Process Overview
This palletizing automation solution uses the Hyundai Robotics HH050 and Mech-Mind 3D vision to detect mixed-size boxes, align them by size, and automatically stack them onto pallets. By leveraging a cost-effective 3D vision system, the cell can reliably palletize even when cartons of varying dimensions are mixed in the flow, ensuring stable operation.
๊ตฌ์ฑ์์
| ๋ก๋ด | Mech-Mind Mech-Eye Laser L (Industrial 3D camera) FOV: 1250 × 1460 @ 1.5 m; 2500 × 2890 @ 3 m Resolution: 2048 × 1536 (3 MP) Accuracy: 1.0 mm @ 2 m Scan time: 0.9–1.3 s Recommended scanning distance: 1.5–3 m Mech-Viz Intelligent Programming Environment: robot programming tool Hyundai Robotics HDR50-22 (HH050) Repeatability: ±0.06 mm |
|---|---|
| ์ฃผ๋ณ๊ธฐ๊ธฐ | Tooling Vacuum gripper 3D Vision Mech-Eye Laser L (FOV: 1250 × 1460 @ 1.5 m; 2500 × 2890 @ 3 m, Resolution: 2048 × 1536 (3 MP), Accuracy: 1.0 mm @ 2 m, Scan time: 0.9–1.3 s, Recommended scanning distance: 1.5–3 m, Mech-Viz Intelligent Programming Environment: robot programming tool) Peripheral Equipment Robot base Vision mounting stand Box infeed stand Pallet fixing guide |
์์ ์์
| STEP 1. | Scan available pallet stacking space using 3D vision |
|---|---|
| STEP 2. | Pick each box and place it into the remaining pallet space based on its size |
| STEP 3. | Repeat until palletizing is complete |
โป ๋ง๋ก์ ๋ด์ ๋ชจ๋ ์ฝํ ์ธ ๋ฅผ ๋ฌด๋จ์ผ๋ก ๋ณต์ฌ ๋ฐ ์ฌ์ฐฝ์ํ ๊ฒฝ์ฐ ๋ถ์ ๊ฒฝ์๋ฐฉ์ง๋ฒ ๋ฐ ์ ์๊ถ๋ฒ์ ์๋ฐ๋ ์ ์์์ ๋ฐํ๋๋ค.
์ถ์ฒ ๋์ ์ฌ๋ก
ARC Welding Using the Rainbow Robotics RB-5
Process Overview This welding automation solution uses the Rainbow Robotics RB-5 and a NOUBELON DIGITAL MIG/MAG welder to weld cylindrical workpieces onto a plate, featuring fast teaching and high space efficiency. In manual welding, quality can vary significantly depending on operator skill, and the harsh working environment makes it difficult to secure experienced welders. With this cell, even non-expert operators can simply load and unload parts, while the robot performs the critical welding processโenabling consistent weld quality and stable production output.
Indoor Logistics Transport and Loading Using VisionNav VNPA15๐๏ธ
Process Overview This case study showcases a logistics automation solution that uses VisionNav's VNPA15 autonomous mobile robot (AMR) to automate the transport and loading/stacking of packaged products in indoor material-handling operations. Built on robust safety functions, the solution minimizes changes to existing workflows while enabling unattended load/unload tasks, and provides effective monitoring and control of real-time operations.
We successfully automated raw-material transport with OMRON AMRs! ๐
Process Overview This case study features a large, cylindrical raw-material transport solution using OMRON AMRs. Previously, raw-material transport was handled manually, with operators physically rolling large cylindrical containers to move them. Due to operator fatigue and low handling efficiency in this workflow, an automation solution became necessary. In this project, OMRON AMRs were introduced to automate raw-material transport, with the goal of reducing operator burden and significantly improving material-handling efficiency. Project Background and Objectives When raw materials arrived inbound in large containers, the manual transport process required high physical effort and carried a risk of injury. In addition, time loss and workload imbalance led to reduced productivity. The objective of this project is to address these issues by deploying an AMR-based automation solution and realizing more efficient internal logistics operations.
Process Overview
This palletizing automation solution uses the Hyundai Robotics HH050 and Mech-Mind 3D vision to detect mixed-size boxes, align them by size, and automatically stack them onto pallets. By leveraging a cost-effective 3D vision system, the cell can reliably palletize even when cartons of varying dimensions are mixed in the flow, ensuring stable operation.
๊ตฌ์ฑ์์
| ๋ก๋ด | Mech-Mind Mech-Eye Laser L (Industrial 3D camera) FOV: 1250 × 1460 @ 1.5 m; 2500 × 2890 @ 3 m Resolution: 2048 × 1536 (3 MP) Accuracy: 1.0 mm @ 2 m Scan time: 0.9–1.3 s Recommended scanning distance: 1.5–3 m Mech-Viz Intelligent Programming Environment: robot programming tool Hyundai Robotics HDR50-22 (HH050) Repeatability: ±0.06 mm |
|---|---|
| ์ฃผ๋ณ๊ธฐ๊ธฐ | Tooling Vacuum gripper 3D Vision Mech-Eye Laser L (FOV: 1250 × 1460 @ 1.5 m; 2500 × 2890 @ 3 m, Resolution: 2048 × 1536 (3 MP), Accuracy: 1.0 mm @ 2 m, Scan time: 0.9–1.3 s, Recommended scanning distance: 1.5–3 m, Mech-Viz Intelligent Programming Environment: robot programming tool) Peripheral Equipment Robot base Vision mounting stand Box infeed stand Pallet fixing guide |
์์ ์์
| STEP 1. | Scan available pallet stacking space using 3D vision |
|---|---|
| STEP 2. | Pick each box and place it into the remaining pallet space based on its size |
| STEP 3. | Repeat until palletizing is complete |
โป ๋ง๋ก์ ๋ด์ ๋ชจ๋ ์ฝํ ์ธ ๋ฅผ ๋ฌด๋จ์ผ๋ก ๋ณต์ฌ ๋ฐ ์ฌ์ฐฝ์ํ ๊ฒฝ์ฐ ๋ถ์ ๊ฒฝ์๋ฐฉ์ง๋ฒ ๋ฐ ์ ์๊ถ๋ฒ์ ์๋ฐ๋ ์ ์์์ ๋ฐํ๋๋ค.











