Name: data communication based on mqtt in a polymer extrusion process
Uploaded: 2022-07-15T13:00:00.000+00:00
Duration: 8 min 15 s
Description: Watch this Scientific Journal Video about Data Communication Based on MQTT in a Polymer Extrusion Process at JoVE.com

This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).

1. Broker installation
NOTE: To monitor and record processing data via the Internet, a computer system that relays the data should be prepared. The system should be accessible from both the publishers and the subscribers as shown in Figure 2. Thus, it needs to have a public IP address that is known prior to any communication. An open MQTT broker called Eclipse Mosquitto is installed on the system13.
<ol>
	<li>Connect a computer to the Internet giving a public IP address. Give the address in the IP setting of the operating system.</li>
	<li>Install a broker software such as Eclipse Mosquitto on the computer. Download the installation file using a browser and execute it.</li>
	<li>Test the broker with a test program such as MQTT Lens. Download MQTT Lens using a browser and install it. Then, ensure published messages are subscribed.</li>
</ol>
2. Main publisher preparation
NOTE: This computer publishes the machine data via MQTT over TCP to the broker. Legacy data should be interpreted and repackaged to be sent out.This can be usually done by RS485 or Ethernet. The connection at the hardware level should be verified depending on the bus type. The extrusion machine sends out the data via Modbus through an Ethernet port.
<ol>
	<li>Physically place a computer in the machine site and set it up as the main publisher. 
	NOTE: Although not obligatory, an industrial computer was selected in this work.</li>
	<li>Install Python3 on the computer. Download the installer file using a browser and execute it.</li>
	<li>Install PyModbus16. Download the installer file using a browser and execute it.</li>
	<li>Examine the extrusion controller with HMI controlling the machine and connect the extrusion controller to the main publisher.</li>
	<li>Fully identify the data and the corresponding address in the Modbus protocol from the machine using a Modbus tool such as ModbusPoll or QModMaster. Ensure the sent machine data are shown in the corresponding cells of the Modbus tool.</li>
	<li>Write a Python code on the publisher PC that retrieves the data from the extrusion controller. 
	NOTE: Here is a code example: 
	from pyModbusTCP.client import ModbusClient 
	client = ModbusClient(host=&#34;192.168.1.***&#34;, port=***, unit_id=***) 
	client.open() 
	ExtrusionData = str(client.read_holding_registers(1000, 58))</li>
	<li>Merge additional data streams from other devices via PCIe, USB, RS232, and RS485. 
	NOTE: This is straightforward. Once an additional data string is obtained, simply add the data to the existing data stream, which is done by the following code: 
	ExtrusionData += AdditionalData</li>
	<li>Import the paho.mqtt.client after installing paho-mqtt by pip install paho-mqtt17.</li>
	<li>Implement the code to connect and publish data to the broker. 
	NOTE: Refer to the following code example: 
	url=&#34;117.xx.xxx.xx&#34;; port = 1883; username = &#34;****&#34;; password = &#34;xxxxx&#34;; topic = &#34;Extruder&#34; 
	mqttc = mqtt.Client() 
	mqttc.username_pw_set(username,password) 
	mqttc.connect(host=url, port=port) 
	mqttc.loop_start() 
	ExtrusionData = str(client.read_holding_registers(1000, 58)) 
	Pub1= mqttc.publish(topic, ExtrusionData)) 
	Pub1.wait_for_publish()</li>
</ol>
3. Additional publisher preparation
NOTE: This computer also publishes the machine data via MQTT over TCP to the broker. Sometimes, additional measurement that cannot be done on the main publisher is required. Internet of Things (IoT) devices such as Raspberry Pi and Arduino can take the role. In this work, Raspberry Pi was employed for temperature data and illuminance data. The procedure is similar to protocol section 2.
<ol>
	<li>Place a Raspberry Pi near the sensor location. 
	NOTE: Since the wiring distance is limited, the Raspberry Pi cannot be placed very far from the measurement location. However, as the vicinity of the extruder is very hot, the device needs to be placed at least 1 m away from the measurement location.</li>
	<li>Install Python3 on the Raspberry Pi by the following commands in the command line: 
	sudo apt update 
	sudo apt install Python3 idle3</li>
	<li>Implement the code to acquire the sensor data. The sensor data can be transmitted via I2C or GPIO. 
	NOTE: Refer to the following code example for additional temperature data via GPIO: 
	from max6675 import MAX6675 
	cs_pin1 = 24; clock_pin1 = 25; data_pin1 = 18 
	cs_pin2 = 9; clock_pin2 = 11; data_pin2 = 19 
	units = &#34;C&#34; 
	thermocouple1 = MAX6675(cs_pin1, clock_pin1, data_pin1, units) 
	thermocouple2 = MAX6675(cs_pin2, clock_pin2, data_pin2, units) 
	T1 = thermocouple1.get() 
	T2 = thermocouple2.get()</li>
	<li>Import paho.mqtt.client.</li>
	<li>Reuse the code in section 2 to connect and publish data to the broker.</li>
</ol>
4. Subscriber&#39;s setup
