Explore the ZEKEEP Machine Vision-Based Intelligent Sorting Training Workstation, featuring a 4-axis robotic arm, industrial camera and vision software for object recognition, positioning, robotic picking and automated sorting training.
Payload
1 kg
Reach
~500mm
Repeatability
±0.05mm
End Speed
1200mm/s
Integrating machine vision, a 4-axis robot and automated sorting for practical intelligent manufacturing education.
The ZEKEEP Machine Vision-Based Intelligent Sorting Training Workstation is an integrated training platform designed for vocational education, intelligent manufacturing, machine vision experiments and robotics skills development.
The workstation combines an integrated 4-axis robotic arm, industrial camera, industrial lighting, adjustable vision bracket, machine vision recognition software and an integrated worktable.
It supports practical training activities involving image acquisition, object recognition, position detection, robotic picking and automated sorting, helping students understand how machine vision and robotic control work together.
By combining visual perception, object positioning and robotic execution on one platform, the workstation supports practical projects in image processing, workpiece recognition, dimensional measurement, vision-guided picking and automated sorting.
Teachers can organize progressive training activities based on robotic control, vision parameter adjustment and sorting logic design, ranging from basic equipment operation to integrated application projects.
The workstation features an integrated worktable equipped with an industrial camera, adjustable camera bracket and industrial lighting. This configuration supports comparative experiments involving different imaging conditions, object recognition methods and robotic picking workflows.
The reference robotic arm configuration provides a rated payload of 1 kg, an approximate working radius of 500 mm and 10 digital inputs (DI) / 10 digital outputs (DO). These specifications support basic robotic control and automation interface training under the corresponding configuration.
The workstation is suitable for secondary vocational schools, higher vocational colleges, applied undergraduate programs, innovation laboratories and related training environments. It provides practical equipment support for courses in machine vision, industrial robotics and intelligent manufacturing, helping students progress from individual operations to integrated vision-based robotic sorting projects.
Connect visual perception with robotic execution in one integrated system.
The workstation integrates a 4-axis robotic arm, industrial camera, industrial lighting, adjustable camera bracket and related software.
It supports training activities involving image acquisition, object recognition, position detection, robotic picking and sorting.
Students can explore the relationship between visual input, recognition results, positioning information and robotic movements on a single platform, making it suitable for structured practical teaching.
From object recognition to position measurement, supporting a range of vision experiments.
The documented vision software functions include image acquisition, object recognition, position detection and dimensional measurement.
Available output data includes coordinate points, angles and classification results.
Depending on the actual software version and configuration, training projects can cover workpiece recognition, position measurement, object classification and vision-guided picking.
Actual recognition performance depends on workpiece characteristics, lighting conditions, camera installation and software configuration.
Combine robot motion control with visual positioning to explore automated operations.
The reference robotic arm configuration features 4 axes, a rated payload of 1 kg, an approximate working radius of 500 mm and repeatability of ±0.05 mm.
Its maximum point-to-point speed is 1200 mm/s, while its maximum linear speed is 800 mm/s.
Students can practice robot motion adjustment, picking position configuration and sorting sequence optimization to understand the fundamentals of robotic task execution.
Modify imaging conditions and compare their effects on recognition performance.
The vision bracket supports height and angle adjustment. The documented lighting options include ring lights and area lights, with a reference lighting configuration of 24V / 15W.
Depending on the selected camera, lens and lighting configuration, students can conduct comparative experiments involving illumination, camera viewing angles and recognition parameters.
These activities help learners understand the relationship between image quality and machine vision performance.
Progress from basic operation to integrated vision-based sorting applications.
Training projects can be organized around recognition conditions, classification rules, picking positions and motion paths.
Within the capabilities of the actual software and equipment configuration, students can adjust vision parameters, modify sorting logic and optimize picking workflows.
Teachers can design basic, intermediate and advanced training tasks according to course requirements, encouraging problem-solving and practical automation skills.
Build practical knowledge step by step, from equipment familiarization to complete sorting projects.
Training content can cover equipment operation, safety procedures, robotic arm control, image acquisition, object recognition, position detection, vision-guided picking and automated sorting.
A progressive training structure helps students understand the relationship between robotics, machine vision and automation control.
A consolidated equipment layout for practical teaching and on-site deployment.
The workstation features an aluminum-profile frame, metal worktable and sheet metal cabinet.
Its reference overall dimensions are approximately 1000 × 700 × 1200 mm, including the camera bracket. The bottom is equipped with swivel casters with brakes.
The integrated layout makes it easier to organize equipment components and practical training activities.
Installation requirements should be confirmed according to the actual delivered dimensions and available site space.
Explore signal interaction between robotic equipment and automation systems.
The reference robotic arm configuration provides 10 digital inputs (DI), 10 digital outputs (DO), and RS232 and RS485 communication interfaces.
