Deep Learning in Machine Vision
Back to all insights We take it for granted, but our eyes and brain work together in ways that almost seem miraculous when you consider them. The human eye is capable of receiving light and converting it into information that is streamed instantaneously to the brain, which interprets that data just as quickly and prompts the body to react to whatever is seen. After hundreds of years of industry and innovation, only now is technology beginning to catch up to nature in this regard. Recent breakthroughs in artificial intelligence have led to some exciting developments in what is being called “deep learning.” In the manufacturing sector, the marriage of deep learning and existing machine vision tech is helping automation take another leap forward to machines that may soon have more in common with people than we ever thought possible. Understanding Deep Learning and Neural Networks Just like people, artificial intelligence wasn’t born knowing or understanding much on its own. It has to be taught the same way humans are, by introducing it to huge amounts of data. Advanced neural networks that simulate the functions of the human brain receive this information and “learns” to identify patterns that it uses to make connections with new information it encounters later. For example, showing an AI a single picture of a duck and telling it what the animal is only means the algorithm can successfully identify that specific drawing as a duck. However, training the program by showing it thousands of different pictures of ducks enables it to identify the common characteristics, such as the webbed feet, the wings, the bill, etc. Based on this training, the algorithm should be capable of identifying a duck even if it had never seen that specific image in the past. The computational power of such a neural network is massive compared to computers that run traditional automation systems, and pairing it with a machine vision system has the potential to change the way manufacturers interact with their equipment. By integrating deep learning, machine vision technology is poised to become much more versatile and effective for the manufacturing industry. Applications of Deep Learning in Machine Vision Prior to these recent advancements in AI machine vision, cameras were used in many automation applications, but in limited capacities. Traditional machine vision was capable of identifying certain aspects of components or products for sorting purposes, but these were relatively simple functions. Today, however, the integration of a deep learning algorithm has expanded the utility of machine vision systems by enabling them to go beyond image classification. Deep learning machine vision systems can serve a variety of roles within a manufacturing environment. Some of the most common machine vision application types include: Quality Assurance: The advanced pattern recognition capabilities of this technology make it ideal for QA. A machine vision inspection platform can identify defects on parts or products almost instantly, saving a considerable amount of time compared to traditional visual inspections. Product Assembly: Thanks to the use of a deep learning model, modern industrial machine vision systems are capable of providing much more accurate and detailed control over robotic assembly arms. Rather than needing all components to be in a precise location for the robot to find them, these machine vision systems immediately identify the parts and assemble them in the most efficient manner. Machine Health Monitoring: The same principle that enables machine learning to spot defects in products also can be applied to manufacturing equipment. Computer vision platforms can keep a close eye on components and alert technicians to any signs of wear and tear before they lead to failure. This means preventive maintenance can be delivered with greater accuracy and efficiency. Workplace Safety: Machine vision also can be used to ensure employees are adhering to proper safety protocols. Cameras in production areas can monitor employee movements and determine when unsafe behaviors occur. Transfer Learning vs. Reinforcement Learning No matter how sophisticated they are, machine learning needs to be trained before it can be of use to manufacturers. The software needs to review a substantial amount of data to be able to make decisions autonomously, and starting from zero with every new application can be a massive undertaking. Fortunately, there are ways to train machine vision systems that allow them to use information they already have without needing to start from scratch. Transfer Learning: In the simplest terms, this involves training an algorithm to transfer the skills it learned on one task to a similar job. For example, a system that is trained to identify defects in nails can be taught to do the same thing for screws simply by showing it the difference between nails and screws. This means the system can carry over what it knows about defects and apply it to something similar but unique. Reinforcement Learning: Best suited for more complex applications, this model mimics the trial-and-error process of problem-solving people use. The system is told what the optimal outcome is and learns to perform the task in the most efficient and effective way by being told what it did correctly and what it got wrong. In effect, the algorithm is rewarded or punished based on its performance and learns to adjust its routines. Thanks to the integration of a deep learning system, a modern machine vision solution becomes capable of so much more than it was in the past. In essence, it gives automated systems the ability to think about what they see and make better decisions as a result. As manufacturers look for better ways to become more efficient and productive, it’s easy to see how deep learning machine vision technology will play a significant role. more insights
How to Choose the Right Assembly Automation Solution for Your Business