NOTE: Any devices on Internet may receive the processing data via the broker. The data is processed and visualized also by a Python code. In case the development is difficult, available applications such as MQTT Client in Google Play and MQT Tool in the App Store can be employed. Since the implementation of the user interface is quite lengthy, the details are not described here. Also note that existing applications such as MQT Tool in App Store can receive the data.
<ol>
	<li>Engage a device for subscription to the Internet. Ensure a physical cable connection and then execute a ping to the broker IP on the command line (e.g., ping 117.xx.xxx.xx).</li>
	<li>Install a suitable Python environment depending on the device and the OS. For example, install Pydroid3 on an android device instead of Python3 from the Google Play.</li>
	<li>Import both paho.mqtt.client and paho.mqtt.subscribe to connect to and receive data from the broker. 
	NOTE: Refer to the following code example: 
	import paho.mqtt.client as mqtt 
	import paho.mqtt.subscribe as subscribe 
	url=&#34;117.xx.xxx.xx&#34;; port = 1883; username = &#34;****&#34;; password = &#34;xxxxx&#34;; topic = &#34;Extruder&#34; 
	mqttc.username_pw_set(username, password) 
	mqttc.connect(host=url, port=port) 
	mqttc.subscribe(topic, 0) 
	mqttc.loop_start() 
	Sub1 = subscribe.simple(topic, hostname=url) 
	ExtruderData = Sub1.payload.decode(&#34;utf-8&#34;)</li>
	<li>Build a user interface as required using PyQT5. 
	NOTE: This part is very lengthy and focuses on graphical representation of the received data rather than communication. The corresponding code is provided as supplementary data.</li>
	<li>Display the incoming data on the GUI by executing the built code.</li>
</ol>
5. Data logging
NOTE: The processing data can be written in a database while monitoring. In this work, a lab-scale database was chosen. The data are connected onto a Microsoft Access file to easily write and retrieve from a user computer. In addition, a table can be instantly built by a query to analyze data in a spreadsheet such as Microsoft Excel.
<ol>
	<li>Select a subscriber device to record the data.</li>
	<li>Import pyodbc by executing &#34;pip install pyodbc&#34; on the command line for the Python code to access the database as shown in Figure 318.</li>
	<li>Send a query to the database by the Python code for recording the processing data. Refer to the Python code in Figure 3 for the method.</li>
	<li>Send a query to the database for retrieval of the recorded data. 
	NOTE: A code example for data retrieval is given below: 
	import pyodbc 
	x for x in pyodbc.drivers() if x.startswith(&#39;Microsoft Access Driver&#39;)] 
	conn_str = ( 
	r&#39;DRIVER={Microsoft Access Driver (*.mdb, *.accdb)};&#39; 
	r&#39;DBQ=C:\Users\data_analysis\db1.accdb;&#39; 
	) 
	cnxn = pyodbc.connect(conn_str) 
	crsr = cnxn.cursor() 
	for table_info in crsr.tables(tableType=&#39;TABLE&#39;): 
	print(table_info.table_name) 
	sql = &#34;&#34;&#34;\ 
	SELECT * FROM Process_Condition 
	&#34;&#34;&#34; 
	crsr = cnxn.execute(sql) 
	for row in crsr: 
	RetrievedData = pd.read_sql(sql, cnxn) 
	crsr.close() 
	cnxn.close()</li>
</ol>
6. Deployment
NOTE: If all the devices can be connected to the Internet, the setup is simple. However, to secure the machine side data, the publishers can be in the intranet only. In this case, the broker can be a gateway to the Internet. To be so, the broker should be equipped with two ethernet adaptors, one of which must have a public IP address. After all the items are developed, the codes should be deployed onto each device as shown in Figure 4. The mode of connection, wired or wireless, is not important, but it has to be secured so that each device should be able to access the broker. This means the broker can act as a gateway on the border between the intranet and the Internet for security purposes. Of course, even if all the devices are exposed to the Internet, there is no problem from a communication point of view.
<ol>
	<li>Connect the extrusion controller, the main publisher, and the additional publishers to the Intranet port via ethernet.</li>
	<li>Connect one ethernet port of the broker to the intranet and the other to the Internet.</li>
	<li>Connect subscribers to the Internet by repeating step 4.1 for all of them.</li>
</ol>
7. Execution
NOTE: To test the whole system, we started the extrusion line and ran all the Python codes and Mosquitto.
<ol>
	<li>Start the extrusion process. On the HMI of the machine, set the temperatures and turn on the heater by touching the button on the HMI screen. Once the required temperature is reached, start the screw rotation to extrude the polymer melt.</li>
	<li>Turn on all the computers, start the broker software on the broker device by the command &#34;net start mosquitto&#34;, and then run the Python codes to monitor and record the processing data as needed. 
	NOTE: The order of step 7.1 and step 7.2 can be reversed. The Python codes can be executed simply by typing &#34;python3 xxxxx.py&#34; on the command line followed by pressing Enter. Add this command to the start-up programs to avoid typing the command every time the device reboots.</li>
</ol>

