Tu sei qui:

Service

sistemi di visione

We are Logirobotix

Artificial Intelligence & Machine Vision applied in Manufacturing

With solutions implemented by Logirobotix, the Factories of the Future can detect defects throughout the production process with artificial intelligence inspection systems, deploy predictive maintenance to reduce downtime, respond to real-time changes in demand across the supply chain, reduce costs of small-batch or single-run goods, enabling greater customization, improve employee satisfaction by shifting mundane tasks to machines.

Artificial Intelligence (AI) and machine vision are becoming increasingly prevalent in manufacturing , as they offer many benefits in terms of quality control, process optimization, and productivity. Here are some examples of how AI and machine vision are being applied in manufacturing:

  1. Quality control: AI and machine vision can be used to inspect products and identify defects or anomalies in real-time. This allows manufacturers to detect problems early and take corrective action, reducing the number of defective products and improving overall product quality.
  2. Process optimization: AI and machine vision can analyze data from sensors and cameras to optimize manufacturing processes. For example, they can detect inefficiencies in the production line and suggest changes to improve efficiency, reduce waste, and save energy.
  3. Predictive maintenance: AI and machine vision can be used to predict when machines or equipment are likely to fail, allowing manufacturers to schedule maintenance before a breakdown occurs. This reduces downtime and extends the life of equipment, improving overall productivity.
  4. Robotics and automation: AI and machine vision can be used to control robots and automated systems, allowing them to perform tasks that require high precision and speed. This reduces the need for human labor and increases productivity.
  5. Supply chain optimization: AI and machine vision can be used to optimize the supply chain by tracking inventory levels, predicting demand, and identifying potential bottlenecks. This allows manufacturers to respond quickly to changes in demand and ensure that materials and products are delivered on time.

In summary, AI and machine vision are becoming essential tools in modern manufacturing, helping manufacturers to improve quality, increase productivity, and optimize processes. As the technology continues to improve, we can expect to see even more applications of AI and machine vision in manufacturing in the future.

Artificial intelligence software and hardware experience

Artificial intelligence software refers to the programs, algorithms, and tools used to develop and deploy AI applications. Some of the commonly used AI software includes:

  1. TensorFlow: an open-source software library developed by Google for building and deploying machine learning models.

  2. PyTorch: another open-source software library that provides tools for building and training deep learning models.

  3. Keras: a high-level neural network API that can run on top of TensorFlow, Theano, or CNTK.

  4. Scikit-learn: a machine learning library that provides tools for data analysis, clustering, and classification.

Artificial intelligence hardware refers to the specialized hardware designed to accelerate AI workloads.

Some of the commonly used AI hardware includes:

  1. Graphics Processing Units (GPUs): specialized hardware designed for parallel processing that can significantly accelerate training and inference of deep learning models.

  2. Field Programmable Gate Arrays (FPGAs): reprogrammable hardware that can be customized for specific AI workloads, offering high performance and low latency.

  3. Tensor Processing Units (TPUs): Google’s custom-built AI hardware designed to accelerate machine learning workloads.

  4. Application-Specific Integrated Circuits (ASICs): specialized hardware designed for specific AI workloads, offering high performance and low power consumption.

In summary, AI software and hardware are essential components in the development and deployment of AI applications. As the technology continues to evolve, we can expect to see even more advanced software and hardware solutions for AI.

Benefits for your business

Leading Complex Projects

Developments in artificial intelligence technologies, from basic Machine Learning to advanced Deep Learning models, have made it capable of solving complex problems.

Minimizing Errors

Artificial intelligence (AI) technologies can further mitigate human factor errors to improve safety and performance.

Quality Control

Artificial intelligence is extremely useful for performing predictive maintenance on machinery and equipment. Machines can learn to predict malfunctions and failures.

Cybersecurity

Machine learning solutions rely on networks, data, and technology platforms. The security of these systems and data is critical and machine learning can play a role by regulating access to valuable digital platforms.

What we offer

Artificial Intelligence inspection systems

We develop artificial intelligence solutions to make companies competitive in a constantly evolving market. We have completed more than 200 projects developing artificial intelligence solutions for manufacturing , pharmaceuticals and healthcare, public sector, transportation and logistics.

Artificial Intelligence (AI) inspection systems are becoming increasingly popular in various industries for their ability to detect defects and anomalies in a variety of products and materials. These systems use machine learning algorithms and computer vision technology to analyze data and images and identify any irregularities.

