The ZEISS Automated Defect Detection (ZADD) software detects even small and fuzzy defects in components reliably and quickly. The Automated Defect Detection (ADD) software, also called Automated Defect Recognition (ADR) software, can be used not only for injection molded parts, but also for batteries and medical or additively manufactured components.
ZEISS offers everything from a single source: with the complete ZEISS X-ray portfolio, you can use artificial intelligence (AI) in 3D computed tomography (CT) and 2D X-ray technology both inline and atline. ZEISS Automated Defect Detection (ZADD) software is compatible with all ZEISS computed tomography systems.
- Minimization of test cycle times
- Faster scans/defect detection
- Time savings for operators
- Perfect results, even if image quality is not perfect
- Suitable for mixed and dense materials
- Subjective decisions are avoided
- No reference data required in atline mode
- Custom optimization of defect analysis
- User-defined programming of the software
ZEISS Automated Defect Detection machine learning software sets new standards by applying AI to 3D CT and 2D X-ray systems with CT option. It detects, localizes and classifies defects or anomalies (irregularities) while analyzing them in detail by reading CT-scans and X-rays. Based on inspection regions, the AI software evaluates whether the defect will pose a problem after further processing steps and whether the component must be rejected as a result. If similar defects occur more frequently in inline operation, this can be detected and thus intervention in the production process can take place at an early stage to reduce scrap and save costs. In atline operation, the ZADD software is convincing in optimizing and monitoring the casting process as well as improved and faster component development.
During the complex manufacturing process of components, different defects can occur. Especially inside the part, defects are invisible to the naked eye and can have a major impact on the stability and functionality of the component. Artificial intelligence combined with industrial computed tomography or 2D X-ray technology makes these hidden problem areas visible, analyzes them and detects them at an early stage. The ZEISS Automated Defect Detection software specializes in the detection of different defects, so that defects can be detected quickly and reliably even in poor image quality with many artifacts.
In order to be able to sort out defective components at an early stage in a value chain, the 3D data must be evaluated reliably and quickly during an inline inspection. In just 60 seconds, the ZEISS Automated Defect Detection analysis software reliably examines 4 billion voxels (3D pixels) for defects that are only a few voxels in size. Components with critical defects are thus accurately sorted out or, if possible, subjected to rework. Good parts, on the other hand, pass through the further machining process unhindered. The result: a lower reject rate and high component quality. In this way, you achieve a constant increase in efficiency and maximum process reliability with AI in CT.
If the ZEISS ADD software detects a defect, the software evaluates it in terms of position, shape, size and type. For example, if pores are close to a surface that will be machined in the further process, they may pose a greater risk than elsewhere inside the component. The software is able to predict whether the defect may cause problems in subsequent machining steps. The component is then automatically rejected at an early stage. This saves you time and money. In addition, you can define in the ZADD software under which criteria a defect still meets the quality standards and when it is classified as critical. With ZEISS Automated Defect Detection, you get a solution that is tailored precisely to your requirements.
If the ZEISS Automated Defect Detection software finds a critical defect in inline operation, it creates a report using the ZEISS PiWeb data management software. This gives you the opportunity to view and evaluate defects again afterwards in a 3D view. If your manual inspection leads you to the conclusion that the defect was caused by sand residue, for example, you can easily remove this source of defect and avoid unnecessary scrap. This results in high cost savings. The software also enables results to be displayed in tabular form and output in common formats such as csv.
A pore is a spherical or ellipsoidal cavity with mostly smooth walls inside the component. Depending on their origin, they can contain air, vapor, hydrogen, or other gases (e.g. from lubricants). They often occur in upper casting layer, but in poorly evacuated areas or undercuts they can be distributed within the whole casting.
Cold run occurs preferably on flat surfaces with relatively low thickness. This can result in a separation of the cohesion, leaving holes, areas that have not run out, but also rounded edges and overlaps. In die casting, cold run can be seen on very fine and thin surface slates.
Damage to the casting core, such as breaking off or breaking apart, leads to defective component geometries during the casting process, which can have a significant impact on functionality. This can be caused by suboptimal molding material composition or excessive thermal stress.
Shrinkage holes occur through the shrinkage process during the solidification of melted metal. The degree of shrinkage depends on the melting point of the material. As the outer shell and sprue of a casting generally solidify early, volume deficits can be formed in the interior of the casting. Shrinkage is characterized by rough and spongy surfaces and is more elongated.
Micro-porosity can be understood as an accumulation of small shrinkage holes (micro-shrinkage / interdendritic shrinkage), which can create chains and lead to leakages. This porosity appears in a CT scan with lower resolution as spongious areas.
Through gravity casting and low-pressure die casting, residuals of sand (or salt) cores may remain in the interiors of parts, when the coring process is not sufficient.
If, for example, defects occur in the positioning of the core in the mold before casting, or if the cores shift during the casting process, the geometries of the casting no longer match the CAD model.
