SIASUN New Energy Vehicle Trunk Battery Pack Tightening (GCR20)

The application sits at the intersection of EV battery-pack manufacturing, torque-controlled fastening, and human–robot collaborative automation, where consistent tightening quality and production traceability are high-priority requirements.

In this scenario, the cobot typically carries an electric screwdriver/nutrunner and uses machine vision (including 3D vision) to locate fasteners, compensate for positioning variation, and execute tightening cycles while capturing process data for quality control and manufacturing execution systems (MES). SIASUN’s DUCO application description emphasizes challenges such as long reach, difficult positioning, tight workspace constraints, and the need for tightening traceability—conditions common in EV assembly lines where battery packs are integrated into vehicle bodies.

Design and Features

Collaborative robot-based tightening cell

A trunk-area battery pack tightening task is often difficult to automate with conventional fixed automation because the joint locations can be deep inside the vehicle body, with limited access and variable tolerances between vehicles. SIASUN’s application description frames the cobot workstation as a flexible alternative that can approach multiple tightening points and adapt to slight changes in pose.

Vision-guided “secondary positioning”

A key feature highlighted in SIASUN’s DUCO application write-up is the use of a 3D camera for “secondary positioning” (i.e., a vision-based refinement step) before tightening. This approach is commonly used in robotic fastening to reduce the risk of cross-threading, missed holes, or tool misalignment when the robot’s nominal program position is not sufficient. In the SIASUN description, the 3D camera + cobot pairing is presented as the solution to “difficult positioning” in trunk battery pack tightening.

Tightening tool integration and data connectivity

The described configuration uses an electric screwdriver integrated with the robot and supported by visual communication with a host computer, plus the ability to upload process data (supporting quality records and traceability). This aligns with broader industry practice in torque-controlled assembly, where fastening tools store or transmit tightening results (torque/angle/status) for quality assurance; modern tightening tools emphasize memory and traceability for quality control.

Technology and Specifications

Collaborative robot platform (GCR20 family)

In SIASUN’s DUCO lineup, GCR20 commonly denotes a 20 kg payload-class collaborative robot, often paired with a mid-to-long reach configuration (frequently referenced as 1.4 m reach in product listings). The GCR20-1400 product description lists a 20 kg rated payload, 1400 mm reach, and repeatability in the ±0.05 mm class, positioning it for tool-carrying operations like screwdriving and handling.

Note: Exact workstation performance depends on tool mass, bit/socket design, joint access geometry, and tightening strategy (torque control vs. torque+angle), not just robot payload.

Core automation components typically involved

While implementations vary, a trunk battery pack tightening cell built around GCR20 commonly includes:

• Robot + cobot controller (motion control, safety functions, I/O integration)

• End-of-arm tightening tool (electric screwdriver/nutrunner, reaction torque management)

• 3D vision sensor for locating fasteners and refining pose (“secondary positioning”)

• Workstation software / host PC for recipe selection, monitoring, and “visual communication”

• Process-data logging for traceability and quality control

Safety and collaborative operation context

Collaborative robot deployments are typically designed around internationally recognized safety concepts that can include safety-rated monitored stop, hand guiding, speed and separation monitoring, and power and force limiting—modes widely discussed in relation to ISO 15066 collaborative operations.
For tightening work, integrators also evaluate hazards introduced by the end effector/tooling, workpiece geometry, and pinch points, applying risk assessment and safeguards accordingly.

Applications and Use Cases

EV trunk-area battery pack fastening

The primary use case is tightening fasteners on a battery pack or battery-related structure located in the trunk/rear of an NEV. SIASUN’s application note highlights issues of long reach, tight space, and high labor intensity, positioning the cobot workstation as a way to reduce manual burden while improving consistency.

Related EV and automotive fastening tasks

Although the SIASUN description focuses on trunk battery pack tightening, the same architecture (cobot + vision + nutrunner + traceability) is commonly applied to:

• Underbody battery pack enclosure fastening

• Battery module or pack bracket tightening

• Rear structure, reinforcement, or mounting-point tightening

• Mixed-model assembly lines, where quick changeover is required

Quality-driven tightening with traceability

Battery pack fastening is typically quality-critical because loosening, mis-torque, or missing fasteners can lead to noise/vibration issues, sealing problems, or structural concerns. As a result, tightening systems often prioritize traceability—capturing tightening outcomes for each joint. The broader tightening-tool market emphasizes memory and traceability features for quality control, matching the SIASUN description of process data uploading.

Advantages / Benefits

Consistency and repeatable tightening quality

Robotic tightening can reduce variability associated with manual posture constraints and tool alignment challenges in tight trunk spaces, especially when supported by vision-based pose refinement.

Flexibility versus hard automation

Compared with fixed jigs or dedicated multi-spindle machines, a cobot-based workstation can be more adaptable to changes in vehicle variants, fastener locations, or production volumes—particularly when the robot is programmed for multiple tightening points and recipes.

Improved ergonomics and reduced labor intensity

SIASUN explicitly describes the trunk battery pack tightening task as labor-intensive and difficult to position manually, and frames the cobot workstation as a remedy.

Data capture for manufacturing quality systems

With process data uploading and host-computer integration noted in the SIASUN description, the workstation supports auditability and quality management workflows.

FAQ Section 

What is SIASUN New Energy Vehicle trunk battery pack tightening (GCR20)?

It is a cobot-based EV assembly workstation concept that uses a SIASUN DUCO GCR20 collaborative robot with an electric screwdriver and 3D vision-based secondary positioning to tighten battery pack fasteners in a vehicle trunk area, with process data upload for traceability.

How does SIASUN trunk battery pack tightening work?

A GCR20 cobot moves to each fastener location, uses a 3D camera for secondary positioning, aligns the tool to the joint, performs the tightening cycle with an electric screwdriver/nutrunner, and uploads tightening/process data via a host-computer interface for quality tracking.

Why is trunk battery pack tightening important in EV manufacturing?

Battery pack fastening is quality-critical: incorrect torque, missing fasteners, or poor alignment can create reliability and safety risks. EV assembly commonly relies on tightening systems with traceability to ensure every joint meets specification and is recorded for quality control.

What are the benefits of using a GCR20 collaborative robot for tightening?

Key benefits include improved reach and positioning for tight trunk spaces, reduced manual labor intensity, repeatable tightening assisted by vision-based alignment, and process data capture for traceability and quality control.

Summary

SIASUN’s NEV trunk battery pack tightening (GCR20) concept represents a modern EV assembly approach that combines a 20 kg-class collaborative robot, 3D vision-based secondary positioning, and torque-controlled tightening with process data upload for traceability. By addressing the trunk area’s long reach, tight access, and positioning difficulty, the workstation aims to improve fastening consistency, reduce labor intensity, and support quality systems—key goals in scalable electric vehicle manufacturing.