Bosch

Support its workers and to reduce the error rate

CHALLENGE

Robert Bosch España Fábrica Madrid S.A. is the mass-scale manufacturer for automotive sensors, which process is fully automated and is running 24/7 non-stop. However, the plant is also manufacturing Electronic Control Units, where small series of spare parts are supplied to car makers and aftermarket. Since the batches are small and it only requires a few hours in one month, even in some cases, in one year, hence it is neither possible

nor economically viable to automate the production line for those automotive spare parts. Although they are small in quantity, they are complex in reference designs that usually BOSCH need to produce more than 1500 different reference designs. Therefore, BOSCH have workstations target for such small-scale production in the shopfloor supervised by experienced workers. It’s not hard to understand such production is prone to errors.

USE CASE

BOSCH expects to support collaboration and monitor the process, speed up task allocation, exception handling, reduce labour effort (lifting and transferring products) and quality checking operations, thus changing this area into a truly collaborative workspace.

This will be achieved by redesigning and optimising production process, to take full advantage of several new technologies – end-to-end production monitoring and execution including:

collaborative robots capable of handling and reliably manipulating Printed Circuit Boards (PCBs) and boxes containing electronic components;

AGVs capable of transferring components and processed goods between the working cells without interrupting the workers’ tasks;

Augmented Reality to support non-expert workers in proper placement of the electronic components over the PCB;

Additional technologies like RFID or computer vision to facilitate tracking and quality assurance integration of this production process with the existing management software, including SAP used in the plant.

Currently the worker has to read carefully the instructions and understand the whole assembly sequence and actions required. Then they have to perform the assembly sequence keeping in line with the instructions. After the assembly is completed, the worker has to manually check if the assembled PCB corresponds to the result on the instructions.

Additionally, the transport of finished PCBs (depending on the final product to be manufactured) has to be done between floors. This introduces a big amount of non-added value worker time for performing manually these transfers. Additionally, in crisis situation, like the COVID pandemic, the personnel is reduced, and it needs to even more focus on added value activities instead of transporting items.

The objective of the SHOP4CF pilot is to release the worker from the cognitive load, and ensure the correctness and quality of the assembled PCB, as well as take over the transporting of assembled PCBs.

AR support for assembling PCBs

SHOP4CF scope:

Show instructions for the human worker with AR
Check that the components assembled are the correct ones
Check that the components were assembled on the correct position

Transfer of PCB trays between the lines & ‘supermarkets’

SHOP4CF scope:

Load trays on AGV
AGV transfer in collaborative space
Control of elevator
Tracking of PCB trays

CHALLENGE

Pilot 2: Multiple-line material loading by shared autonomous mobile robot

USE CASE

The 2nd pilot at Bosch is focused on an production with similar parallel automated production lines. These lines produce similar, but not identical products. The lines are not utilized 24/7, but their utilization time depends on the orders and the type of products that are required. Each line is fed with trays of parts, which, again, are similar but not identical. As the utilization rate of these lines is random, it is difficult to schedule specific resources for taking over the loading task for these lines. As a result, the feeding task is performed by workers, whose main responsibility is another production task.

The objective of the SHOP4CF pilot is to use 1 robotic arm on top of an automated mobile platform (AGV), which will be efficiently shared across these production lines.

Multiple-line material loading by shared autonomous mobile robot

SHOP4CF scope:

Allocation of loading tasks to shared robot and workers
Safe AGV navigation in area shared with human workers
Localization of the current AGV position

IMPACT

The automated process model will ensure traceability and support timely allocation of tasks and shorter production cycles. The AGVs improves the overall productivity of the line and reduced workers burden of repetitive and arduous work.

In the first stage of piloting the new lines will be tested outside of the manufacturing line. Afterwards, the newly developed stations will be integrated within the existing manufacturing process.The main results will be demonstrated in core Bosch management level and employees

SHOP4CF 2nd open call for Industry 4.0 innovators

SHOP4CF launches it’s first open call for innovative manufacturing projects

The SHOP4CF Ambassador Programme

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Bosch

CHALLENGE

USE CASE

CHALLENGE

Pilot 2: Multiple-line material loading by shared autonomous mobile robot

USE CASE

IMPACT

ROS2 Monitoring Tool

M2O2P: Multi-Modal Online and Offline Programming solution

VR-RM-MT: Virtual reality set for robot and machine monitoring and training

DT-CP: Digital Twin – for planning and control

DCF: Data Collection Framework

ADIN: Adaptive Interfaces

Shakeit: Workcell Process Optimization based on Reinforcement Learning

FBAS-ML: Force-Based Assembly Strategies for Difficult Snap-Fit Parts Using Machine Learning

DTS: Dynamic Task Scheduling for Efficient Human Robot Collaboration

HA-MRN: Human Aware Mobile Robot Navigation in Large Scale Dynamic Environments

FTPT: Flexible Task Programming Tool

ASA: Automated Safety Approval

RA: Review of Risk Analysis

FLINT

OpenWIFI - Open-source implementation of 802.11 WIFI on FPGA

Wi-POS Indoor Localization

DYAMAND

PMADAI - Predictive Maintenance and Anomaly Detection in Automotive Industry

VQC - Visual Quality Check

Digital Twin for Intralogistics

AR Manual Editor

AR-based Teleassistance

VR Creator

MPMS - Manufacturing Process Management System

AR-CVI - AR for Collaborative Visual Inspection/AR for Task Instructions

WoT-IL - Interoperability Layer through Web of Things

Be the first one to know

SHOP4CF 2nd open call for Industry 4.0 innovators

SHOP4CF launches it’s first open call for innovative manufacturing projects

The SHOP4CF Ambassador Programme

Follow Us

Bosch

CHALLENGE

USE CASE

CHALLENGE

Pilot 2: Multiple-line material loading by shared autonomous mobile robot

USE CASE

IMPACT

ROS2 Monitoring Tool​

M2O2P: Multi-Modal Online and Offline Programming solution

VR-RM-MT: Virtual reality set for robot and machine monitoring and training

DT-CP: Digital Twin – for planning and control

DCF: Data Collection Framework

ADIN: Adaptive Interfaces

Shakeit: Workcell Process Optimization based on Reinforcement Learning

FBAS-ML: Force-Based Assembly Strategies for Difficult Snap-Fit Parts Using Machine Learning

DTS: Dynamic Task Scheduling for Efficient Human Robot Collaboration

HA-MRN: Human Aware Mobile Robot Navigation in Large Scale Dynamic Environments

FTPT: Flexible Task Programming Tool

ASA: Automated Safety Approval

RA: Review of Risk Analysis

FLINT

OpenWIFI - Open-source implementation of 802.11 WIFI on FPGA

Wi-POS Indoor Localization

DYAMAND

PMADAI - Predictive Maintenance and Anomaly Detection in Automotive Industry

VQC - Visual Quality Check

Digital Twin for Intralogistics

AR Manual Editor

AR-based Teleassistance

VR Creator

MPMS - Manufacturing Process Management System

AR-CVI - AR for Collaborative Visual Inspection/AR for Task Instructions

WoT-IL - Interoperability Layer through Web of Things

Be the first one to know

ROS2 Monitoring Tool