Quality projections through IOT-based data analysis technology Developing failure-prevention methods
We develop technologies to innovatively improve productivity by conducting surveys and analyses of production lines designed and manufactured with conventional technologies and introducing leading-edge technologies in order to realize highly productive operating conditions.
1. Visualizing production conditions
We set up a system to constantly record production conditions every time a product is formed on the production line.
This has enabled production conditions and the occurrence of defects to be visualized in real time and the realization of a system for immediately detecting abnormalities, such as line malfunctions and defects that occur in large numbers.
2. Utilization in terms of the management of production lines
In order to continue producing high-quality products with high capacity-operating rates, equipment needs to be operating under ideal conditions. For this reason, various types of data are collected from each type of equipment for use in the management of production lines.
By using numbers and graphs to visualize the number of line equipment stoppages, changes in inspection results, the status of tool life management, and more, areas that should undergo improvements can be clarified, which should then lead to improved capacity-operating rates.
3. Reducing defects through data analysis based on the use of IOT data
We have set up a system to collect and analyze data linking production-related measurement data (approximately 120 types and 15,000 points per shot) with quality information. Whenever a defective product emerges, we can now ascertain adverse factors by employing machine-learning analysis and other analytical methods.
Developing technology to automate inspection processes using inspection equipment with onboard AI technology
The use of AI-based image inspection equipment delivers a number of advantages, including thestabilization of inspection decision criteria, a reduction in the number of inspection man-hours needed (by 50%), a reduction in skills training for inspections, and the quantification and recording of defects.Die-cast products can vary in terms of casting surface even when they are produced under the same conditions. We have focused on AI technology as a means of taking in such variations in inspected items and correctly separating defective and non-defective items from one another and are working to apply automatic inspection equipment at a practical level.
By using AI-based image inspection equipment to automatically assess flaws, impurities, product chipping, and other defects, we have managed to halve the amount of time spent by inspectors to carry out work and help reduce the number of inspectors needed for this purpose. We will continue to promote improvements and the introduction of suitable technology in this area.