<ol>
	<li>Shafiq, S. I., Szczerbicki, E., Sanin, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proposition+of+the+methodology+for+Data+Acquisition,+Analysis+and+Visualization+in+support+of+Industry+4.0.">Proposition of the methodology for Data Acquisition, Analysis and Visualization in support of Industry 4.0.</a> Procedia Computer Science. 159, 1976-1985 (2019).</li><li>Dilda, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Manufacturing:+Analytics+unleashes+productivity+and+profitability.">Manufacturing: Analytics unleashes productivity and profitability.</a> McKinsey &amp; Company. , (2017).</li><li>Ismail, A., Truong, H. L., Kastner, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Manufacturing+process+data+analysis+pipelines:+A+requirements+analysis+and+survey.">Manufacturing process data analysis pipelines: A requirements analysis and survey.</a> Journal of Big Data. 6, 1 (2019).</li><li>Nwanya, S. C., Udofia, J. I., Ajayi, O. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+machine+downtime+in+the+plastic+manufacturing.">Optimization of machine downtime in the plastic manufacturing.</a> Cogent Engineering. 4 (1), 1335444 (2017).</li><li>Zhou, T., Song, Z., Sundmacher, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Big+data+creates+new+opportunities+for+materials+research:+A+review+on+methods+and+applications+of+machine+learning+for+materials+design.">Big data creates new opportunities for materials research: A review on methods and applications of machine learning for materials design.</a> Engineering. 5 (6), 1017-1026 (2019).</li><li>Rousopoulou, V., Nizamis, A., Thanasis, V., Ioannidis, D., Tzovaras, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+maintenance+for+injection+molding+machines+enabled+by+cognitive+analytics+for+Industry+4.0.">Predictive maintenance for injection molding machines enabled by cognitive analytics for Industry 4.0.</a> Frontiers in Artificial Intelligence. 3, 578152 (2020).</li><li>Mamo, F. T., Sikora, A., Rathfelder, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Legacy+to+Industry+4.0:+A+Profibus+Sniffer.">Legacy to Industry 4.0: A Profibus Sniffer.</a> Journal of Physics: Conference Series. 870, 012002 (2017).</li><li>Figueroa-Lorenzo, S., A&#241;orga, J., Arrizabalaga, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+role-based+access+control+model+in+Modbus+SCADA+systems.+A+centralized+model+approach.">A role-based access control model in Modbus SCADA systems. A centralized model approach.</a> Sensors. 19 (20), 4455 (2019).</li><li>Schleipen, M., Gilani, S. -. S., Bischoff, T., Pfrommer, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=OPC+UA+&amp;+Industrie+4.0+-+Enabling+technology+with+high+diversity+and+variability.">OPC UA &amp; Industrie 4.0 - Enabling technology with high diversity and variability.</a> Procedia CIRP. 57, 315-320 (2016).</li><li>Ni&#539;ulescu, I. -. V., Korodi, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Supervisory+control+and+data+Acquisition+approach+in+node-RED:+Application+and+discussions.">Supervisory control and data Acquisition approach in node-RED: Application and discussions.</a> IoT. 1, 76-91 (2020).</li><li>Perez-Lopez, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SCADA+systems+in+the+industrial+automation.">SCADA systems in the industrial automation.</a> Tecnolog&#237;a en Marcha. 28 (4), 3-14 (2015).</li><li>Andersen, D. L., Ashbrook, C. S. A., Karlborg, N. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Significance+of+big+data+analytics+and+the+internet+of+things+(IoT)+aspects+in+industrial+development,+governance+and+sustainability.">Significance of big data analytics and the internet of things (IoT) aspects in industrial development, governance and sustainability.</a> International Journal of Intelligent Networks. 1, 107-111 (2020).</li><li>Kashyap, M., Sharma, V., Gupta, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Taking+MQTT+and+NodeMcu+to+IOT:+Communication+in+Internet+of+Things.">Taking MQTT and NodeMcu to IOT: Communication in Internet of Things.</a> Procedia Computer Science. 132, 1611-1618 (2018).</li><li>. Mythbusting the MES. Systema Available from: <a target="_blank" href="https://www.systema.com/blog/mythbusting-the-mes">https://www.systema.com/blog/mythbusting-the-mes</a> (2021)</li><li>Yeh, C. -. S., Chen, S. -. L., Li, I. -. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Implementation+of+MQTT+protocol+based+network+architecture+for+smart+factory.">Implementation of MQTT protocol based network architecture for smart factory.</a> Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 235 (13), 2132-2142 (2021).</li><li>Parian, C., Guldimann, T., Bhatia, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fooling+the+master:+Exploiting+weaknesses+in+the+Modbus+protocol.">Fooling the master: Exploiting weaknesses in the Modbus protocol.</a> Procedia Computer Science. 171, 2453-2458 (2020).</li><li>Mishra, B., Kertesz, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+MQTT+in+M2M+and+IoT+systems:+A+survey.">The use of MQTT in M2M and IoT systems: A survey.</a> IEEE Access. 8, 201071-201086 (2020).</li><li>. pyodbc 4.0.34 Available from: <a target="_blank" href="https://pypi.org/project/pyodbc/">https://pypi.org/project/pyodbc/</a> (2021)</li><li>Ayer, V., Miguez, S., Toby, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Why+scientists+should+learn+to+program+in+Python.">Why scientists should learn to program in Python.</a> Powder Diffraction. 29 (2), 48-64 (2014).</li><li>Boyes, H., Hallaq, B., Cunningham, J., Watson, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+industrial+internet+of+things+(IIoT):+An+analysis+framework.">The industrial internet of things (IIoT): An analysis framework.</a> Computers in Industry. 101, 1-12 (2018).</li></ol>