Back to all insights The manufacturing industry is continuously evolving, and assembly automation solutions are quickly gaining popularity due to their potential to increase productivity, efficiency, and profitability. Assembly automation solutions are designed to streamline production processes, reduce human error, and improve the quality of the products being manufactured. These systems can also enhance workplace safety and reduce the likelihood of work-related injuries or accidents. However, with numerous assembly automation solutions available in the market, selecting the right one for your business can be challenging. Choosing the wrong solution can result in wasted resources and, ultimately, reduced productivity. Therefore, it’s crucial to understand what factors to consider when choosing an assembly automation solution for your business. This blog post aims to guide you through the process of selecting the right assembly automation solution for your business. By the end of this post, you will have a comprehensive understanding of the key factors to consider when choosing the best assembly automation solution that fits your business needs. Whether you’re a small startup or a large corporation, this guide will help you make an informed decision about selecting the right assembly automation solution to maximize your business’s efficiency and profitability. Industry The industry in which your business operates is a crucial factor to consider when selecting an assembly automation solution. Different industries have varying requirements, regulations, and standards that must be followed to ensure compliance and optimal efficiency. Therefore, it’s essential to choose a solution that is specifically tailored to meet the unique needs of your industry. For instance, if your business operates in the food industry, you must consider FDA regulations when selecting an assembly automation solution. The solution you choose must comply with food safety standards and be easy to clean to avoid contamination of the food products you manufacture. Similarly, if your business is in the automotive industry, you need an assembly automation solution that can handle heavy-duty parts and equipment. The solution must be capable of handling a high volume of production and have the flexibility to adjust to varying production demands. It’s also crucial to consider the future growth of your industry. Will your industry have changing requirements and regulations that will affect your production processes? Choosing a scalable and adaptable assembly automation solution that can accommodate future changes will help ensure that your business remains competitive in the long run. In summary, it’s essential to select an assembly automation solution that is tailored to meet the specific needs of your industry. Understanding the unique requirements and regulations of your industry will help you choose a solution that ensures compliance and optimal efficiency. Production Requirements Production requirements are a crucial factor to consider when selecting an assembly automation solution for your business. Understanding your production goals and objectives will help you choose a solution that can meet your business’s specific needs. The volume of production is an important consideration when choosing an assembly automation solution. If your business requires a high volume of production, you need a solution that can handle the workload. A solution that is capable of producing a high number of units in a short period is ideal. On the other hand, if your production volume is relatively low, you can consider a solution that is designed for low-volume production. Speed of production is another critical factor to consider. If your business requires quick turnaround times, you need a solution that can handle high-speed production. Choosing an assembly automation solution that can accommodate high-speed production will ensure that you meet your production goals within the required time frame. Product specifications are also essential when selecting an assembly automation solution. If your products have complex specifications, you need a solution that can handle multiple assembly steps. The solution must be capable of handling different sizes, shapes, and weights of the products you produce. This is especially important for businesses that manufacture products with varying specifications. In summary, it’s crucial to select an assembly automation solution that can meet your production goals and objectives. Understanding the volume of production, speed of production, and product specifications will help you choose a solution that meets your business’s specific needs. Budget Budget is a crucial factor to consider when selecting an assembly automation solution for your business. Implementing automation solutions can be costly, and it’s essential to choose a solution that fits your budget. When considering your budget, it’s important to factor in the total cost of ownership (TCO) of the solution. This includes the cost of installation, maintenance, and upgrades. Choosing a solution that has a lower TCO may be more cost-effective in the long run, even if it has a higher initial cost. In addition to the cost of the solution itself, you also need to consider the cost of training your staff on how to use the new equipment. Proper training is essential to ensure that your staff can operate the equipment efficiently and safely. Failing to train your staff properly can lead to costly mistakes and downtime, which can impact your business’s profitability. It’s important to work closely with the vendor to understand the total cost of ownership and to negotiate the best possible deal. Some vendors may offer financing options or flexible payment plans that can help you manage the cost of implementing the solution. In summary, budget is a crucial factor to consider when selecting an assembly automation solution. Understanding the total cost of ownership and factoring in the cost of training your staff will help you choose a solution that fits your budget and meets your business’s specific needs. Workforce When selecting an assembly automation solution, it’s crucial to consider your workforce and their skill levels. Different automation solutions require varying levels of training and expertise from your employees. It’s important to choose a solution that aligns with your workforce’s skills and expertise. If you have a highly skilled workforce, you may be able to implement a more complex automation solution that requires advanced technical knowledge. However,