Improving transferability between different engineering stages in the development of automated material flow modules

For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from the electrical, mechanical or software engineering, based on a modular architecture. Considering the development of these modules, there are different stages, e.g. module planning or functional engineering, which have to be completed. A reference model that addresses the different stages for the engineering of aPS is proposed by AutomationML. Due to these different stages and the integration of several engineering disciplines, e.g. mechanical, electrical/electronic or software engineering, information not limited to one discipline are stored redundantly increasing the effort to transfer information and the risk of inconsistency. Although, data formats for the storage and exchange of plant engineering information exist, e.g. AutomationML, fixed domain specific structures and relations of the information, e.g. for automated material flow systems (aMFS), are missing. This paper presents the integration of a meta model into the development of modules for aMFS to improve the transferability and consistency of information between the different engineering stages and the increasing level of detail from the coarse-grained plant planning to the fine-grained functional engineering.Comment: 11 pages, https://ieeexplore.ieee.org/abstract/document/7499821

Novel Approach using Risk Analysis Component to Continuously Update Collaborative Robotics Applications in the Smart, Connected Factory Model

Building on the idea of Industry 4.0, new models of the highly connected factory that leverage factory‐generated data to introduce cost‐effective automation and involve the human worker for creating higher added value are possible. Within this context, collaborative robots are becoming more common in industry. However, promises regarding flexibility cannot be satisfied due to the challenging process of ensuring human safety. This is because current regulations and standards require updates to the risk assessment for every change to the robotic application, including the parts involved, the robotic components, and the type of interaction within the workspace. This work presents a novel risk analysis software tool that was developed to support change management for adaptive collaborative robotic systems in the connected factory model. The main innovation of this work is the tool’s ability to automatically identify where changes have been made to components or processes within a specific application through its integration with a connected factory architecture. This allows a safety expert to easily see where updates to the risk assessment are required, helping them to maintain conformity with the CE marking process despite frequent changes. To evaluate the benefits of this tool, a user study was performed with an exemplary use-case from the SHOP4CF project. The results show that this newly developed technology for risk assessment has better usability and lower omission errors when compared to existing methods. Therefore, this study underlines the need for tools that can help safety engineers cope with changes in flexible robotics applications and reduce omission errors.publishedVersionPeer reviewe

Efficient Messaging through Cluster Coordinators in Decentralized Controlled Material Flow Systems

The modularization of the hard- and software is one approach handling the demand for increasing flexibility and changeability of automated material flow systems. A control that is distributed across several different hardware controllers leads to a great demand for coordination between the modules while planning for example transports, especially if there is a mutual dependency between the modules on the executing tasks. Short-term changes in planning often initiate a rescheduling chain reaction, which causes a high communication load in the system. In the presented approach, module clusters with a centralized coordinator are automatically formed out of multiple modules and substitutional take over the surrounding communication for the modules. As a result, they minimize exchanged messages by focusing on the essential information

Efficient Messaging through Cluster Coordinators in Decentralized Controlled Material Flow Systems

The modularization of the hard- and software is one approach handling the demand for increasing flexibility and changeability of automated material flow systems. A control that is distributed across several different hardware controllers leads to a great demand for coordination between the modules while planning for example transports, especially if there is a mutual dependency between the modules on the executing tasks. Short-term changes in planning often initiate a rescheduling chain reaction, which causes a high communication load in the system. In the presented approach, module clusters with a centralized coordinator are automatically formed out of multiple modules and substitutional take over the surrounding communication for the modules. As a result, they minimize exchanged messages by focusing on the essential information

In situ measurement and closed-loop control for powder supply processes

The powder mass flow rate is one of the main parameters regarding the geometrical precision of built components in the additive manufacturing process of laser metal deposition. However, its accuracy, constancy, and repeatability over the course of the running process is not given. Reasons among others are the performance of the powder conveyors, the complex nature of the powder behavior, and the resulting issues with existing closed-loop control approaches. Additionally, a direct in situ measurement of the powder mass flow rate is only possible with intrusive methods. This publication introduces a novel approach to measure the current powder mass flow rate at a frequency of 125 Hz. The volumetric powder flow evaluation given by a simple optical sensor concept was transferred to a mass flow rate through mathematical dependencies. They were found experimentally for a nickel-based powder (Inconel 625) and are valid for a wide range of mass flow rates. With this, the dynamic behavior of a vibration powder feeder was investigated and a memory effect dependent on previous powder feeder speeds was discovered. Next, a closed-loop control with the received sensor signal was implemented. The concept as a whole gives a repeatable and accurate powder mass flow rate while being universally retrofittable and applicable. In a final step, the improved dynamic and steady performance of the powder mass flow rate with closed-loop control was validated. It showed a reduction of mean relative errors for step responses of up to 81% compared to the uncontrolled cases.TU Berlin, Open-Access-Mittel – 202

Towards Material-Batch-Aware Tool Condition Monitoring

In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework

Towards Material-Batch-Aware Tool Condition Monitoring

In subtractive manufacturing, process monitoring systems are used to observe the manufacturing process, to predict maintenance actions and to suggest process optimizations. One challenge, however, is that the observable signals are influenced not only by the degradation of the cutting tool, but also by deviations in machinability among material batches. Thus it is necessary to first predict the respective material batch before making maintenance decisions. In this study, an approach is shown for batch-aware tool condition monitoring using feature extraction and unsupervised learning to analyze high-frequency control data in order to detect clusters of materials with different machinability, and subsequently optimize the respective manufacturing process. This approach is validated using cutting experiments and implemented as an edge framework

A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

The presence of Deformable Linear Objects (DLOs) such as wires, cables or ropes in our everyday life is massive. However, the applicability of robotic solutions to DLOs is still marginal due to the many challenges involved in their perception. In this letter, a methodology to generate datasets from a mixture of synthetic and real samples for the training of DLOs segmentation approaches is thus presented. The method is composed of two steps. First, key-points along a real-world DLO are labeled by employing a VR tracker operated by a user. Second, synthetic and real-world datasets are mixed for the training of semantic and instance segmentation deep learning algorithms to study the benefit of real-world data in DLOs segmentation. To validate this method a user study and a parameter study are conducted. The results show that the VR tracker labeling is usable as other labeling techniques but reduces the number of clicks. Moreover, mixing real-world and synthetic DLOs data can improve the IoU score of a semantic segmentation algorithm by circa 5%. Therefore, this work demonstrates that labeling real-world data via a VR tracker can be done quickly and, if the real-world data are mixed with synthetic data, the performances of segmentation algorithms for DLOs can be improved