Data-based optimization of manufacturing processes

Production systems are subject to a wide range of settings and disturbances, and their control is correspondingly complex. Process settings are often based on experience. Due to external changes - such as new material batches or varying environmental conditions - processes must be constantly readjusted and are rarely operated at their optimum.

We help you adapt your production in an agile way so that you can produce efficiently regardless of external conditions. We do this by working with you to validate and extend your process knowledge. We use a variety of approaches, including capturing data from your current production or generating new data sets using design of experiments (DoE), depending on your use case and objectives.

Benefits of data-based process optimization

• Increased efficiency: By optimizing process parameters, you can increase the performance level of a machine/system and make more efficient use of materials.
• Improved product quality: Precise control and monitoring of processes improves quality and makes product quality more consistent.
• Reduced costs: Reduced scrap and rework, lower maintenance costs and reduced energy consumption lead to significant cost savings.
• Flexibility and adaptability: The ability to react quickly to changes and adjust processes in real time increases the flexibility of your production.

Our approach

1. Data acquisition and analysis:

• Real-time data collection: we integrate sensors and IoT devices into your manufacturing equipment to collect real-time data about your processes.
• Historical data analysis: Using existing data to identify patterns and trends.
• Design of Experiments (DoE): Systematic planning and execution of experiments to investigate specific influencing factors and their interactions.

2. Data processing and optimization:

• Data cleansing: Ensuring data quality by removing outliers and noise.
• Data integration: Combining data from different sources for a comprehensive analysis.
• Applying optimisation algorithms: Using machine learning and artificial intelligence to identify optimal process parameters.

3. Implementation and customization:

• Adaptive control systems: Designing and implementing adaptive control systems that automatically adapt to changing conditions.
• Continuous monitoring: Continuous monitoring and adjustment of processes to ensure consistently high product quality.

Examples of use

• Automotive industry: Optimising manufacturing processes in vehicle assembly
• Electronics manufacturing: Improving assembly and soldering processes through precise process control.
• Food industry: Ensuring consistent product quality despite variations in raw material characteristics.
• Chemical industry: Optimizing reaction conditions and reducing by-products through precise process control.