Industrial Automation

Industrial Automation
Smart Factories for a Smart Future

Industry 4.0 (fourth industrial revolution) refers to the conversion of the manufacturing sector into a fully digitized and connected environment. This transformation encompasses the incorporation of emerging technologies such as the Internet of Things (IoT), computing spanning from edge to cloud across various layers, and the seamless integration of AI-driven analytics into every facet of their operations. The smart factories are equipped with advanced sensors, embedded software and collaborative robots.

Our utilization of camera and Sensor Fusion technologies, including Voice & Facial Recognition, Object / Parts Recognition, Collision Avoidance, Trajectory & Path Planning, in our Facility Monitoring & Inventory Movement solutions empowers robots to carry out diverse tasks across industrial and retail environments, encompassing factories, warehouses, supermarkets, and malls.


We are experts in seamlessly integrating sensors into different applications, improving data processes. Our proficiency enhances data collection, analysis, and decision-making. Our array of perception algorithms spans a variety of applications, encompassing Multi-Object Tracking, Sensor Fusion, Radar Odometry, and more, catering to diverse use cases. MulticoreWare excels in sensor-related expertise across the following domains:

Control & Data

  • Sensors
  • Actuators
  • Sensor Fusion
  • Device Drivers

Analysis & Development

  • Algorithms
  • Deep Learning
  • Machine Learning
  • Big Data


  • Pruning
  • Quantization
  • Porting
  • Vectorization


  • Graphical
  • 3D Virtual environment


MulticoreWare is engaged in implementing Industry 4.0 principles and practices, integrating digital technologies, automation, data analytics, and IoT into industrial processes, fostering smart factories. This approach enhances operational efficiency, flexibility, and productivity while promoting data-driven decision-making and predictive maintenance to optimize manufacturing operations and drive innovation.

Quality Control and Inspection:

Employ real-time monitoring, sensors, and predictive algorithms for adherence to standards, proactive quality control, and error reduction.

Predictive Maintenance:

Utilize IoT, ML, and predictive analytics to predict equipment failures, optimize maintenance, and enhance operational efficiency.

Safety and Hazard Detection:

Automate response to unsafe conditions, mitigate risks, and ensure worker safety through early detection.

Supply Chain Optimization:

Enhance supply chain efficiency with IoT, AI, and autonomous robots, achieve real-time monitoring, demand forecasting, inventory management, and logistics optimization for cost reduction and customer satisfaction.


Defect Detection at Bubble in Gear:

The task is to find the bubble in the gear. Identifying and flagging bubbles as defects within gear components is a critical quality control process in manufacturing. Using advanced imaging technologies, we can specifically target bubble-related anomalies.

Categorization of a Milling Dataset:

It involves the process of organizing and grouping the data into distinct categories or classes based on specific criteria or attributes. This process is essential for various applications, such as quality control, process optimization, and predictive maintenance in manufacturing.

Image Enhancement:

It is one of the most important techniques to find the detail and anomaly, which is done using super-resolution.


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