The authors declare no conflicts of interest.

By following the protocol presented, the processing data can be monitored and recorded without expensive IT solutions such as the MES. The IoT technologies can make it easier to acquire and deliver data from conventional machines. It has been shown that the message-based protocol, MQTT, successfully serves as a platform for data communication for polymer processing lines. Moreover, additional data can be flexibly measured and transmitted together. The additional publishers employed in this work were the Raspberry Pis. No...

In the wave of the 4th industrial revolution, the acquisition and monitoring of various processing data have become important tasks1. In particular, improving the manufacturing process using process data and establishing efficient operation plans will be an important goal of all manufacturing facilities2,3. Downtime can be greatly reduced if an alarm can be sent out of the factory or if predictive maintenance can be performed in time4. Recently, many efforts have been made for data analyses in polymer processes5,6. However, it is not easy to conduct these tasks due to the difficulties in acquiring such data from the existing systems7. The hierarchical structure of the control and instrumentation makes the data acquisition and communication difficult.
First of all, it is not possible to obtain data from different machines with different manufacturing dates. It is difficult to realize communication between different machines since this requires interoperability between different fieldbuses in proprietary formats. In this way, communication methods and data formats are kept private. This helps one easily maintain data security but keeps users dependent on the machine builder for the services and future developments. The recent control computers including human-machine interface (HMI) attached to polymer processing machines are mostly Windows-based these days but are loaded with software created in a proprietary development environment. It is possible to use programmable logic controllers (PLCs) from different companies to communicate with the sensors or actuators, but in many cases, the upper supervisory control and data acquisition (SCADA) system is dependent on the control computers8. This practice has caused numerous protocols, fieldbuses, and control systems to compete in the market. Although this complexity has been alleviated little by little over time, many types of fieldbuses and protocols are still in active use.
On the other hand, communication between control devices and SCADA has been standardized by the Open Platform Communications United Architecture (OPCUA)9. Moreover, communication between SCADA and the Manufacturing Execution System (MES) has also been mainly made through OPCUA. In such a tight hierarchical structure, it is not easy to freely extract data for process monitoring and analysis. Usually, data has to be extracted out of the SCADA or MES10. As mentioned earlier, these systems are vendor-specific, and the data formats are rarely open. As a result, the data extraction requires substantial support from the original information technology/operational technology (IT/OT) solution vendors. This can hinder data acquisition for monitoring and analysis.
In a film extrusion line, the control PC is supervised by a SCADA system11. The SCADA system is operated by a computer program that cannot be easily modified. The computer program might be editable, but the editing is quite expensive and time-consuming. To easily monitor and analyze the processing data, the data should be accessible from any location. To monitor the processing data away from the site, the computer program should be capable of streaming the processing data to the Internet12. Moreover, an open free method reduces the expenses for the data acquisition13. This approach allows data analysis to be performed even in small factories that cannot afford to invest in commercial IT solutions14.
In this study, a message protocol based on the publisher-subscriber model is employed. The Message Queuing Telemetry Transport (MQTT) is an open and standard protocol that enables messaging between multiple data providers and consumers15. Here, we propose a system that acquires, transmits, and monitors data using MQTT for existing manufacturing facilities. The system is tested in a film extrusion line to verify the performance. The data from the original controller are transmitted to an edge device via the Modbus protocol. Then, the data is published to the broker. In the meantime, two Raspberry Pis publish the measured temperatures and illuminance to the same broker. Then, any device on the Internet can subscribe to the data, followed by monitoring and recording it as shown in Figure 1. The protocol in this work shows how the whole procedure can be done.

