Fraunhofer Institute for Manufacturing Engineering and Automation IPA
AI-Powered Automation for Quality Control Processes
What are the biggest challenges in quality control during production?
If defects are detected too late—or, in the worst case, only after they reach the customer—this results in high costs and poses risks to reputation and delivery reliability. A 100% inspection is often feasible only with significant manpower, while at the same time, subjective inspection results lead to inconsistent quality.
Furthermore, defect patterns frequently change, and conventional inspection methods are slow to adapt to these changes, resulting in high scrap rates or costly rework. In addition, the increasing documentation requirements for audits tie up additional resources.
What are the benefits of automated quality control?
The foundation for reliable, automatically evaluable inspection results is objectivity—that is, replacing subjective visual inspections with measurable, reproducible decision criteria. These can be documented comprehensively and provide a solid basis for evidence in the event of a complaint, as well as for the continuous optimization of your quality processes. This significantly reduces staffing requirements.
Where and how can automation and AI support quality processes?
Visual Inline Inspection
Camera- and sensor-based inline inspections in 2D and 3D combine AI with analytical image processing. AI models detect complex defect patterns such as:
- Shape deviations
- Scratches, cracks, and inclusions
- Assembly errors
These systems are robust and adaptive, even when dealing with a wide variety of product variants. Additional analytical methods (e.g., edge detection, geometric measurement) precisely verify dimensions and tolerances. This enables optical defects and dimensional deviations to be inspected automatically, objectively, and fully documented.
Process parameters and time-series-based quality analysis
Quality monitoring and predictive quality work seamlessly together. Sensor data collected continuously (forces, temperature, vibration, current, etc.) is automatically analyzed using AI-based algorithms, enabling early detection of deviations in the process. At the same time, predictive quality models use historical process and quality data to forecast quality, the probability of scrap, and influencing factors. Furthermore, prescriptive approaches are used to derive specific recommendations for action from the process data. This transforms quality control from a reactive final inspection into a proactive, data-driven system that increases process stability and significantly reduces scrap.
Agent System for Quality Assurance
A quality assurance agent system integrates knowledge from manuals, process data, and inspection reports. Instead of a simple error message, inspectors receive a detailed analysis report with clear recommendations for action, such as process adjustments or rework. The agent system accesses relevant data and AI applications and can:
- Detect defects in images, pinpoint their exact location, and describe them in natural language
- Specify the position, type, and suspected cause
- Monitor sensor data in real time
- Report any discrepancies immediately
- Guide users step-by-step through the inspection
Relevant inspection instructions and documentation are automatically provided to enable faster and more reliable decisions. All inspections, measurement values, and evaluations are recorded in an audit-proof manner and prepared to meet audit requirements (e.g., for ISO, IATF, FDA). This reduces your documentation workload and increases transparency, compliance, and quality in your production and quality management.
How Can You Get Started with AI-Powered Quality Control?
The implementation of an AI-powered quality assurance system must fit within your existing structures. That’s why, when necessary, we analyze the relevant manufacturing steps, identify typical hurdles (organizational, technical, and procedural), and define key process variables (e.g., process parameters, material and environmental data) that are captured via sensors, machine integration, and measurement systems.
Based on this, key influencing factors are derived, and machine learning systems are used for the automated detection of deviations. This makes quality risks visible early on, and your processes gradually evolve from purely inspection-based quality to stable, predictive process quality.
We offer our solution with a customized approach. A proof of concept for automated, AI-supported quality control is available starting at 14,400 euros.
Using AI-powered automation for your quality processes
Would you like to assess the potential of AI for your quality processes? In a no-obligation initial consultation, we’d be happy to work with you to identify where automation and AI can deliver the greatest benefits in your quality control. Based on this, we’ll provide you with a free initial assessment—for example, by evaluating your defect patterns or directly analyzing your components.