By using machine learning, digital twin, virtual reality, and several other cutting-edge technologies, intelligent manufacturing creates optimal production conditions. This article provides a comprehensive review of seven forms of intelligent manufacturing, hoping to give you an in-depth understanding of this new manufacturing model and how it drives innovation and business growth.
3D Printing
3D printing is a process that allows a three-dimensional solid object to be created from a digital file. 3D printing was first developed in the 1980s and originated as a rapid prototyping tool. However, 3D printing evolved to be a more practical and powerful tool with technological advances and has already been adopted in various fields.
The applications and use cases vary across industries but broadly include tooling aids and visual and functional prototypes. It is worth mentioning that the consumer electronics and automotive sectors each contribute 20% of the total 3D printing market share.
3D printing is a more feasible option for general manufacturing use than most other technologies due to reduced costs in equipment and materials in recent years.3D printing technology is an ideal solution for several manufacturing cases. For industries like aerospace and defense, where highly complex parts are produced in low volumes, 3D printing is a perfect match. Using the technology, complex geometries can be created without investing in expensive tooling equipment, and the production line is more straightforward than traditional manufacturing methods.
Alongside low-volume manufacturing processes, 3D printing technology works wonder in weight-reduction cases. Weight is one of the most important factors to consider when it comes to sophisticated products, such as aircraft design and electronic goods. This is where 3D printing comes in: the technology is perfect for creating lightweight parts, especially with design optimization tools. Reducing the weight of products can significantly contribute to lowering payload and production complexity.
Virtual Reality
virtual reality refers to using computer technology to create realistic user simulation experiences. In simpler terms, virtual reality transforms a computer into a three-dimensional world by triggering the simulation of human senses, breaking the limitations of time and location. Virtual reality's most immediately recognizable component is the head-mounted display. Virtual reality applications include healthcare training, entertainment activities, education programs, business meetings and more.
One application of virtual reality changing manufacturing is providing safety training without exposing workers to danger. The manufacturing process could be dangerous, as some equipment requires special training. The use of virtual reality allows workers to experience the complex and hazardous processes involved in production in a safe place, which can prevent incorrect operations that may be potentially dangerous to them.
Another application of virtual reality technology is product design. In manufacturing, the design process almost always includes the prototyping stage. This is where virtual reality shows its real value. The cost and efforts of moving the prototyping to virtual reality are much lower than building a line of physical prototypes, especially when the prototype needs some additional refining.
3D Machine Vision
3D machine vision technology should be considered one of the essential technologies for achieving intelligent manufacturing. It utilizes various digital sensors and cameras to create 3-dimensional images of an object. The performance of 3D machine vision tends to be superior to the human eye due to the ability to operate 7/24 and examine a large number of objects in high-speed production lines.
Manufacturers widely adopt this technology in chemical, automotive, plastics, metal forming, food and other industries. The list goes on and on and even expands beyond manufacturing. The business benefits of adopting 3D machine vision include minimized human errors, boosted productivity, reduced machine downtime, and tighter process control.
China accounted for a huge market base for 3D machine vision, and players in this industry contribute significantly to the digital transformations of other manufacturing companies. Feizheping, the founder of Percipio.XYZ stated that intelligent manufacturing is a future trend. The underlying logic of smart manufacturing is replacing labor resources with machines. Industrial insiders have already utilized machines or robots to reduce the workload or lower the work intensity for workers, which indicates insufficient developed intelligent manufacturing processes and huge market potential. True intelligent manufacturing should be achieved through the large-scale machine substation of more technical work.
Robots
Though existing for decades, robotic technologies are a relatively new concept for some manufacturing companies.. By freeing human employees from repetitive or hazardous tasks, robots have changed manufacturing in myriad positive ways. The robots-assisted manufacturing process contributes to increased productivity, boosts the bottom line, higher return on investment, additional workplace safety and greater precision and objectivity.
The applications of robots in the manufacturing process could be divided into three main categories: material handling, processing operations, and inspection. Robotic automation in the manufacturing guarantees an efficient and reliable way to offshore and fill the skills gap in areas where it may be difficult to recruit the necessary employees, delivering strong business benefits. Additionally, fully autonomous robots in manufacturing are ideal for high-volume, repetitive processes, where the speed, accuracy and durability of a robot are highly valued. On the other hand, when employees are freed from tasks that robots can efficiently perform, they can utilize more of their energy and time to contribute knowledge and ideas within higher organizational roles.
Machine Learning
Machine learning is a type of artificial intelligence that makes algorithms more accurate and reliable in predicting outcomes. There are four standard learning approaches: supervised, unsupervised, semi-supervised, and reinforcement learning. Historical data are often used as input in this process to predict new output values. Researchers choose different algorithm data based on the type of data that they want to predict. After the data selection, the machine learning process begins with observing these data sets and then proceeds to make inferences based on the data provided. The ultimate goal of machine learning is to allow computers to learn and identify patterns autonomously with minimum human intervention or assistance and adjust actions.
Machine learning has proven valuable as it provides specific trends or patterns of customer behaviors and business operations for future development for enterprises. More importantly, with massive amounts of computational ability, machine learning can solve problems at a speed and scale that cannot be duplicated by the human mind alone. Many of today's leading companies, such as Alibaba, Baidu and Tencent, make machine learning a central part of their strategic plan.
Edge Computing
Edge computing is a networking technology that transforms how raw data is stored, processed, analyzed and transported. More specifically, edge computing aims to reduce latency and bandwidth costs associated with moving raw data from where it was created to a centralized data center or the cloud. The whole process is achieved by running fewer processes in the cloud and moving those processes to local places, such as IoT devices or local edge servers.
Recently, the rise of real-time applications that require minimal response time and better bandwidth availability, such as autonomous vehicles and 5G networks, is driving the growth of edge computing. Edge computing allows manufacturers to filter data and monitor the condition of their assets remotely faster and more reliably, feeding innovation and unlocking the limitations of the data within the business.
Liu Jiangchuan, the founder of Jiangxing Intelligence, said that edge computing is the cornerstone of the fourth industrial revolution. The industrial internet drives the high-speed growth of big data and cloud computing technologies, bringing revolutionary breakthroughs in information transmission and data computing. While edge computing would be the catalyst for the convergence of 5G, industrial internet and artificial intelligence.
Digital Twin
The digital twin is a digital copy of any process, product, system, person, asset or real-life scenario. The digital twin is created using data from the real-world component and could mirror what happens to the object in real time. Adjustments can be made to the digital twin in any phase of the product lifecycle to see how the status and functionality of the original system would change in real life, enabling more knowledge and deeper visibility of the production process and better business objectives.
A viable digital twin could help engineers and data scientists in the following ways. First of all, operational teams can use a digital twin to identify variances that indicate the need for preventative repairs or maintenance before a serious problem occurs. Secondly, digital twins make cross-discipline collaboration and sharing easier. A digital twin could put the most important and straightforward operational data to people with different backgrounds, facilitating them to gain deeper insights into other team members' work. Improved communication and collaboration are achieved based on their more profound understanding. Lastly, a digital twin could lead to improved customer experience. As a digital twin can automate data pulling, cleaning, structuring, and transforming in a way that benefits the operational team to solve problems and determine which input variables make the most significant impacts on customer experience, it can deliver insights into product performance and move the company forward.
By adopting intelligent manufacturing methods properly, a firm's competitiveness can be significantly enhanced. However, to realize the true potential of the intelligent manufacturing process, manufacturers must stop relying on legacy systems and place intelligent manufacturing at the heart of their business plans.