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10 reasons why MaxQ is ideal for academic research

With its state of the art multi-sensor sensor system, data analysis tools and easily integratable architecture, RAMLAB’s MaxQ could be your solution for welding and additive manufacturing R&D. Developed fully in-house and tested for internal R&D, we have developed an easy-to-use monitoring system that is ideal for academic research in metal arc welding and wire based additive manufacturing. In this article, we outline 10 reasons why RAMLAB’s MaxQ is ideal for academic research:

1. MaxQ is compatible with a range of weld sources and robots

MaxQ can be integrated with a variety of different power sources and welder modes. It has been tested with Panasonic (figure 1), Miller, Fronius and Yaskawa systems and works in a variety of welder modes like Cold Metal Transfer (CMT), Super Active Wire Transfer (SAWP), MIG/MAG welding and Pulsed welding conditions. The sensor suite can also be customised based on the desired application.

Figure 1: MaxQ Robot

2. MaxQ offers multi-sensor monitoring for WAAM

MaxQ is integrated with a high frequency current and voltage sensor and can be supplemented with a microphone, optical emission spectrometer and a welding camera that collects data from the metal deposition process. It is additionally integrated with a Zivid 3D scanning camera for geometry control as well as an Optis Xi 80 infrared camera for interpass temperature control. The advantage of using multiple sensors (figure 2) is to get a deeper understanding about the mechanism of metal transfer and to collect data that can be independently correlated. 

Figure 2: Multi-sensor monitoring of WAAM

3. MaxQ offers anomaly Detection for WAAM

Statistical features are calculated on the multi-sensor data streams that can be correlated with process anomalies and defects. These sensors detect a physical parameter from the deposition process and the data can be used to calculate statistical features that can be correlated with processing conditions. A combination of features from the audio, U/I and optical emission spectroscopy data can be used to identify commonly occurring welding defects. More information about the anomaly detection system can be found here.

Figure 3: Multi-sensor dashboard for feature visualisation

4. Data processing functions are customizable

The data from the multi-sensor system can be processed in customizable functions to study the WAAM/GMAW process in greater detail. These sensors give insight into the metal transfer mechanism, temperature of the plasma arc, heat input, and welding defects. The ability to customise the data processing functions is useful to discover new correlations between process parameters (figure 4), weld geometry, microstructural features and anomalies. 

Figure 4: Casing with Voltage sensor

5. All data is timestamped and retrievablie

The data streams from the multi-sensor system are synchronised with time-stamps and saved in a backend database server together with data from the welding robot such as position coordinates, welding parameters and program details. These data streams can be processed in real-time and displayed in a dashboard (figure 5) to identify useful features and can be customised to calculate any user defined functions. 

Figure 5: Minimized average delay between different sensor data streams to 6 ms → less than the time taken for 1 pulse/short circuit to occur

6. Data can be customized and visualized in real-time

The time-stamped sensor data and features can be visualised in real-time by means of a customizable Grafana dashboard. The dashboard can be used to visualise the robot coordinates and process parameters and also to visualise the variation of individual features with position. Additionally, alerts can be set on the sensor data when it satisfies a user-defined condition. 





Figure 6: Data visualisation (a) of porosity (b) caused by spatter (c) in the gas cap visualised by tracking short circuit fraction from MaxQ sensor data stream and superimposed on the toolpath (d)

7. MaxQ enables automated WAAM

MaxQ monitoring and control offers the capability of not only maintaining interpass temperature conditions but also offers other features that enable the WAAM printing jobs to be done in an automated production environment. Temperature control as one of the features in the MaxQ system, which is currently equipped with an Optris XI 80 infrared thermal imaging camera. It is integrated with the welding system and ensures that the component reaches the set temperature boundaries, thus minimising the probability of geometric anomalies and defects.

Figure 7: interpass temperature control during WAAM

8. MaxQ offers the potential for feedback control

MaxQ collects a large volume of data from the different sensors that actively monitor the processing conditions during the WAAM process. The long term goal for the development of this monitoring system is to integrate with a feedback-based control system with the ability to self-correct the printing process. By studying the correlation between the process parameters and weld geometry (figure 8), the sensor data stream can be used to build a feedback system that controls the process parameters to maintain a predetermined weld geometry. MaxQ offers users the capability to design and test these self-correcting feedback based monitoring and control systems.

Figure 8: 3D scan of the geometry by the Zivid 2 scanner 

9. MaxQ Robot is the turn-key WAAM system for students

MaxQ Robot was designed to be a plug-and-play system that can easily be set up for experimentation and data collection (figure 9). RAMLAB conducts a thorough verification test before shipping to ensure the hardware and software system are seamlessly integrated and ready to use. It is also safe and easy to use for students to conduct welding experiments, set up the data acquisition and visualise real-time data. 

Figure 9: MaxQ Robot by RAMLAB in cooperation with Valk Welding

10. Work with the RAMLAB ecosystem

RAMLAB has built up a consortium of stakeholders that includes suppliers, manufacturers and research institutions in the welding and additive manufacturing industry. Through this consortium and internal R&D, RAMLAB has developed a repository of knowledge in metal additive manufacturing, monitoring and control and toolpath design. Collaboration with academic institutions and manufacturing units gives you a unique opportunity to be at the forefront of additive manufacturing research and its application in industry. 

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