Subject to the actual hardware and software configuration, these interfaces can be used for input triggering, output-state control and related automation integration experiments.
Students can learn how signals are exchanged between different automation devices.
It is designed for practical training in machine vision, industrial robotics and intelligent manufacturing.
Typical training activities include image acquisition, object recognition, classification, position detection, dimensional measurement, vision-guided picking and automated sorting.
The workstation uses an integrated 4-axis robotic arm with a reference rated payload of 1 kg.
Higher-payload models may also be available as optional configurations. Final specifications depend on the selected model and confirmed delivery configuration.
The reference working radius is approximately 500 mm, with repeatability of ±0.05 mm.
Actual specifications should be confirmed against the corresponding robotic arm model and official technical documentation.
The documented functions include image acquisition, object recognition, position detection and dimensional measurement.
The vision software can provide coordinate points, angles and classification results. Actual functions depend on the software version and delivered configuration.
The workstation can be used as a training platform for these applications.
Projects can be organized around object recognition, position detection, robotic picking and classification-based sorting.
The actual implementation depends on the vision software, control system and equipment configuration.
The reference overall dimensions are approximately 1000 × 700 × 1200 mm (L × W × H), including the vision bracket.
The worktable dimensions are approximately 1000 × 700 mm.
Additional surrounding space should be reserved for equipment operation and practical training. Final dimensions are subject to the delivered workstation.
Available options, delivery schedules and pricing should be confirmed according to the specific application requirements and proposed configuration.
To better match the workstation with your teaching objectives and practical training requirements, we recommend confirming the following information:
Teaching Audience and Programs: Student levels, academic disciplines and training objectives.
Training Projects: Planned experiments, practical exercises and assessment requirements.
Workpiece Requirements: Dimensions, materials, colors and gripping methods of the training objects.
System Integration: Whether PLCs, sensors or other automation equipment need to be connected.
Robot Specifications: Payload, working radius, vision functions and software accessibility requirements.
Based on the curriculum objectives, training projects and actual workpiece conditions, the robotic arm, industrial camera, lens, lighting and control system can be further evaluated to determine a suitable workstation configuration.
| Parameter | Specification |
|---|---|
| Product Name | Machine Vision-Based Intelligent Sorting Training Workstation |
| Product Model | To Be Confirmed |
| Robot Type | Integrated 4-Axis Robotic Arm with Vacuum Suction Gripper |
| Degrees of Freedom (DOF) | 4 Axes |
| Rated Payload | 1 kg (Higher-payload models available as options) |
| Working Radius | Approx. 500 mm (Larger working radii available as options) |
| Repeatability | ±0.05 mm |
| Maximum Point-to-Point Speed | 1200 mm/s |
| Maximum Linear Speed | 800 mm/s |
| Control System | Integrated Motion Controller and Drive System |
| Operating Voltage | DC 24V |
| Axis 1 (Base) Motion Range | ±160° |
| Axis 2 (Upper Arm) Motion Range | -35° to +140° |
| Axis 3 (Forearm) Motion Range | -20° to +135° |
| Axis 4 (Wrist) Motion Range | ±360° |
| Axis 1 Maximum Speed | 230°/s |
| Axis 2 Maximum Speed | 200°/s |
| Axis 3 Maximum Speed | 200°/s |
| Axis 4 Maximum Speed | 300°/s |
| Digital Inputs (DI) | 10 Channels |
| Digital Outputs (DO) | 10 Channels |
| Communication Interfaces | RS232 / RS485 / I/O |
| Installation | Desktop Installation with Standard Mounting Base |
| End Effector | Vacuum Suction Gripper |
| Suction Cup Configuration | Multiple Suction Cup Options; Replaceable |
| Robot Weight | Approx. 5–6 kg (Including Suction Gripper; Reference Value) |
| Parameter | Specification |
|---|---|
| Camera Type | Industrial Digital Camera |
| Camera Resolution | 2 Megapixels (Higher resolutions available as options) |
| Camera Frame Rate | Up to 60 fps (Reference Configuration) |
| Camera Interface | USB 3.0 |
| Lens Type | Fixed-Focus Lens; Replaceable |
| Camera Mounting Bracket | Adjustable Height and Angle |
| Adjustment Method | Manual Adjustment with Locking Mechanism |
| Lighting Options | Ring Light / Area Light |
| Light Colors | White; Red, Blue, Green and Other Colors Available |
| Light Source Power | 24V / 15W (Higher-power Options Available) |
| Lighting Control | Constant-Current Driver with Adjustable Brightness |
| Vision Software | Machine Vision Recognition Software |
| Main Functions | Image Acquisition, Object Recognition, Positioning and Dimensional Measurement |
| Vision Output Data | Coordinate Points, Angles and Classification Results |
| Vision-to-Robot Communication | TCP / Serial Communication, Depending on Configuration |
| Parameter | Specification |
|---|---|
| Overall Dimensions (L × W × H) | Approx. 1000 × 700 × 1200 mm (Including Camera Bracket) |
| Worktable Dimensions (L × W) | Approx. 1000 × 700 mm |
| Overall Weight | Approx. 60–80 kg |
| Power Supply | AC 220V, 50Hz |
| Total Power Consumption | ≤500W (Reference Value) |
| Low-Voltage Power Supply | DC 24V |
| Mobility | Swivel Casters with Brakes |
| Structural Materials | Aluminum Profiles, Metal Worktable and Sheet Metal Cabinet |
| Operating Temperature | 0–40°C |