The use of AI inspection systems offers several advantages over traditional inspection methods. Firstly, they are much faster and more accurate, as they can process large amounts of data in real-time and detect even the smallest defects. Secondly, they can be used in a wide range of applications, from manufacturing to quality control, and can be customized to suit the specific needs of different industries and products.

Applications of inspection systems

One common application of AI inspection systems is in the automotive industry, where they are used to inspect critical components such as engine parts, transmissions, and brakes. They can detect cracks, corrosion, and other defects that might not be visible to the human eye, allowing manufacturers to ensure that their products are safe and reliable.

Another application is in the food industry, where AI inspection systems can be used to detect contaminants, foreign objects, and other quality issues in food products. This helps to ensure that the food is safe for consumption and meets regulatory standards.

In summary, AI inspection systems are a valuable tool for many industries, as they offer faster, more accurate, and more reliable inspection processes. As the technology continues to improve, we can expect to see even more widespread adoption of AI inspection systems in the future.

AI-based inspection system for manual and automatic processes

Our revolutionary, low-cost, automated AI guided inspection systems, suitable for both inline or near production that automates quality control in the shopfloor (plastic, silicone, metal components). Plug & Play, inexpensive compared to the traditional industrial vision, is offered through a monthly 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐩𝐭𝐢𝐨𝐧.

AI-based inspection systems can also be applied to manual processes where human inspection is traditionally required. These systems use computer vision and machine learning algorithms to analyze images and video feeds, allowing them to detect defects and anomalies in real-time.

One example of a manual process that can benefit from AI-based inspection systems is welding. Welding is a critical process in many manufacturing industries, and it is essential to ensure that the welds are of high quality to maintain the safety and integrity of the product. However, manual inspection of welds can be time-consuming and prone to errors.

Versatile for multiple cases

AI-based inspection systems can automate the process of inspecting welds, reducing the need for manual inspection and increasing the accuracy and speed of the inspection process. These systems can analyze weld images in real-time and detect defects or anomalies, such as cracks, porosity, or incomplete fusion.

Another example of a manual process that can benefit from AI-based inspection systems is manual assembly. In manual assembly, it is essential to ensure that all components are correctly assembled and aligned, and that there are no missing or damaged parts. However, manual inspection of assembled components can be time-consuming and prone to errors.

AI-based inspection systems can analyze images and video feeds of the assembly process in real-time and detect any defects or anomalies, such as misaligned components, missing or damaged parts, or incorrect assembly. This can help to ensure that the final product is of high quality and meets the required standards.

In summary, AI-based inspection systems can be applied to manual processes to automate the inspection process, increase accuracy, and reduce the need for manual inspection. As the technology continues to improve, we can expect to see even more applications of AI-based inspection systems in the future.

PCB quality control

PCB quality control with computer vision is a popular application of machine vision in the electronics industry. Computer vision technology can be used to inspect printed circuit boards (PCBs) to detect defects and ensure they meet the required quality standards.

There are different types of defects that can occur in PCBs, such as missing components, incorrect orientation, misalignment, short circuits, open circuits, and solder defects. Computer vision systems can detect these defects by analyzing images of the PCBs.

To implement PCB quality control with computer vision, the following steps can be taken:

  1. Capture images of the PCBs : The PCBs can be captured using a high-resolution camera or a scanner. The images should be clear and of high quality to enable accurate analysis.

  2. Image preprocessing: The images can be preprocessed to enhance the quality and remove noise. This can involve techniques such as image filtering, thresholding, and image segmentation.

  3. Defect detection: Computer vision algorithms can be used to detect defects in the PCBs. This can involve techniques such as template matching, edge detection, and machine learning.

  4. Defect classification: Once defects are detected, they can be classified based on their type and severity. This can enable appropriate action to be taken to address the defects.

  5. Reporting: The results of the inspection can be reported to the relevant stakeholders, such as quality control personnel or production supervisors. This can enable timely action to be taken to address any issues.

Overall, PCB quality control with computer vision can help to improve the efficiency and accuracy of the inspection process, reduce costs associated with manual inspection, and improve the overall quality of the PCBs produced.

Computer vision for quality control

Molds
Plastic
Metal
Rubber
PCB
Surface verification
Previous slide
Next slide

Medical devices quality control

Quality control of medical devices is critical to ensure that they are safe and effective for use in healthcare settings. Computer vision technology can be used to inspect medical devices to detect defects and ensure they meet the required quality standards.

There are different types of defects that can occur in medical devices, such as cracks, scratches, deformations, and missing components. Computer vision systems can detect these defects by analyzing images or videos of the medical devices.