<table><tbody><tr><td>Edge Device</td><td>Adavantech</td><td>UNO 420</td><td>Intel Atom E3815 Fanless</td></tr><tr><td>Film Extrusion Machine</td><td>EM Korea</td><td>Not Available</td><td>For production of 450 mm film</td></tr><tr><td>Pydroid</td><td>IIEC</td><td>Not Available</td><td>Android Devices</td></tr><tr><td>Python3</td><td>Python Software Foundataion</td><td>Not Available</td><td>Windows, Linux</td></tr><tr><td>Raspberry Pi 4</td><td>CanaKit</td><td>Not Available</td><td>Standard Kit</td></tr></tbody></table>

data communication based on mqtt in a polymer extrusion process

This work aims at building a flexible data communication structure for a polymer processing machine by employing a publisher-subscriber based protocol called Message Queuing Telemetry Transport (MQTT), which is operated over TCP/IP. Even when using conventional equipment, processing data can be measured and recorded by various devices anywhere through an Internet communication. A message-based protocol allows flexible communication that overcomes the shortcomings of the existing server-client protocol. Multiple devices can subscribe to the processing data published by source devices. The proposed method facilitates data communication between multiple publishers and subscribers. This work has implemented a system that publishes data from the equipment and additional sensors to a message broker. The subscribers can monitor and store the process data relayed by the broker. The system has been deployed and run for a film extrusion line to demonstrate the effectiveness.

It has been found that the data shown in the HMI and measured by the Raspberry Pis were monitored and recorded in the subscribers as shown in Figure 5. As presented in the video, the processing data are logged into the database.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63717/63717fig01.jpg" /> 
Figure 1: Outline of the data transmission using the MQTT protocol.<...

Watch this Scientific Journal Video about Data Communication Based on MQTT in a Polymer Extrusion Process at JoVE.com

Data Communication Based on MQTT in a Polymer Extrusion Process

This work proposes a flexible method for data communication between a film extrusion system and monitoring devices based on a message protocol called Message Queuing Telemetry Transport (MQTT).

This work aims at building a flexible data communication structure for a polymer processing machine by employing a publisher-subscriber based protocol ...

data-communication-based-on-mqtt-in-a-polymer-extrusion-process

Research

JoVE Journal

Engineering

3.4K Views.  Seoul National University of Science and Technology (SeoulTech). This work proposes a flexible method for data communication between a film extrusion system and monitoring devices based on a message protocol called Message Queuing Telemetry Transport (MQTT). 

Video: Data Communication Based on MQTT in a Polymer Extrusion Process

Polymer Microarrays for High Throughput Discovery of Biomaterials

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a combinatorial library of materials to be screened in a parallel, high throughput format1. Herein is described the formation and characterization of a polymer microarray using an on-chip photopolymerization technique 2. This involves mixing monomers at varied ratios to produce a library of monomer solutions, transferring the solution to a glass slide format using a robotic printing device and curing with UV irradiation. This format is readily amenable to many biological assays, including stem cell attachment and proliferation, cell sorting and low bacterial adhesion, allowing the ready identification of 'hit' materials that fulfill a specific biological criterion3-5. Furthermore, the use of high throughput surface characterization (HTSC) allows the biological performance to be correlated with physio-chemical properties, hence elucidating the biological-material interaction6. HTSC makes use of water contact angle (WCA) measurements, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). In particular, ToF-SIMS provides a chemically rich analysis of the sample that can be used to correlate the cell response with a molecular moiety. In some cases, the biological performance can be predicted from the ToF-SIMS spectra, demonstrating the chemical dependence of a biological-material interaction, and informing the development of hit materials5,3.

A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a ...

Bioengineering

Fabrication of the Thermoplastic Microfluidic Channels

In our lab, we have successfully isolated nucleic acids directly from microliter and submicroliter volumes of human blood, urine and stool using polymer/nanoparticle composite microscale lysis and solid phase extraction columns. The recovered samples are concentrated, small volume samples that are PCRable, without any additional cleanup. Here, we demonstrate how to fabricate thermoplastic microfluidic chips using hot embossing and heat sealing. Then, we demonstrate how to use in situ light directed surface grafting and polymerization through the sealed chip to form the composite solid phase columns. We demonstrate grafting and polymerization of a carbon nanotube/polymer composite column for bacterial cell lysis. We then show the lysis process followed by solid phase extraction of nucleic acids from the sample on chip using a silica/polymer composite column. The attached protocols contain detailed instructions on how to make both lysis and solid phase extraction columns.