| Operating Humidity | 20%–80% RH, Non-Condensing |
| Component | Function |
|---|---|
| Integrated Aluminum-Profile Worktable | Provides space for equipment installation, practical training and component arrangement |
| Sheet Metal Cabinet | Supports the equipment and houses internal components |
| Integrated 4-Axis Robotic Arm | Performs workpiece picking, transfer and sorting operations |
| Vacuum Suction Gripper | Picks up compatible training workpieces and experimental materials |
| Industrial Camera | Captures images for machine vision recognition and positioning |
| Industrial Light Source | Provides controlled illumination to support image acquisition and recognition |
| Adjustable Camera Bracket | Mounts the camera and related vision components, with adjustable height and angle |
| Monitor | Displays software interfaces, captured images and operating information |
| Keyboard and Mouse | Enable software operation and parameter adjustment |
| Control Components | Support equipment operation, status control and related interactions; actual configuration is subject to the delivery specification |
| Swivel Casters | Allow workstation mobility and on-site deployment; equipped with brakes |
Overall Dimensions: Approx. 1000 × 700 × 1200 mm
Worktable Dimensions: Approx. 1000 × 700 mm
Overall Weight: Approx. 60–80 kg
Installation: Integrated workstation deployment with lockable swivel casters
The workstation features an integrated cabinet and training worktable design, bringing together the robotic arm, machine vision components and operating equipment on a single platform. This layout supports deployment in vocational training rooms, innovation laboratories and intelligent manufacturing training environments.
During installation, sufficient space should be reserved for camera bracket height, surrounding operating areas and site access. Final dimensions are subject to the actual delivered equipment.
Suitable for educational programs in industrial robotics, machine vision, mechatronics, intelligent manufacturing and automation.
The workstation can support practical training in equipment operation, robotic arm control, image recognition, workpiece positioning and automated sorting.
Suitable for practical courses covering machine vision, robot motion control, intelligent manufacturing systems and integrated automation.
Students can explore the relationship between visual perception, object recognition, position calculation and robotic execution.
Course suitability should be evaluated according to the institution's curriculum, software functions and training requirements.
By simulating object recognition, positioning, picking and classification workflows, the workstation helps learners understand the operating principles of a typical automated workcell.
It provides a practical foundation for further study of robotic workstation integration, vision inspection and automated control.
Suitable for innovation projects involving machine vision, robotic control and intelligent sorting.
Students can conduct practical projects involving object feature recognition, sorting rule design and robotic picking path optimization.
Can be used for skills training involving robot control, machine vision recognition, workpiece positioning and automated sorting.
The workstation provides a platform for practical coursework, project assessments and competition preparation.
Suitability for specific competitions should be confirmed against the equipment requirements, technical rules and task specifications of each event.
This workstation is primarily designed as a machine vision and robotic sorting training platform. For actual industrial production, additional evaluation of cycle time, continuous operation capability, safety protection and production line integration is required.
Learn the movement of each robotic axis, point setting, motion sequence adjustment and trajectory debugging. Understand the basic methods of programming robotic picking and placement operations.
Use an industrial camera to capture images of the experimental area. Adjust camera positioning, lens settings and lighting conditions to observe how different acquisition conditions affect image quality.
Use machine vision software to recognize and classify training workpieces. Analyze the relationship between object features and recognition results to establish the foundation for automated sorting.
Practice identifying and measuring workpiece positions, angles and dimensions. Understand how visual measurement results contribute to robotic picking tasks.
Use position information generated by the vision system to guide robotic picking operations. Explore the coordination between image acquisition, object positioning, coordinate transmission and robot motion.
Configure classification conditions, sorting rules and destination positions based on object recognition results. Develop and debug a complete sorting workflow from object identification to robotic execution.
Modify lighting intensity, light source types and camera viewing angles to observe changes in visual recognition performance. Explore how imaging conditions affect object recognition and positioning.
Use the robot's DI/DO digital interfaces to conduct input-triggering and output-state control experiments. Actual connected devices and wiring configurations should be designed according to the hardware and control system.
Combine image acquisition, object recognition, position detection, robotic control and sorting rule design to complete an integrated automated sorting training task.
Projects can be adapted to different student skill levels and assessment requirements.
Leave your contact information and requirements. Based on the product specifications and application scenarios, we will provide preliminary selection and solution advice.
