Handling and inspection of indexable inserts

Indexable inserts are interchangeable cutting tools that are indispensable in various industrial applications, particularly in metalworking. They are used as cutting material carriers for cutting metals, plastics or wood. Their manufacture requires high-precision production processes to ensure exact geometry and perfect surface quality. Even minimal deviations affect not only the service life but also the performance of the cutting insert. The smallest defects, invisible to the human eye, can cause immense damage, for example when milling or cutting high-quality components - including consequential costs. Careful quality control is essential to ensure that only flawless inserts leave the production process and meet the high requirements in terms of durability and reliability.

A flagship project by automation and measurement technology specialist Xactools from Baden-Württemberg demonstrates how artificial intelligence can help visual inspection make quantum leaps. The Swabian medium-sized company has developed a fully automated handling and inspection system for Seco, a provider of machining solutions and manufacturer of indexable inserts based in Fagersta, Sweden, in which the Denknet solution for AI-based image evaluation plays a decisive role and sets new standards in terms of performance, zero-defect production and speed. Key factors for the successful implementation of such a project are the close cooperation of all those involved, their technical expertise and the team's high level of commitment. Martin Tobias Lithner, IIoT Manager and Data Scientist at Seco, played a central role in this. Together with Kyle Paulus, Sintering Engineer at Seco, he continuously drove the project forward from the conception phase onwards.

Detect defects at an early stage

Around 1.2 million indexable inserts leave Seco's production halls every week, which have to guarantee the highest possible process reliability and productivity in the metalworking, automotive and aerospace industries, for example.

They are manufactured using the sintering process, in which powdered metals, hard metals and other materials are pressed into the desired shape and then sintered, i.e. bonded together under heat and pressure. The strong and robust structure created in this way makes it possible to combine materials with different properties in order to achieve the desired cutting and wear resistance properties. After the sintering process, the edges of the indexable inserts are rounded and ground, and their surfaces are blasted, ground and coated.

The Robotvision system from the Swabian engineers is used directly after the second manufacturing step, the sintering process. "The earlier defects are detected in the process, the better and cheaper it is to rectify them," says Marvin Krebs, Managing Director at Xactools, explaining the position of the system. A total of eight high-resolution industrial cameras and two spider robots are used to handle and inspect the indexable inserts for defects, which keep an eye on and load three rotary table nests and finally one pin pallet each. At the heart of the complex image processing system between cameras, robots and a multi-GPU computing rack is the AI from Denknet.

Training for correct recognition

As versatile as the areas of application of the small tool parts are, so varied are their properties and geometries. This manufacturer alone has around 2,800 products in its portfolio, which can be divided into almost one hundred geometry families. The task was to automate handling and defect inspection for all of these. "The first challenge results from the numerous color variations within the powder per pressing process," explains Marvin Krebs. "If certain parameters such as time, pressure or positioning vary, this leads to color or gloss level deviations or to a different distribution of so-called speckles on the surface, which is not a defect." The AI-based image evaluation software used had to be trained to correctly recognize the numerous possible colour deviations of the surfaces and evaluate them as "OK". On the other hand, the smallest irregularities such as cracks, scratches, inclusions or other anomalies must be recognized as such and classified as "Not OK". The inspection of metal surfaces is considered one of the highest arts of surface inspection, as their texture can be matt, shiny or even reflective. "The AI had to be extremely sensitive to variations and lighting conditions for this application," emphasizes Marvin Krebs.

However, in addition to the visual appearance, it is also about the insert geometry. Categories such as triangle, rectangle, rhombus or square can be found in countless variants due to the smallest deviations and are therefore divided into manageable sub-categories, so-called geometry families. Xactools made the pre-selection for training the meshes; almost one hundred geometry families were defined and then taught in by the manufacturer itself. What sounds like a laborious undertaking was completed surprisingly quickly. "No more than 20 to 30 images were needed to teach in each geometry family," recalls Marvin Krebs. The Denknet palletizing AI used for this purpose uses the Denknet segmentation and classification network. The customer itself trained the individual image analysis solution with the Denk Vision AI Hub. In just a few months, the AI was integrated into the production line and achieved almost perfectly reliable AI results for the metal components to be inspected right from the start. "Indexable inserts identified as defective are sorted out and grouped according to the size and position of the defect. The AI image analysis detects 99% of manufacturing defects," emphasizes Daniel Routschka, Sales Manager Artificial Intelligence at IDS Imaging Development Systems.