Analytics

Do you understand why your Vision System rejects parts?

We feel this is an overseen part in many vision systems. Most of them just return a fail/pass for each item maybe a count on the screen. If the application has to be restarted the count resets. There is no information why parts fail.

Analysing outputs from a vision system is a critical aspect of improving Return on Investment (ROI) for several reasons. The insights gained from the analysis can lead to enhanced system performance, increased efficiency, and better decision-making. Here’s how the analysis of outputs in a vision system can contribute to improving ROI:

Reasons to include analytics:

Quality Control and Defect Detection.
Vision systems are often used in manufacturing environments for quality control. Analyzing the system outputs allows for the identification and categorization of defects or anomalies in products.
By minimizing defective products and improving quality, companies can reduce waste, avoid rework costs, and enhance customer satisfaction, leading to a positive impact on ROI.
Process Optimization.
Continuous analysis of vision system outputs enables the identification of bottlenecks, inefficiencies, or variations in production processes.
By optimizing workflows based on insights gained from the vision system, organizations can improve production efficiency, reduce cycle times, and lower operational costs, contributing to a better ROI.
Reduced Downtime and Maintenance Costs.
Vision systems can be used for predictive maintenance by monitoring equipment conditions through image analysis. Early detection of equipment issues can prevent unplanned downtime and reduce maintenance costs.
Analysing outputs for patterns indicative of potential equipment failures allows for proactive maintenance, minimizing disruptions and improving overall system reliability.
Increased Throughput and Production Rates.
By analyzing outputs related to production rates, cycle times, and throughput, organizations can identify opportunities to increase the speed of manufacturing processes.
Improved throughput leads to higher production volumes, potentially resulting in increased sales and revenue, thus positively impacting ROI.
Data-Driven Decision-Making.
Analyzing vision system outputs provides valuable data that can inform decision-making at various levels of the organization.
Data-driven decision-making enables organizations to make informed choices about resource allocation, process improvements, and strategic planning, ultimately contributing to improved ROI.
Enhanced Product Traceability.
Vision systems often play a crucial role in tracking and tracing products throughout the manufacturing and supply chain.
Improved traceability not only helps in compliance with regulations but also allows for better recall management, reducing the financial impact of recalls and potential damage to the brand.
Minimized False Positives/Negatives.
Continuous analysis of outputs helps in refining and fine-tuning the algorithms used by the vision system.
By minimizing false positives (incorrectly identifying defects) and false negatives (missing actual defects), the system becomes more reliable, reducing the likelihood of unnecessary rejections or defects escaping detection, thereby improving ROI.
Adaptability to Changing Conditions.
Analysis of vision system outputs enables organizations to adapt the system to changing conditions, such as variations in lighting, environmental factors, or product specifications.
An adaptable vision system ensures consistent performance and accuracy, even in dynamic manufacturing environments.
Improved Training and Maintenance.
Analyzing outputs can provide insights into the performance of the vision system under different conditions.
This information is valuable for training personnel, ensuring they understand the system’s capabilities and limitations. Additionally, it aids in developing effective maintenance schedules based on actual usage patterns.