Quality data analysis for production and manufacturing with i-Analyzer
Intuitive, software-supported evaluation of quality data
i-Analyzer is a browser-based application designed to reduce the workload for IT administrators when establishing a quality control environment, including deployment, installation, maintenance and system management. At the same time, it enables convenient access to centrally stored information, supports data editing and analyses quality data in depth.
In industrial manufacturing, quality data analysis involves the systematic evaluation of production and inspection data to identify defects, trends, root causes and corrective actions. It enables production processes to be stabilised and improved in a targeted manner. Data is never considered in isolation, but always within the context of, for example, a machine, shift or batch. Only in this way can users identify quality issues at an early stage and understand their causes.
i-Analyzer interprets production-related quality information using a wide range of analytical methods, visualisations and machine learning technologies. The software for production data analysis supports users in forecasting quality trends and in detecting potential issues at an early stage.
Database and filter
Recorded quality information is automatically written to the database using key attributes such as item name, item number, production line and process, and is also linked to inspection characteristics. With fast filtering by date, time, process, machine and additional parameters, users can immediately retrieve the records required to analyse production data in a targeted manner. Individual database queries can also be configured.
i-Analyzer for quality data analysis works with two central database technologies: MySQL and MS SQL Server.
Various charts and methods for statistical quality data analysis
i-Analyzer as part of the iNDEQS software suite delivers comprehensive statistical productiondata analysis and displays the results in clear graphical formats. Examples include value charts, ARIMA forecasts, histograms, probability plots, box plots and control charts (QCC). Beyond visualization, the application provides analytical procedures such as regression, correlation and factorial analysis.
The system determines the capability indices necessary for quality evaluation and generates a wide range of aggregated graphics and reports. These structured overviews make it straightforward to introduce focused corrective actions and continuously improve production processes.
Users can zoom in and out of every chart and adapt colour schemes and graph sizes. All statistical displays are designed to be intuitive and easy to interpret, offering effective visual support for day-to-day quality data analysis.
Machine Learning in quality management
Before completion, a component passes through multiple processes and production facilities. Numerous variables influence operating and manufacturing conditions. In machining, for instance, relevant parameters include feed rate, spindle speed, temperature, tool wear and coolant supply. The final product quality reflects the interaction of all these production factors.
Through machine learning-based production data analysis, relationships between process parameters and inspection outcomes can be uncovered through simple linear correlation. The algorithms identify which parameters have the greatest impact on results and determine the most suitable operating conditions. This enables rapid detection of production issues and highlights opportunities for improvement.
Looking for quality reporting that is perfectly tailored to your needs?
Let us explain the advantages of the server or standalone solution, which will enable you to receive all relevant quality information in highly informative and clearly structured reports.
