Insituware integrates hardware and software technology to enable the rapid assessment of materials during use. They combine advanced analytical sensors, smart devices, and intelligent cloud-based information with innovative packaging methods to enable nonintrusive sensing of materials in motion. The company is a subsidiary of Systems Innovation Engineering (SIE), an innovative technology-driven company that provides systems engineering capabilities specifically designed to improve supplier resiliency and overall product affordability.
“Insituware revolutionizes the way we control materials in manufacturing, sense physiological changes in sports, understand materials performance in aviation, and measure critical changes in construction materials,” Co-founder, David Tafuna.
The company is cloud-based and operates with a local family of materials-specific apps on a handheld device. Insituware integrates technology that enables better control of materials by performing rapid assessments of materials on location and during use or in situ meaning ‘in place’. With a combination of advanced analytical sensors, smart devices, and intelligent cloud-based information with innovative packaging methods, Insituware enables nonintrusive sensing.
Measuring materials “in situ” (i.e. while in use) allows for rapid decision making. Measurements in situ provide numerous benefits over lab measurements. While measurements in a lab are accurate, they are also expensive and slow, requiring trained lab technicians and costly equipment as well as the time to ship materials to the lab facility. These benefits include:
Effective: Enables users to make decisions quickly
Inexpensive: Highly integrated analytical sensors can be deployed at scale
Quick: Automated interpretations generated using machine learning methods
Intelligent factory measurement
The Vision product family consists of the Mark-1 handheld device, the plug and play modules, material specific Mark-1 application programs (“Apps”), and the Insitucloud which incorporates the data management system, “MethodQC” (the statistical process control software), and the machine learning back-end analysis system. The Vision product family operates seamlessly to enable rapid and intelligent measurements in the factory while trending and tracking the collected data from any computer with internet access. It enables the monitoring and control of laminates, soldering materials, coatings, masks, and adhesives. This family of products will also enable the characterization and identification of residues to assure effective cleaning processes and ultimate product reliability.
Vision Mark-1 and insight modules
The plug and play Insight modules expand the functionality of the Vision Mark-1 device to enable the non-destructive inspection and process control of materials both before and during use.
With a touch of a button, the Vision Mark-1 shows results that can increase efficiency, knowledge and productivity. The company’s hardware device technology is designed to be used as a partner with materials specific Insituware Apps, allowing users to see and use customizable results to better monitor and control materials. These apps, combined with Vision Mark-1, integrate statistical process control analysis methods (MethodQC) and machine learning in a cloud environment.
Access to materials data
The Insitucloud with the MethodQC expands the reach of a device by providing access to a repository of materials data. Users can rapidly identify unknown materials, investigate global and local changes in materials, and identify the root cause of material degradation or failure. It also continuously improves to provide users with material knowledge and predictive models to make informed decisions.
The company’s team makes it a priority to help customers measure in situ. First, they use the latest generation of integrated, miniaturized analytical sensors. Next, they perform data science and machine learning techniques to compensate for the in-field sensor outputs. This helps customers replace a trained technician with automated software. Finally, they connect all sensors to a centralized cloud backend to enable big data analyses. This gathers and stores sensor data to continually improve the system.
Tafuna added, “It is important to be solutions-focused, rather than sensor-focused. Many IoT companies start at the sensor and try and find use cases. We start at the problem and find the best sensors to solve the problem.” Starting with the sensor and attempting to create relevant applications can result in companies that are siloed and force a solution to a problem.