To implement quality control of medical devices with computer vision, the following steps can be taken:

  1. Capture images or videos of the medical devices: The medical devices can be captured using a high-resolution camera or a scanner. The images or videos should be clear and of high quality to enable accurate analysis.

  2. Image preprocessing: The images or videos can be preprocessed to enhance the quality and remove noise. This can involve techniques such as image filtering, thresholding, and image segmentation.

  3. Defect detection: Computer vision algorithms can be used to detect defects in the medical devices. This can involve techniques such as template matching, edge detection, and machine learning.

  4. Defect classification: Once defects are detected, they can be classified based on their type and severity. This can enable appropriate action to be taken to address the defects.

  5. Reporting: The results of the inspection can be reported to the relevant stakeholders, such as quality control personnel or production supervisors. This can enable timely action to be taken to address any issues.

Overall, quality control of medical devices with computer vision can help to improve the efficiency and accuracy of the inspection process, reduce costs associated with manual inspection, and improve the overall quality of the medical devices produced. It is important to note that the use of computer vision technology for medical device quality control must comply with regulatory requirements and standards such as ISO 13485 and FDA regulations.

SaaS and Cloud Computing

Software-as-a-service (SaaS) and cloud computing are two related concepts that have transformed the way businesses access and use software applications.

SaaS refers to a model of software delivery where applications are hosted by a third-party provider and made available to customers over the internet. Instead of buying and installing software on their own hardware, customers can access software applications through a web browser or a dedicated app. The provider is responsible for maintaining the software, upgrading it, and ensuring its availability.

Cloud computing refers to the use of remote servers to store, manage, and process data, instead of using local servers or personal devices. Cloud computing can be used for a variety of purposes, including data storage, application hosting, and computing resources. Cloud computing can be accessed through the internet, allowing businesses to scale their infrastructure and resources without having to invest in expensive hardware and infrastructure.

The key benefits of SaaS and cloud computing include:

  1. Reduced costs: SaaS and cloud computing can significantly reduce costs associated with hardware, infrastructure, and maintenance. Businesses can access software applications and computing resources on a pay-per-use basis, eliminating the need for upfront investment in hardware and software.

  2. Scalability: SaaS and cloud computing can be easily scaled up or down depending on business needs. As businesses grow or change, they can adjust their usage of software applications and computing resources accordingly.

  3. Accessibility: SaaS and cloud computing can be accessed from anywhere with an internet connection, making it easier for businesses to collaborate and work remotely.

  4. Reliability: SaaS and cloud computing providers are responsible for ensuring the availability and performance of their services, reducing the risk of downtime or system failures.

Overall, SaaS and cloud computing have revolutionized the way businesses access and use software applications and computing resources. They provide cost-effective, scalable, and reliable solutions that can help businesses stay competitive and agile in a fast-changing market.

cloud computing

IoT Lab

We are committed to providing fully functional, customizable, and cost-effective open source IoT solutions.

IoT and computer vision are two technologies that can be integrated to provide a wide range of applications for different industries, including manufacturing, healthcare, transportation, and security.

IoT refers to the network of physical devices, vehicles, buildings, and other objects that are embedded with sensors, software, and connectivity, allowing them to exchange data and communicate with each other. IoT devices can generate large volumes of data, which can be analyzed to extract insights and improve decision-making.

Computer vision, on the other hand, is a field of study that focuses on enabling machines to interpret and understand visual information from the world. It involves the use of algorithms and machine learning techniques to analyze and extract information from images and videos.

When IoT and computer vision are combined, they can enable a wide range of applications, such as:

  1. Smart surveillance: IoT sensors can be combined with computer vision algorithms to detect and track people and objects in real-time, improving security and safety in public spaces, airports, and other critical areas.

  2. Industrial automation: IoT sensors can be integrated with computer vision systems to monitor and optimize manufacturing processes, detecting defects and improving efficiency.

  3. Healthcare: IoT devices such as wearables can be combined with computer vision algorithms to monitor patient health, detect anomalies, and provide early warning signs of potential health problems.

  4. Traffic management: IoT sensors can be combined with computer vision systems to monitor traffic patterns, detect accidents, and optimize traffic flow in real-time.

  5. Retail: IoT sensors and computer vision systems can be used to analyze customer behavior, detect anomalies, and provide personalized recommendations.

Overall, the combination of IoT and computer vision can provide powerful insights and enable a wide range of applications that can improve efficiency, safety, and customer experiences across different industries. Also, check our projects