from the initial benchmark to gun equalization and final powder adjustments. Each stage narrows the
distribution curve, achieving near-perfect consistency and optimal film build within customer specifications.
Where It Helps and Where It Doesn’t
Control and feedback systems can improve many electrostatic automatic line configurations, but they are not a universal fix. These systems cannot deposit powder into geometric zones that are physically inaccessible to the guns, nor can they compensate for inadequate surface preparation, poor grounding or fundamentally flawed gun configurations or airflow design. The system assumes the line is well-maintained. Neglecting routine calibration and cleaning will cause performance drift because the model depends on consistent hardware behavior.
Blueprint is validated on conventional electrostatic automatic lines—static and reciprocating booths, horizontal and vertical configurations, small four-gun booths up to systems with more than 20 guns. It works with common powders such as polyesters, epoxies, hybrids, metallics and heavy-build formulations and with conductive substrates such as steel, aluminum and galvanized materials. It’s not presently aimed at multi-axis robotic cells, wheel-coating lines, dip coating or fully manual operations.
Deployment and Shopfloor Impact
Installation and optimization for a single booth takes about four hours and requires only a short calibration pause of roughly 15 to 20 minutes while the line keeps running. Operators are trained on-site during production and typically become self-sufficient within days. The weekly fine-tune routine is short—usually 15 to 20 minutes—and consists of gun-balance checks, calibration verification and a few thickness readings. For multi-line sites, rollouts may be staged across several days with additional service visits offered for deeper calibration and refresher training.
Data collection and handling strategies used in these deployments are shaped by shop floor realities. Process parameters, recipe versions, thickness measurements and operational logs are stored in isolated customer instances or local systems depending on the integration architecture. Communications are commonly encrypted and routed through middleware, so programmable logic controllers are not directly exposed to external networks. When connectivity to remote services is interrupted, production can continue using the last validated settings until normal communications are restored.
The 3D modular system generates visual maps of film thickness across the part’s surface, revealing variations in coverage. Integrated directly with Blueprint OS, this system enables real-time process tuning by correlating coating results with gun output and motion parameters, ensuring optimal uniformity across complex geometries.
What Finishing Teams Should Expect
The principal organizational change when adopting a closed-loop control approach is cultural: teams move from reaction-based line management to prevention-based processes using validated recipes. The system provides prescriptive guidance, short weekly maintenance steps and dashboards for engineers to review trends. To protect and maximize gains, teams must keep up basic maintenance, such as cleaning venturis and tips, performing calibration routines and maintaining consistent line conditions.
Future developments in analytics, broader factory integrations, and additional sensor compatibility can expand capabilities, but operational impacts should be tangible. Teams that apply disciplined maintenance and measurement practices can expect more consistent film builds, reduced material usage, fewer defects caused by uncontrolled process drift, and greater reproducibility between shifts and across sites.
Blueprint OS is a systems-level control approach that augments operator expertise with modeling and feedback. It does not substitute for foundational process practices, but when applied to well-maintained lines it is one method for converting tacit knowledge into documented recipes, reducing guesswork and improving repeatability in powder coating production.
Link Brown is chief revenue officer at CoatingAI.