Here we demonstrate how to fabricate thermoplastic microfluidic chips using hot embossing and heat sealing. Then we demonstrate how to use in situ light directed surface grafting and polymerization through the sealed chip to form the composite solid phase columns.

In our lab, we have successfully isolated nucleic acids directly from microliter and submicroliter volumes of human blood, urine and stool using ...

Biology

High Throughput Microfluidic Rapid and Low Cost Prototyping Packaging Methods

In this work, 3 different packaging and assembly techniques are presented. They can be classified into two categories: one-time use and reusable packaging techniques.
The one-time use packaging technique employs UV-based and temperature curing epoxies to connect microtubes to access holes, wire-bonding for integrated circuit connections, and silver epoxy for electrical connections. This method is based on a robust assembly technique that can support relatively high pressure close to 1 psi and does not need any support to strengthen the microfluidic architecture.
Reusable packaging techniques consist of PDMS-based microtube interconnectors and anisotropic adhesive films for electrical connections. These devices are more sensitive and fragile. Consequently, Plexiglas support is added to the microfluidic structure to improve the electrical contact when anisotropic adhesive films are used, and also to strengthen the microfluidic architecture. In addition, a micromanipulator is needed to maintain tubes while using a thin PDMS layer to connect them to the access holes. Different PDMS layer thicknesses, ranging from 0.45-3 mm, are tested to compare the best adherence versus injection rates. Applied injection rates are varied from 50-300 &#956;l/hr for 0.45-3 mm PDMS layers, respectively. These techniques are mainly applicable for low-pressure applications. However, they can be extended for high-pressure ones through plasma-oxygen process to permanently seal the PDMS to glass substrates. The main advantage of this technique, besides the fact that it is reusable, consists of keeping the device observable when the microchannel length is very short (in the range of 3 mm or lower).

In this article we describe different techniques for microfluidic rapid prototyping platforms. The proposed techniques are based on ultraviolet (UV) sensitive and temperature curing epoxies, polydimethylsiloxane (PDMS) based tubing, wire-bonding, and anisotropic adhesive films. The assembling procedures presented are developed for both one-time use devices as well as reusable microfluidic systems.

In this work, 3 different packaging and assembly techniques are presented. They can be classified into two categories: one-time use and reusable packaging ...

Microfluidic Fabrication of Polymeric and Biohybrid Fibers with Predesigned Size and Shape

A &#8220;sheath&#8221; fluid passing through a microfluidic channel at low Reynolds number can be directed around another &#8220;core&#8221; stream and used to dictate the shape as well as the diameter of a core stream.&#160;Grooves in the top and bottom of a microfluidic channel were designed to direct the sheath fluid and shape the core fluid.&#160;By matching the viscosity and hydrophilicity of the sheath and core fluids, the interfacial effects are minimized and complex fluid shapes can be formed.&#160;Controlling the relative flow rates of the sheath and core fluids determines the cross-sectional area of the core fluid.&#160;Fibers have been produced with sizes ranging from 300 nm to ~1 mm, and fiber cross-sections can be round, flat, square, or complex as in the case with double anchor fibers. Polymerization of the core fluid downstream from the shaping region solidifies the fibers.&#160;Photoinitiated click chemistries are well suited for rapid polymerization of the core fluid by irradiation with ultraviolet light.&#160;Fibers with a wide variety of shapes have been produced from a list of polymers including liquid crystals, poly(methylmethacrylate), thiol-ene and thiol-yne resins, polyethylene glycol, and hydrogel derivatives. Minimal shear during the shaping process and mild polymerization conditions also makes the fabrication process well suited for encapsulation of cells and other biological components.

Two adjacent fluids passing through a grooved microfluidic channel can be directed to form a sheath around a prepolymer core; thereby&#160;determining both shape and cross-section. Photoinitiated polymerization, such as thiol click chemistry, is well suited for rapidly solidifying the core fluid into a microfiber with predetermined size and shape.

A &#8220;sheath&#8221; fluid passing through a microfluidic channel at low Reynolds number can be directed around another &#8220;core&#8221; stream and ...

Solvent Bonding for Fabrication of PMMA and COP Microfluidic Devices

Thermoplastic microfluidic devices offer many advantages over those made from silicone elastomers, but bonding procedures must be developed for each thermoplastic of interest. Solvent bonding is a simple and versatile method that can be used to fabricate devices from a variety of plastics. An appropriate solvent is added between two device layers to be bonded, and heat and pressure are applied to the device to facilitate the bonding. By using an appropriate combination of solvent, plastic, heat, and pressure, the device can be sealed with a high quality bond, characterized as having high bond coverage, bond strength, optical clarity, durability over time, and low deformation or damage to microfeature geometry. We describe the procedure for bonding devices made from two popular thermoplastics, poly(methyl-methacrylate) (PMMA), and cyclo-olefin polymer (COP), as well as a variety of methods to characterize the quality of the resulting bonds, and strategies to troubleshoot low quality bonds. These methods can be used to develop new solvent bonding protocols for other plastic-solvent systems.