During rough machining of the component (e.g. saw cut on the feeder), aluminum chips are produced which can fall into the component. Likewise, small protrusions (feathers) can break off during coring and remain in the component. These aluminum residues can lead to defects in the cooling system, for example, during subsequent operation.
Inclusions are partially or completely embedded impurities in the cast component which are usually denser than the base material. They are caused, for example, by foreign bodies in the casting mold or by contaminated casting material.
ZEISS Automated Defect Detection is compatible with any ZEISS computed tomography system and can be easily integrated into your process. During continuous inline measurement along the entire process, ZADD delivers precise results and detects the smallest defects or flaws early on.
ZEISS also offers a complete solution from a single source: with the ZEISS VoluMax inline X-ray system, you have the option of using AI for reading 3D CT scans with perfectly coordinated measurement technology and software.
- Fully automated, non-destructive inline inspection
- Fast inspection of many workpieces in 3D
- Individual configuration depending on measurement task by ZEISS
- Choice of manual or automatic loading
- With Golden Part Inspection
Atline measurements also benefit from AI in CT. Complex measurement procedures can be performed in the quality lab without the environment of production influencing the results. The ZEISS Automated Defect Detection software makes manual measurements more precise and detects defects in production quickly and accurately. In combination with ZEISS atline X-ray systems and GOM Volume Inspect Pro, you can achieve compliance with high inspection standards and efficient quality control.
The ZEISS team accompanies you from the very beginning! You will receive support in the design and programming of the algorithm, so that your ZEISS X-ray system in combination with the ZEISS Automated Defect Detection software will contribute to process reliability and increased efficiency.
The ZEISS team is available to help you adapt and optimize the algorithm of the ZEISS Automated Defect Detection software. ZEISS is also your competent partner for other questions and applications related to metrology.
ZEISS caters to your application areas and needs! The AI-driven ZEISS Automated Defect Detection software can be trained with an individual model to become a customized solution for your measurement task.
AI drives CT forward
Artificial intelligence is ubiquitous. Autonomous driving is just one of many examples of the application of AI. Artificial intelligence is also a topic in industry and thus in computed tomography and is becoming increasingly significant. This is because it enables defect analyses to be carried out even more reliably, accurately and quickly. In industry, a defect is often located inside a component. An optical inspection process for quality control is then no longer sufficient because it does not provide any indication of internal defects. X-ray inspection allows a close look inside a component and can thus detect defects at an early stage. By using AI in 3D CT and 2D X-ray inspection, a partially automated defect analysis is realized.
Explanation of terms:
In connection with AI and CT, the terms AI Defect Detection or AI Anomaly Detection are often used. AI stands for Artificial Intelligence and Defect Detection or Anomaly Detection means defect detection or anomaly detection. The addition of "NDT" makes it clear that AI works non-destructively, because NDT stands for non-destructive testing.
The ZADD software has several features that you can use optimally in either your inline or atline process. In the slider, the features and for which solution they are suitable are explained in detail.
- The software will detect anomalies regardless of defect type
- The overall volume for thorough analyses is drastically reduced – the inspection process is decisively accelerated
- The process is based on classic machine learning methods
- Enables fast and easy adaptation to new components
- AI teach-in is completed after only 30-50 scans of good parts
- The defect type of each anomaly found is determined
- Depending on the type of defect, the part is forwarded for rework, toleranced as a good part while still within tolerance, or classified as scrap
- The process is based on Deep Learning
- No customer-specific training necessary
- Parts with uncertain classification can be forwarded to an operator for manual inspection
- ZADD software determines the exact 3D shape of the defect
- This allows the following to be accurately calculated:
- Exact defect sizes
- Defect volume
- Distance to the machined surface
- The process is based on Deep Learning
- The decision whether the part is a good part or not is based on predefined values and tolerances
- The X-ray inspection tolerance workflow is rule-based and does not need to be re-trained
- The software is easy to understand and customized for application experts
- Defined regions allow differentiated decisions regarding relevance
- That is, in one place the defect may be problematic, in another place it is tolerable
- The operator can overrule the suggestions of the ZADD software
- All defects (sectional views and properties) are collected in a database
- Archiving of data allows long-term tracking of defects
- ZADD software provides advanced visualization features for a holistic view
- Statistical process control enables the detection of process correlations and optimizations
- Future trend: With the use of machine learning, process deviations can be detected more quickly
Artificial intelligence is a trend in automation. Process requirements are becoming tighter and tighter, and even in harsh measurement environments, image evaluation and defect analysis must work quickly and reliably. This is especially true for safety-relevant components, e.g. in the automotive industry or aerospace. To increase quality by performing defect analyses faster, while at the same time offering high process reliability, AI is used for reading CT-scans. Defect detection with AI eliminates the need for manual tuning of parameters, thus avoiding subjective decisions in defect detection.
ZEISS Automated Defect Detection is particularly useful when volume data is affected by overly dense materials or short scan times. While artifacts and noise in the images usually cause faulty detections, the software remains unaffected by these effects.
Take a look at specific examples of how AI is used in 3D CT, but also in X-ray inspection in general: