Vision Systems as Part of a Larger Quality Architecture
Artificial intelligence is increasingly embedded in industrial inspection, but its role is often misunderstood. In practice, AI is rarely a standalone solution. Instead, it functions as one element within a broader verification architecture, deployed selectively where conventional approaches reach their limits.
As Alex Hunt Vision Tools engineer explains, neural networks are most effective when applied to specific, high-complexity tasks rather than as a universal replacement for rule-based vision systems. Traditional machine vision continues to handle a large proportion of inspection requirements and remains a deliberate and essential component of modern system design.
This hybrid approach reflects two realities on today’s shopfloor. First, established vision techniques remain highly effective and are not being displaced. Second, a growing skills gap means many manufacturers cannot rely on specialist engineers to continually optimise inspection systems. As a result, there is increasing emphasis on platforms that minimise dependency on expert intervention while maintaining performance.
Optical Complexity in EV Battery Inspection
Inspection challenges in electric vehicle (EV) battery production illustrate the limits of conventional imaging. Battery assemblies often combine low-contrast materials—such as dark sealants on black housings or transparent adhesives on reflective substrates—that are difficult to detect using standard 2D vision.
To address this, manufacturers are adopting multi-modal imaging strategies. These may include low-angle illumination, fluorescent techniques for transparent media, spectral imaging to differentiate visually similar materials, and multi-angle shadow casting to build composite views when single images are insufficient. Feasibility studies are typically required to determine the optimal combination for each application.
In some cases, the most effective solution lies not in inspection, but in process optimisation. Eliminating a defect at source can be more robust and cost-effective than attempting to detect it downstream.
Inspection in Motion: Robotics and Vision Integration
The integration of vision systems with robotic platforms introduces additional complexity. Motion, vibration and variable working distances all impact image quality and measurement reliability.
Systems designed for these environments incorporate features such as global-shutter sensors to eliminate motion distortion, calculated exposure and lighting parameters to manage blur, and automated optics to maintain focus across changing fields of view. Safety considerations are equally critical, particularly in collaborative robot (cobot) environments, where end-effector design and formal safety validation play a central role.
Context-Driven Data Requirements
The effectiveness of AI-based inspection is closely tied to application context. Data requirements vary significantly depending on the feature being inspected.
Simple, high-contrast features can often be handled without AI, while more complex scenarios – such as detecting foreign objects in visually cluttered environments – require larger and more diverse datasets. Edge cases further complicate inspection; for example, overlapping components may appear acceptable in a 2D image, necessitating multi-camera setups or height-sensing technologies to resolve ambiguity.
In well-engineered systems, detection rates exceeding 99% are achievable, often with greater repeatability than manual inspection. Validation commonly involves parallel operation of manual and automated processes before full deployment. A key advantage is traceability: storing inspection data enables trend analysis and supports continuous improvement initiatives.
From Detection to Closed-Loop Quality Control
Modern inspection systems are increasingly integrated into production environments rather than operating as isolated checkpoints. Pass/fail signals can be communicated directly to PLCs, while more detailed data—such as part identity and variant information—can be incorporated into digital production records.
Where defects are correctable, operators can be alerted in real time, enabling intervention and reinspection without removing components from the production line. Some manufacturers implement rework loops, allowing defective parts to be diverted, corrected and revalidated before re-entering the process.
This integration supports a shift toward closed-loop quality control, where inspection not only detects defects but actively contributes to process optimisation.
Thermal Imaging as a Process Indicator
Temperature monitoring is emerging as a critical quality parameter, particularly in battery manufacturing. Inline thermal imaging systems provide continuous, colour-mapped temperature data, allowing manufacturers to define acceptable thresholds and trigger alarms when deviations occur.
By embedding thermal analysis within the inspection process, manufacturers can move from retrospective quality checks to proactive, preventative control—identifying potential issues before they escalate.
Accelerating Changeovers with Synthetic Data
As product variants proliferate, rapid changeover becomes a competitive necessity. One emerging approach involves the use of synthetic images generated from CAD models to develop and validate inspection programmes offline.
This enables manufacturers to prepare for new variants without interrupting production, reducing commissioning time and minimising the need for physical test runs. Early implementations have demonstrated the potential of this approach, with ongoing development aimed at broader industrial adoption.
EV Batteries as a Catalyst for Innovation
The EV battery sector has become a focal point for advanced inspection technologies. High production speeds, stringent safety requirements and frequent design changes create an environment where flexible, scalable inspection solutions are essential.
While AI has clear benefits in assembly and subcomponent inspection, its application at cell level remains constrained by extreme cycle times. As a result, current deployments tend to focus on areas where the balance of cost and performance is most favourable.
Integration and Ecosystem Development
Recent industry developments highlight a move toward more integrated inspection ecosystems. The acquisition of VisionTools GmbH by Atlas Copco reflects this trend, combining specialised expertise in AI-supported vision with a broader global infrastructure.
The emphasis is shifting away from standalone products toward cohesive architectures, where inspection technologies are aligned with specific applications and integrated within wider smart manufacturing frameworks.
The Path Toward Zero Defects
The pursuit of zero-defect manufacturing remains a key objective across industry, but it is best understood as a continuous journey rather than a fixed endpoint. Achieving it requires not only advanced technology, but also stable processes, high-quality data and systems that can be maintained effectively on the shopfloor.
Over the next three to five years, a significant shift is expected: from defect detection toward process interaction. Inspection systems will increasingly function as nodes within closed-loop quality systems, enabling earlier intervention and more efficient control.
Safety, Traceability and Accountability
In sectors such as battery manufacturing, quality is closely linked to safety, regulatory compliance and brand reputation. Advanced inspection systems, including AI and thermal monitoring, enable earlier and more consistent detection of anomalies in complex production environments.
Equally important is the ability to generate traceable records, providing the documentation required for compliance and supporting accountability across the product lifecycle.
From Supplier to Strategic Partner
As inspection technologies become more integrated and application-specific, the role of solution providers is evolving. Rather than delivering standalone systems, they are increasingly acting as strategic partners – working with manufacturers to define failure modes, identify risks and implement tailored solutions.
This approach extends beyond initial deployment to include integration, global rollout and ongoing lifecycle support, ensuring that inspection systems continue to deliver value as production requirements evolve.
For more information: www.atlascopco.com
