FB3
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ÁÂÀå : ±è¿øÅÂ(°øÁÖ´ë), ÃÖ¸¸¿ë(Çѱ¹½º¸¶Æ®±¸Á¶¿¬±¸¿ø) 10:50-12:20
FB3-1
10:50-11:20
[ÃÊû] ÇÔÁ¤ÀÇ Àû¿Ü¼± ½ÅÈ£ Ư¼º°ú °¨¼Ò ¹æ¾È
À±¼®ÅÂ(µ¿ÀÇ´ëÇб³)
FB3-2
11:20-11:35
°ú°ËÃâ Á¦¾î¸¦ À§ÇÑ ÄÄÇ»ÅÍ ºñÀü ±â¹Ý ÅëÇÕÇü ÇÁ·¹½º ÆгΠũ·¢ ŽÁö ±â¹ý °³¹ß
·ùÈ£ÁØ, Zhang Penghua, °ø¿¹½½, ¹Ú±ÔÇØ(Àü³²´ëÇб³)
FB3-3
11:35-11:50
¿­ ±ÕÀÏÈ­ ±â¹Ý À¯µµ ¿­È­»óÀ» È°¿ëÇÑ YOLO Deep LearningÀÇ ÀÚµ¿È­ Å©·¢ °ËÃâ Automatic Crack Detection of YOLO Deep Learning using Thermal Equalization-based Inductive Thermography
À̽ÂÁÖ, ±è¿øÅÂ(°øÁÖ´ëÇб³)
FB3-4
11:50-12:05
·ÕÆÞ½º ¿­È­»ó Ž»ó¿¡ ÀÇÇÑ ±Ý¼Ó Ç÷¹ÀÌÆ® ¹è¸é °¨À° Æò°¡ Evaluation for Backside Thinning of Metal Plate by Long Pulse Thermographic Testing
±èÁØ¿µ, ÃÖ°­ÀÏ((ÁÖ)¿¡³×½ºÁö), Á¤À±Àç, ±è¿Áȯ, ±è¿øÅÂ(°øÁÖ´ëÇб³)
FB3-5
12:05-12:20
Infrared Thermography Based Real-Time Melt Pool Monitoring and Control in Metal DED 3D Printing (¿­È­»ó Ä«¸Þ¶ó¸¦ ÀÌ¿ëÇÑ ±Ý¼Ó DED 3D ÇÁ¸°Æà ½Ç½Ã°£ ¿ëÀ¶Ç® ¸ð´ÏÅ͸µ ¹× Á¦¾î)
½Å¼öºó(Çѱ¹°úÇбâ¼ú¿ø), ÀüÀͱÙ(Çѱ¹±â°è¿¬±¸¿ø), ¼ÕÈÆ(Çѱ¹°úÇбâ¼ú¿ø)

(»ç)Çѱ¹ºñÆı«°Ë»çÇÐȸ
´ëÇ¥ÀÚ: Àå°æ¿µ »ç¾÷ÀÚµî·Ï¹øÈ£: 214-82-02092
(06130) ¼­¿ï½Ã °­³²±¸ Å×Çì¶õ·Î7±æ 22 Çѱ¹°úÇбâ¼úȸ°ü 1°ü 903È£
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