IMA: IoT system for data collection and analysis

We transformed an intuition into a PoC that saved 90% of R&D costs.

Industrial automation has historically been the domain of mechanical and electronic engineers. Today, the integration of computer engineering generates value and innovation, unlocking new R&D frontiers.

R&D in machinery production has always been costly. Having identified a new quality control machine equipped with an optical laser, IMA's need is to develop a monitoring and data visualization program to enable ideal line automation.

The development of this embedded software is challenging, especially without feasibility confirmations based on the machine's performance. What if it were possible to verify the suitability of the machine for the intended application through a virtual PoC, a testing method capable of streamlining the costs and time of this operation? This led to the Quinck team's intuition to perform this verification through a web-based simulation of the software to be installed on the machine. Thanks to a web-based simulation of the software, it was possible to measure data collection, analysis, and visualization performance, even with very high scrolling speeds.

In this way, IMA was able to verify the suitability of the new machine's performance by investing only 10% of the resources and time required compared to embedded implementation. A few simple steps: development of the simulation algorithm, creation of the interface software, and on-board machine testing with real measurements.

All internal components communicate via synchronized channels and concurrent operations, enabling the following essential functionalities for the project:

Data Ingestion via TCP from direct Laser sensor connection: Ensures direct and high-speed acquisition of raw data from the sensor.
Sending and receiving commands for settings via TCP: Allows for bidirectional and real-time control of sensor settings.
REST API for communicating with the web dashboard: Facilitates standardized interaction between the backend and the web user interface.
Data parsing, analysis, and processing of data and measurements: Ensures the transformation of raw data into meaningful information and usable measurements.
Websockets API for displaying live data in semi-real time on the web dashboard: Enables dynamic and near-instantaneous visualization of acquired data, crucial for monitoring.
User-configurable alerting system on performed measurements: Offers the ability to configure notifications based on measurement thresholds, supporting operational responsiveness.
Storage of selected samples on a MongoDB database: Allows for efficient archiving of relevant data samples for subsequent analysis.
Telemetry and internal monitoring for software visibility via Prometheus and Grafana: Provides tools for continuous monitoring of software performance and status, ensuring reliability and debugging.

The management of this data is accessed via a web dashboard, developed in Svelte. This front-end technology allows for real-time visualization of the sample graph, management of sensor and user settings, and the use of an alerting system that can be activated when user-defined limits are exceeded.

In addition to software development, an in-house testing phase allowed us to move from applying the algorithm to simulated data to an on-machine test with real-time measurements, validating the technical choices and the achievement of the project's performance objectives.

All internal components communicate via synchronized channels and concurrent operations, enabling the following essential functionalities for the project:

Data Ingestion via TCP from direct Laser sensor connection: Ensures direct and high-speed acquisition of raw data from the sensor.
Sending and receiving commands for settings via TCP: Allows for bidirectional and real-time control of sensor settings.
REST API for communicating with the web dashboard: Facilitates standardized interaction between the backend and the web user interface.
Data parsing, analysis, and processing of data and measurements: Ensures the transformation of raw data into meaningful information and usable measurements.
Websockets API for displaying live data in semi-real time on the web dashboard: Enables dynamic and near-instantaneous visualization of acquired data, crucial for monitoring.
User-configurable alerting system on performed measurements: Offers the ability to configure notifications based on measurement thresholds, supporting operational responsiveness.
Storage of selected samples on a MongoDB database: Allows for efficient archiving of relevant data samples for subsequent analysis.
Telemetry and internal monitoring for software visibility via Prometheus and Grafana: Provides tools for continuous monitoring of software performance and status, ensuring reliability and debugging.

The management of this data is accessed via a web dashboard, developed in Svelte. This front-end technology allows for real-time visualization of the sample graph, management of sensor and user settings, and the use of an alerting system that can be activated when user-defined limits are exceeded.

In addition to software development, an in-house testing phase allowed us to move from applying the algorithm to simulated data to an on-machine test with real-time measurements, validating the technical choices and the achievement of the project's performance objectives.

ICP - Meddle and Data Certification on Blockchain

Data validation from IoT platform directly on ICP blockchain

Go to the project

R&W Tech: Web Dashboard for Centralized IoT Machinery Management

We gave a collective brain to 50 welding machines that weren't talking to each other

Go to the project

Autoctono: Wine E-commerce

The digital ecosystem that transforms the purchasing experience for the wine community.

Go to the project

NETECH: Banking Calibration Management

UX/UI redesign of platform for anti-money laundering controls in banking environment

Go to the project

Data Warehouse: Anti-Money Laundering System

Computerized database for data storage in compliance with anti-money laundering regulations

Go to the project

CHARGECAR: Electric Charging Station Finder

Backend for mobile platform aggregating multiple electric vehicle charging providers

Go to the project

HCUBE - HTees Project: Sports Wearables

Mobile app for vital parameter monitoring via innovative patches applied to sportswear

Go to the project

VERNICE: Neural Network for Paint Mixing

Machine learning system to predict paint formulas from specific colors in RGB format

Go to the project

EVENN: Neighborhood Services App

Mobile marketplace for sharing neighborhood services with time-based currency system

Go to the project

Non così raro: Clinical Screening

Web platform for diagnostic kit logistics management in hospital clinical trials

Go to the project

FFRI: Artificial Intelligence and Fashion

AI tool for enhancing and blending historical textile patterns

Go to the project

Agrinsight: AgriTech Platform for Remote Orchard Management

Remote monitoring and digital management for intensive orchards

Go to the project

WEP: Custom Global e-commerce

Transform a travel catalog into a machine for generating personalized experiences

Go to the project

Satispay: Corporate WebApp

When one of Europe's most innovative fintechs asks you to build its new digital home

Go to the project