- 100% inspection
- Automatic rejection
- Classify, quantify and log defects
- Automated, just-in-time QA sampling
- Advanced, ad-hoc product development
- Quantify product quality characteristics
Our global install base of machine vision systems spans the synthetic rubber industry and foods industry, in addition to other applications. Our clients not only inspect their manufactured products, but they use our solutions’ outputs to gain new information for process troubleshooting, monitoring and improvement.
It All Began With Doritos
ProSensus’ history with machine vision began in 2004 when PepsiCo approached us to quantify a hard to measure attribute (seasoning levels) on Doritos chips as they were experiencing unacceptable variation.
Using multivariate image analysis, we successfully quantified the seasoning levels. This allowed PepsiCo to implement a closed-loop control system, reducing variation by 50%.
Since 2004, ProSensus has continued to perform offline feasibility studies and we have developed and proven our ability to implement complete systems for our clients.
Machine Vision for Industry 4.0
The effective use of image data for quality monitoring is an innovative concept consistent with the Industry 4.0 movement.
A ProSensus machine vision turnkey solution addresses all four of the Industry 4.0 design principles.
|Technical Assistance & Decentralized Decisions||The ProSensus solution (which includes custom hardware, algorithm, and control room software) is designed to act as an autonomous, on-line quality control system with optional results visualization for operator support|
|Interoperability & Information Transparency||Detection of defective product is facilitated via information exchange between hardware and software. All vision sensor data results are logged to a database and images are logged to a storage server.|
ProSensus combines advanced analytics with hardware to maximize the insights from machine vision solutions. Contact us to get started with an on-site trial, or an off-line analysis of your existing images so you can gain new insights that can lead to significant process improvement.