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Cloud-based Data Analytics Platform for Injection Molding Machines

Collects data from battery-powered sensing devices, uses multiple connectivity technologies to transfer data to the cloud

Solution

Industrial IoT platform for injection molding machines: from sensor data to intelligent insights
Industry

Industrial Manufacturing
ENGAGEMENT MODEL

T&M (time and materials)
METHODOLOGY

Agile
Team
  • Firmware Developers
  • Back-end Developer
  • Business Analyst
  • Project Manager
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Customer

Krammer Technology, a startup that provides consulting services to injection molding businesses, is working on an Industrial IoT solution that transforms molds into intelligent devices providing robust data on equipment performance.

  • BUSINESS NAME Krammer Technology
  • LOCATION USA
  • BUSINESS ACTIVITY Industrial manufacturing, industrial data analytics

Problem

Krammer Technology addressed Softeq to test the feasibility of their idea, choose the optimum technology stack, and outline the scope of work. Therefore, we began the project with an Analysis Phase.

A market-ready solution is supposed to:

  • Collect and process data from water temperature and mold movement sensors installed on injection molding machines
  • Improve the manufacturing process
  • Detect abnormal behavior and notify human operators before the defects result in a failure

Solution

Following the Business Analysis phase, we have made the following assumptions:

  • The system collects equipment performance data via battery-operated intelligent sensor devices (ISDs). The devices use custom bare metal firmware
  • The data is transferred to a cloud server via a custom Linux-driven gateway.
  • IEEE 802.15.4 is the primary connectivity standard. This provides an opportunity to implement popular connectivity technologies like Bluetooth, Zigbee, and Z-Wave
  • The platform uses AWS data storage, processing, and visualization tools 

As a first step, the Softeq hardware team will create custom ISDs and conduct field tests to make sure the data is produced in a format suitable for further Machine Learning-assisted analysis. The results will help developers refine the requirements for the sensing devices. Next, we will integrate ISDs with the AWS cloud services. 

The solution will help manufacturing companies lower equipment maintenance costs, prevent downtime, and reduce plastic waste. Besides costly wired molding equipment monitoring solutions produced by KISTLER and WITTMANN, there are no alternatives to the Krammer Technology system available on the market. 

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