![使用蔡司Sigma 300进行软磁复合材料的定量EBSD研究 使用蔡司Sigma 300进行软磁复合材料的定量EBSD研究]({"xsmall":"https://www.zeiss.com/content/dam/rms/reference-master/resource-center/insights-hub/thumbnails/screensaver-routine-metallography-3.jpg/_jcr_content/renditions/original.image_file.100.100.230,0,824,594.file/screensaver-routine-metallography-3.jpg","small":"https://www.zeiss.com/content/dam/rms/reference-master/resource-center/insights-hub/thumbnails/screensaver-routine-metallography-3.jpg/_jcr_content/renditions/original.image_file.360.360.230,0,824,594.file/screensaver-routine-metallography-3.jpg","medium":"https://www.zeiss.com/content/dam/rms/reference-master/resource-center/insights-hub/thumbnails/screensaver-routine-metallography-3.jpg/_jcr_content/renditions/original.image_file.594.594.230,0,824,594.file/screensaver-routine-metallography-3.jpg","large":"https://www.zeiss.com/content/dam/rms/reference-master/resource-center/insights-hub/thumbnails/screensaver-routine-metallography-3.jpg/_jcr_content/renditions/original.image_file.594.594.230,0,824,594.file/screensaver-routine-metallography-3.jpg","xlarge":"https://www.zeiss.com/content/dam/rms/reference-master/resource-center/insights-hub/thumbnails/screensaver-routine-metallography-3.jpg/_jcr_content/renditions/original.image_file.594.594.230,0,824,594.file/screensaver-routine-metallography-3.jpg","xxlarge":"https://www.zeiss.com/content/dam/rms/reference-master/resource-center/insights-hub/thumbnails/screensaver-routine-metallography-3.jpg/_jcr_content/renditions/original.image_file.594.594.230,0,824,594.file/screensaver-routine-metallography-3.jpg","max":"https://www.zeiss.com/content/dam/rms/reference-master/resource-center/insights-hub/thumbnails/screensaver-routine-metallography-3.jpg/_jcr_content/renditions/original.image_file.594.594.230,0,824,594.file/screensaver-routine-metallography-3.jpg"})
用于常规金相学的显微镜解决方案
革新性常规金相学
探索用于金属分析的前沿显微镜解决方案
金属特性及其在使用中的行为主要受其微观结构影响。所有关键的机械特性、腐蚀行为和疲劳性能都受到晶粒尺寸、成分、相尺寸/分布、夹杂物和局部微观结构变化的综合影响。
针对这一需求,蔡司提供先进的解决方案产品组合,涵盖了用于常规金相学和质量控制的各种显微镜技术:
![使用机器学习增强图像分析 使用机器学习增强图像分析]({"xsmall":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.100.67.0,65,960,705.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","small":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.360.240.0,65,960,705.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","medium":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.768.512.0,65,960,705.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","large":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.960.640.0,65,960,705.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","xlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.960.640.0,65,960,705.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","xxlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.960.640.0,65,960,705.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","max":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.960.640.0,65,960,705.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg"})
![使用机器学习增强图像分析 使用机器学习增强图像分析]({"xsmall":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.100.80.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","small":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.360.289.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","medium":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original.image_file.768.616.file/rapid-machine-learning-segmentation-of-duplex-ss.jpg","large":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original./rapid-machine-learning-segmentation-of-duplex-ss.jpg","xlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original./rapid-machine-learning-segmentation-of-duplex-ss.jpg","xxlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original./rapid-machine-learning-segmentation-of-duplex-ss.jpg","max":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/rapid-machine-learning-segmentation-of-duplex-ss.jpg/_jcr_content/renditions/original./rapid-machine-learning-segmentation-of-duplex-ss.jpg"})
使用机器学习增强图像分析
在进行图像或三维数据集的定量分析时,分割步骤必不可少。分割是将图像划分为多个区域并以某种方式将其相互区分的操作。分割完成后,便可对这些区域进行分析,以获取实际可操作的数据——这些区域可以是单个晶粒、夹杂物、气孔、不同的相、层或任何需要检查的对象。
多区域分割可能极具挑战
- 不同区域可能具有相似的颜色/对比度
- 不同区域可能只有纹理不同
- 伪影/划痕可能会产生错误信息
- 三维数据集中的噪声会影响分割精度
- 多通道(RGB或更多)图像的分析越来越复杂
![蔡司ZEN Intellesis使用向导式机器学习来克服这些分割问题。 蔡司ZEN Intellesis使用向导式机器学习来克服这些分割问题。]({"xsmall":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.100.75.0,51,960,771.file/zen-intellesis-monitor_2021.jpg","small":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.360.270.0,51,960,771.file/zen-intellesis-monitor_2021.jpg","medium":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.768.576.0,51,960,771.file/zen-intellesis-monitor_2021.jpg","large":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.960.720.0,51,960,771.file/zen-intellesis-monitor_2021.jpg","xlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.960.720.0,51,960,771.file/zen-intellesis-monitor_2021.jpg","xxlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.960.720.0,51,960,771.file/zen-intellesis-monitor_2021.jpg","max":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.960.720.0,51,960,771.file/zen-intellesis-monitor_2021.jpg"})
![蔡司ZEN Intellesis使用向导式机器学习来克服这些分割问题。 蔡司ZEN Intellesis使用向导式机器学习来克服这些分割问题。]({"xsmall":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.100.104.file/zen-intellesis-monitor_2021.jpg","small":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.360.375.file/zen-intellesis-monitor_2021.jpg","medium":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original.image_file.768.800.file/zen-intellesis-monitor_2021.jpg","large":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original./zen-intellesis-monitor_2021.jpg","xlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original./zen-intellesis-monitor_2021.jpg","xxlarge":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original./zen-intellesis-monitor_2021.jpg","max":"https://www.zeiss.com/content/dam/rms/reference-master/applications/raw-materials-industrial-r-d/metals/zen-intellesis-monitor_2021.jpg/_jcr_content/renditions/original./zen-intellesis-monitor_2021.jpg"})
蔡司ZEN Intellesis使用向导式机器学习来克服这些分割问题。通过用户在简洁图形工作流中提供的训练输入,它可利用30多个不同的参数来评估每个像素,并将其正确归类。该过程重复进行,旨在训练机器学习模型,从而不断提升其性能和精度。随后,用户便可将机器学习自动应用到整个数据集中,将数百幅图像或三维数据集分割成易于分析的格式。借助蔡司ZEN Intellesis,日常显微镜用户无需具备机器学习的专业知识,便能充分利用人工智能的力量。