By using machine learning, digital twin, virtual reality, and several other cutting-edge technologies, intelligent manufacturing creates optimal production conditions. This article will give you a thorough overview of seven forms intelligent manufacturing.
3D printing
3D printing is the process of creating a 3-dimensional solid object from a digital file. 3D printing originated in 1980s as a quick prototyping tool. 3D printing was a practical tool that allowed for faster prototyping and has since been used in many fields.
Although the applications and use cases are different across industries, they all include tooling aids as well as visual and functional prototypes. It is worth noting that the market share for 3D printing in the consumer electronics and automotive industries is 20% each.
3D printing is more practical than other manufacturing technologies because of its lower cost. 3D printing works well in industries like aerospace or defense, which require high-quality parts to be produced in low quantities. 3D printing allows for complex geometries without needing expensive tooling equipment. The production line is also simpler than traditional manufacturing processes.
3D printing technology is a great tool for weight reduction, along with low-volume manufacturing. Weight is an important factor to consider when designing sophisticated products like aircraft designs and electronic goods. 3D printing is a great technology for making lightweight parts. Product weight reduction can make it easier to reduce production costs and payload.
Virtual Reality
Virtual reality is the use of computer technology to create realistic simulation experiences. To put it simply, virtual reality allows a computer to become a 3D world through the activation of the human senses. This breaks the limits of location and time. The head-mounted display is the most prominent component of virtual reality. Virtual reality applications can be used for entertainment, healthcare training, education programs, business meetings, and many other purposes.
Virtual reality is a way to provide safety training, without putting workers in danger. Some equipment is very sensitive and could pose a danger to workers. Virtual reality allows workers to see the dangerous and complex production processes in a safe environment. This can help prevent them from making mistakes that could be potentially hazardous.
Another use of virtual reality technology in product design is Prototyping is a crucial part of manufacturing. Virtual reality is here where it really shines. Virtual reality is much cheaper than creating a series of physical prototypes. This is especially true if the prototype requires further refinement.
3D Machine Vision
Intelligent manufacturing requires 3D machine vision technology. This technology uses a variety of digital cameras and sensors to create 3-dimensional images. 3D machine vision is more efficient than the human eye because it can operate around the clock and inspect large numbers of objects at high speed.
This technology is widely used by manufacturers in the chemical, automotive, plastics and metal forming industries, as well as food. This technology is used in many industries, including manufacturing. 3D machine vision can bring business benefits such as reduced human error, increased productivity, decreased machine downtime, tighter process control, and reduced human errors.
China was the largest market for 3D machine visualisation, and these players are crucial to digital transformations in other manufacturing companies. Percipio.XYZ’s founder Feizheping said that intelligent manufacturing is a new trend. The fundamental idea behind smart manufacturing is to replace labor resources with computers. Industrial insiders know that robots and machines have been used to decrease work load or reduce intensity. This indicates a lack of intelligent manufacturing processes and a huge market. The large-scale substation of more technical work is required to achieve true intelligent manufacturing.
Robots
Robotic technologies, although they have been around for decades, are still relatively new to some manufacturing companies. They free human workers from repetitive and hazardous tasks and make manufacturing more efficient. Robots-assisted production contributes to greater productivity, better return on investment and workplace safety. It also increases precision and objectivity.
Three main categories of applications for robots in manufacturing could be divided: material handling, inspection, and processing operations. Robotic automation in manufacturing provides a reliable and efficient way to outsource and fill skills gaps in areas that may prove difficult to hire the right employees. This can deliver strong business benefits. Fully autonomous robots are great for repetitive, high-volume manufacturing processes where speed, accuracy, and durability are important. However, employees who are not required to perform tasks robots can do efficiently can use their time and energy more effectively to share knowledge and ideas with higher-ranking organizational positions.
Machine Learning
Machine learning is an artificial intelligence type that improves the accuracy and reliability of algorithms in predicting outcomes. There are four types of learning methods: unsupervised, semi-supervised and supervised. In order to predict future output values, historical data is often used. Different algorithm data are chosen by researchers based on what type of data they wish to predict. After selecting the data, machine learning begins by observing the data and then making inferences using the data. Machine learning has the ultimate goal to enable computers to recognize patterns without any human intervention, adjust actions and learn from them.
Machine learning is valuable because it can provide specific patterns and trends of customer behaviors, business operations, and other information that will help enterprises develop in the future. Machine learning is able to solve problems faster than the human brain alone, and at a scale and speed that is unmatched by humans. Machine learning is an integral part of many leading companies like Tencent, Baidu, Alibaba and Baidu.
Edge Computing
Edge computing is a network technology that transforms the way raw data is stored and processed. Edge computing is a technology that reduces latency and bandwidth costs when moving raw data from the original location to a central data center or cloud. By running fewer processes on the cloud, and moving those processes to local places like IoT devices and local edge servers, this whole process can be achieved.
Edge computing has been growing due to the rapid growth of real-time applications, such as autonomous cars and 5G networks. These applications require minimal response time and have better bandwidth availability. Edge computing allows manufacturers the ability to quickly filter data and monitor their assets remotely. This allows them to be more innovative and unlocked the limitations of their data.
Jiangxing Intelligence founder Liu Jiangchuan said that edge computing was the foundation of the fourth industrial revolution. The rapid growth of big data, cloud computing technologies and the industrial internet is driving revolutionary breakthroughs in information transmission technology and data computing. Edge computing is the key to the convergence of 5G and industrial internet with artificial intelligence.
Digital Twin
The digital twin can be described as a digital copy of any product, process, system, person or asset. The digital twin is made using real-world data and can mirror what happens in real time to the object. You can adjust the digital twin at any point in the product lifecycle. The digital twin will show how the status and functionality would change in real-life. This gives you more information and allows you to better understand the business goals.
Engineers and data scientists could benefit from a viable digital twin in the following ways. First, operational teams could use a digital twin in order to spot variances that may indicate the need for preventative maintenance or repairs before a major problem occurs. Second, digital twins facilitate cross-discipline collaboration. Digital twins can help people from different backgrounds to access the most relevant operational data, which allows them to gain greater insight into each other’s work. Their deeper understanding can lead to improved communication and collaboration. A digital twin can also improve customer experience. Digital twins can be used to automate data cleaning, structuring, structuring, and transformation in a way that helps the operational team solve problems and determine which input variables have the greatest impact upon customer experience. They can provide insight into product performance, helping the company move forward.
A firm’s ability to compete can be greatly improved by properly implementing intelligent manufacturing methods. Manufacturers must abandon legacy systems in order to fully realize the potential of intelligent manufacturing. Instead, they should place intelligent manufacturing at their core business plans.