Solvent bonding is a simple and versatile method for fabricating thermoplastic microfluidic devices with high quality bonds. We describe a protocol to achieve strong, optically clear bonds in PMMA and COP microfluidic devices that preserve microfeature details, by a judicious combination of pressure, temperature, an appropriate solvent, and device geometry.

Thermoplastic microfluidic devices offer many advantages over those made from silicone elastomers, but bonding procedures must be developed for each ...

Fabrication of Refractive-index-matched Devices for Biomedical Microfluidics

The use of microfluidic devices has emerged as a defining tool for biomedical applications. When combined with modern microscopy techniques, these devices can be implemented as part of a robust platform capable of making simultaneous complementary measurements. The primary challenge created by the combination of these two techniques is the mismatch in refractive index between the materials traditionally used to make microfluidic devices and the aqueous solutions typically used in biomedicine. This mismatch can create optical artifacts near the channel or device edges. One solution is to reduce the refractive index of the material used to fabricate the device by using a fluorinated polymer such as MY133-V2000 whose refractive index is similar to that of water (n = 1.33). Here, the construction of a microfluidic device made out of MY133-V2000 using soft lithography techniques is demonstrated, using O2 plasma in conjunction with an acrylic holder to increase the adhesion between the MY133-V2000 fabricated device and the polydimethylsiloxane (PDMS) substrate. The device is then tested by incubating it filled with cell culture media for 24 h to demonstrate the ability of the device to maintain cell culture conditions during the course of a typical imaging experiment. Finally, quantitative phase microscopy (QPM) is used to measure the distribution of mass within the live adherent cells in the microchannel. This way, the increased precision, enabled by fabricating the device from a low index of refraction polymer such as MY133-V2000 in lieu of traditional soft lithography materials such as PDMS, is demonstrated. Overall, this approach for fabricating microfluidic devices can be readily integrated into existing soft lithography workflows in order to reduce optical artifacts and increase measurement precision.

This protocol describes the fabrication of microfluidic devices from MY133-V2000 to eliminate artifacts that often arise in microchannels due to the mismatching refractive indices between microchannel structures and an aqueous solution. This protocol uses an acrylic holder to compress the encapsulated device, improving adhesion both chemically and mechanically.

The use of microfluidic devices has emerged as a defining tool for biomedical applications. When combined with modern microscopy techniques, these devices ...

Drawing and Hydrophobicity-patterning Long Polydimethylsiloxane Silicone Filaments

Polydimethylsiloxane (PDMS) silicone is a versatile polymer that cannot readily be formed into long filaments. Traditional spinning methods fail because PDMS does not exhibit long-range fluidity at melting. We introduce an improved method to produce filaments of PDMS by a stepped temperature profile of the polymer as it cross-links from a fluid to an elastomer. By monitoring its warm-temperature viscosity, we estimate a window of time when its material properties are amendable to drawing into long filaments. The filaments pass through a high-temperature tube oven, curing them sufficiently to be harvested. These filaments are on the order of hundreds of micrometers in diameter and tens of centimeters in length, and even longer and thinner filaments are possible. These filaments retain many of the material properties of bulk PDMS, including switchable hydrophobicity. We demonstrate this capability with an automated corona-discharge patterning method. These patternable PDMS silicone filaments have applications in silicone weavings, gas-permeable sensor components, and model microscale foldamers.

Here, we present a protocol to produce long filaments of polydimethylsiloxane (PDMS) silicone by gravity-drawing through a furnace. Filaments are on the order of hundreds of micrometers in diameter and tens of centimeters in length and are hydrophobically patternable via an Arduino-controlled corona discharge system.

Polydimethylsiloxane (PDMS) silicone is a versatile polymer that cannot readily be formed into long filaments. Traditional spinning methods fail because ...

Computer Numerical Control Micromilling of a Microfluidic Acrylic Device with a Staggered Restriction for Magnetic Nanoparticle-Based Immunoassays

Microfluidic systems have greatly improved immunoassay techniques. However, many microfabrication techniques require specialized, expensive, or complicated equipment, making fabrication costly and incompatible with mass production, which is one of the most important preconditions for point-of-care tests (POCT) to be adopted in low-resource settings. This work describes the fabrication process of an acrylic (polymethylmethacrylate, PMMA) device for nanoparticle-conjugated enzymatic immunoassay testing using the computer numerical control (CNC) micromilling technique. The functioning of the microfluidic device is shown by performing an immunoassay to detect a commercial antibody using lysozyme as a model antigen conjugated to 100 nm magnetic nanoparticles. This device integrates a physical staggered restriction of only 5 &#181;m in height, used to capture magnetic microparticles that make up a magnetic trap by placing an external magnet. In this way, the magnetic force on the immunosupport of conjugated nanoparticles is enough to capture them and resist flow drag. This microfluidic device is particularly suitable for low-cost mass production without the loss of precision for immunoassay performance.

