AI-Enhanced Surface Treatment and Automation Project

AI-Enhanced Surface Treatment and Automation Project

Many manufacturers of components for heavy-duty vehicles, trailers, buses, defense, railway systems, and light commercial vehicles operate large-scale production facilities producing parts from elastomers, metals, rubber-metal composites, plastics, and aluminum. Due to requirements such as rubber-to-metal bonding and corrosion resistance, components often undergo pre-treatment in surface treatment baths before final processing.

Within surface treatment operations, the zinc phosphate non-electrolytic immersion coating line is a critical process. Currently, laboratory analysis of bath samples is performed at intervals (e.g., every 8 hours) to ensure product quality. Based on these analyses, the required chemical additives are manually dosed into the baths. This manual, time-consuming approach introduces several limitations:

• Instability of bath chemistry due to infrequent analyses

• Non-homogeneous bath conditions, negatively affecting phosphate crystal morphology

• Overdosing of chemicals to compensate for uncertainty, leading to sludge precipitation and waste

• Increased environmental impact due to chemical disposal requirements

This project aims to implement an AI-driven automated analysis and dosing control system, transforming the process into a closed-loop, self-regulating operation.

Project Objective

The primary objective is to replace manual sampling and dosing in zinc phosphate coating lines with an AI-based automation system that will:

1. Perform continuous or frequent in-line/at-line measurements of bath parameters using AI-enabled sensors and image-processing technologies.

2. Apply machine learning algorithms to predict deviations in chemical balance before they occur.

3. Optimize chemical dosing dynamically, ensuring only the required amount of additives is introduced.

4. Maintain stable bath chemistry, guaranteeing consistent coating quality across all treated products.

This approach stabilizes the process and introduces predictive control, where chemical adjustments are proactively made before instability affects product quality.

Keywords: Surface treatment technologies, image processing, artificial intelligence, process automation, digital twin optimization, chemical process control

Innovative Aspects

The key innovation is the integration of artificial intelligence and digital twin technologies into surface treatment operations:

• AI-driven frequent analysis: AI-enabled systems provide high-frequency, automated chemical bath assessments, replacing periodic manual testing.

• Adaptive dosing via machine learning: Algorithms identify patterns in bath chemistry, learn from historical data, and optimize dosing schedules and quantities.

• Digital twin optimization: A virtual replica of the coating line simulates chemical reactions, predicts sludge formation risks, and evaluates dosing strategies in real time to maximize efficiency and minimize environmental impact.

• Resource efficiency: AI models continuously reduce chemical usage, prevent sludge formation, and extend bath life cycles.

• Quality improvement: Stabilized bath conditions ensure uniform phosphate coating crystallinity, improving product durability, reducing defects, and lowering scrap rates.

By shifting from human-dependent, error-prone manual systems to an AI-augmented autonomous control loop, the project delivers operational efficiency and environmental sustainability.

Expected Outcomes

• Stable bath chemistry throughout the coating process

• Consistent coating quality across all treated components

• 20–30% reduction in chemical consumption due to optimized dosing

• Elimination of sludge precipitation and reduced environmental impact

• Lower operational costs from reduced chemical waste and scrap rates

• Establishment of a scalable AI-driven automation framework applicable to other surface treatment lines