Microfluidics is a powerful tool for the development of diagnostic tests. However, expensive equipment and materials, as well as laborious fabrication and handling techniques, are often required. Here, we detail the fabrication protocol of an acrylic microfluidic device for magnetic micro- and nanoparticle-based immunoassays in a low-cost and simple-to-use setting.

Microfluidic systems have greatly improved immunoassay techniques. However, many microfabrication techniques require specialized, expensive, or ...

Visualizing Polymer Dynamics for Optimizing 3D Printing Quality

Real-Time Imaging of Bonding in 3D-Printed Layers

In recent times, 3D printing technology has revolutionized our ability to design and produce products, but optimizing the print quality can be challenging. The process of extrusion 3D printing involves pressuring molten material through a thin nozzle and depositing it onto previously extruded material. This method relies on bonding between the consecutive layers to create a strong and visually appealing final product. This is no easy task, as many parameters, such as the nozzle temperature, layer thickness, and printing speed, must be fine-tuned to achieve optimal results. In this study, a method for visualizing the polymer dynamics during extrusion is presented, giving insight into the layer bonding process. Using laser speckle imaging, the plastic flow and fusion can be resolved non-invasively, internally, and with high spatiotemporal resolution. This measurement, which is easy to perform, provides an in-depth understanding of the underlying mechanics influencing the final print quality. This methodology was tested with a range of cooling fan speeds, and the results showed increased polymer motion with lower fan speeds and, thus, explained the poor printing quality when the cooling fan was turned off. These findings show that this methodology allows for optimizing the printing settings and understanding the material behavior. This information can be used for the development and testing of novel printing materials or advanced slicing procedures. With this approach, a deeper understanding of extrusion can be built to take 3D printing to the next level.

Author Spotlight: Real-Time Imaging of Bonding in 3D-Printed Layers  

With a non-invasive and real-time technique, nanoscopic polymer motion inside a polymer filament is imaged during 3D printing. Fine-tuning this motion is crucial for producing constructs with optimal performance and appearance. This method reaches the core of plastic layer fusion, thus offering insights into optimal printing conditions and material design criteria.

In recent times, 3D printing technology has revolutionized our ability to design and produce products, but optimizing the print quality can be ...

Single-cell Microfluidic Analysis of Bacillus subtilis

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers single-cell resolution and long observation times spanning hundreds of generations; unlike agarose pad-based microscopy, it has uniform growth conditions that can be tightly controlled. Because the continuous flow of growth medium isolates the cells in a microfluidic device from unpredictable variations in the local chemical environment caused by cell growth and metabolism, authentic changes in gene expression and cell growth in response to specific stimuli can be more confidently observed. Bacillus subtilis is used here as a model bacterial species to demonstrate a &#34;mother machine&#34;-type method for cellular analysis. We show how to construct and plumb a microfluidic device, load it with cells, initiate microscopic imaging, and expose cells to a stimulus by switching from one growth medium to another. A stress-responsive reporter is used as an example to reveal the type of data that may be obtained by this method. We also briefly discuss further applications of this method for other types of experiments, such as analysis of bacterial sporulation.

Single-cell Microfluidic Analysis of Bacillus subtilis

We present a method for the microfluidic analysis of individual bacterial cell lineages using Bacillus subtilis as an example. The method overcomes shortcomings of traditional analytical methods in microbiology by allowing observation of hundreds of cell generations under tightly controllable and uniform growth conditions.

Microfluidic technology overcomes many of the limitations to traditional analytical methods in microbiology. Unlike bulk-culture methods, it offers ...

Data Communication Based on MQTT in a Polymer Extrusion Process

Summary

Explore More Videos

Chapters in this video

Polymer Microarrays for High Throughput Discovery of Biomaterials

Fabrication of the Thermoplastic Microfluidic Channels

High Throughput Microfluidic Rapid and Low Cost Prototyping Packaging Methods

Microfluidic Fabrication of Polymeric and Biohybrid Fibers with Predesigned Size and Shape

Solvent Bonding for Fabrication of PMMA and COP Microfluidic Devices

Fabrication of Refractive-index-matched Devices for Biomedical Microfluidics

Drawing and Hydrophobicity-patterning Long Polydimethylsiloxane Silicone Filaments

Computer Numerical Control Micromilling of a Microfluidic Acrylic Device with a Staggered Restriction for Magnetic Nanoparticle-Based Immunoassays

Real-Time Imaging of Bonding in 3D-Printed Layers

Single-cell Microfluidic Analysis of Bacillus subtilis