How Will AI Robots Disrupt the Manufacturing Industry?

How Will AI Robots Disrupt the Manufacturing Industry?

Artificial intelligence has brought in a new generation of robotics technology: Robotics 2.0. The principal challenge is the transformation from original manual programming methods to true autonomous learning. Faced with this challenge for innovation in AI robotics, how can Taiwan's manufacturing industry best seize the opportunity?
Published: Aug 12, 2021
How Will AI Robots Disrupt the Manufacturing Industry?

What is AI-enabled Robotics?

Changes in the field of robotics and AI, are causing manufacturers to change from automation processes traditionally used in production, to processes using autonomous learning. Beyond the robot’s ability to handle routine tasks, robots can now also respond to changes in input given by humans and the environment.

The current state of manufacturing automation

According to a recent report issued by the International Federation of Robotics (IFR), global shipments of industrial robotic arms set a record in 2018, reaching 384,000 units. Among the major importing countries, China was the largest market (accounting for 35%), followed by Japan and the United States. Taiwan ranked sixth in the world.

Automobile and electronics manufacturing is still the largest application market for industrial arms, accounting for about 60%, which is higher than other industries such as metals, plastics, and food.

Due to the limitations of traditional robots and computer automation, at present, except for the automotive and electronics industries, almost none of the warehousing, agriculture, and other industries have begun to use robotic arms. This situation will be changed by new technologies such as AI robots and deep learning. Automation and industrial robotic arms have been in the manufacturing industry for decades, but even the most automated automobile manufacturing industry is still a long way from the so-called lights-out factory. For example, most parts of car assembly are still done manually. This is also the most labor-intensive part of the car factory. On average, two-thirds of the employees in an automobile factory are in the assembly workshop.

Why is full automation so difficult?

Technical limitations that automation has not been able to overcome so far.

Today's automated production lines are generally designed for mass production. They can effectively reduce costs, but lack flexibility. As consumers prefer products with shorter and shorter life cycles, the demand for customized production increases. Humans are often more capable of responding to these new product lines than robots, as they do not need to spend a lot of time to rewrite programs or change manufacturing processes.

  • Dexterity and complexity
    Despite rapid advances in technology, humans are still much more dexterous than robots. Although assembly processes have been highly automated, they still need to be programed using manpower.
    Material preparation required in manufacturing and warehousing is an area where production efficiency can be improved. In the process of assembling, all the parts needed for assembly can be placed in a tool kit. The robot can then take each part from the toolkit and perform assembly operations. If each part is in its fixed position and angle, automatic programming is relatively easy. On the contrary, where it is necessary to identify and retrieve parts from disordered parts boxes, it is a challenge to existing machine-vision and robotics technology.
  • Visual and non-visual feedback
    Many complex assembly operations require the experience or feeling of the operator. Whether it's installing a car seat or putting parts in a toolkit, these seemingly simple actions require operators or robots to receive and adjust the angle and strength of the actions based on various visual and even tactile signals.
    These fine adjustment requirements make traditional automated programming almost useless, because every time you pick or place an item, the process is not the same. You need to have the ability to learn from multiple attempts, and summarize the required action needed, as a person would. However, machine learning, especially deep and reinforcement learning, can bring major changes to robotics.

Robotics 2.0: What can AI enable production line robots to do?

The biggest change that AI has brought to robotic arms is: In the past, robotic arms could only repeatedly execute programs written by engineers. Although the accuracy and speed were high, they could not cope with any environmental or process changes. But now because of AI, machines can learn more complex tasks on their own.

Specifically, AI robots have major breakthroughs in three major areas compared with traditional robotic arms:

  • Vision System
     Even the highest-end 3D industrial camera still cannot judge depth and distance accurately like the human eye can. They also cannot identify transparent packaging, reflective surfaces, or deformed objects. Machine vision has made great progress in the past few years, using deep learning, semantic segmentation, and scene understanding to improve the depth and image recognition of low-end cameras. This has allowed manufacturers to obtain sufficiently accurate images without using expensive cameras. This image recognition can successfully identify transparent or reflective object packaging.
  • Scalability
    Deep learning does not need to construct a 3D model of each item in advance like traditional machine vision. Just input the picture and after training, the artificial neural network can automatically recognize the objects in the image. It can even use unsupervised or self-supervised learning to reduce the need for manual labeling of data or features. This allows the machine to learn in a way closer to how humans do, eliminating human intervention, and allowing the robot to face new tasks without the need for engineers to rewrite programs. With the continued operation of the machine, more and more data are collected, and the accuracy of the machine learning model is further improved.
    Since deep learning models are generally stored in the cloud, robots can learn from each other and share knowledge. This not only saves on the learning time of other machines, but also ensures the consistency of quality.
  • Intelligent Placement
    "Please handle with care, or arrange the items neatly", this is a huge technical challenge for the robotic arm.
    How to define "handle with care"? Does it stop applying force the moment the object touches the desktop? Or is it moving to a certain distance and letting go to let the object fall naturally? This is a test of the technology.
     It is even more difficult to "arrange items neatly." In order to accurately place items at the desired position and angle, we must first pick up items from the correct position. The robotic arm is still not as dexterous as a human hand. Most robotic arms use suction cups or clamps, and there is still a long way to go to achieve the flexibility of human joints and fingers. Secondly, we need to be able to instantly determine the angular position and shape of the object being gripped. We need to know where other objects or obstacles are in order to judge where to place items to save the most space.
    Through AI, the robot arm can judge depth more accurately, and can also learn to improve through training. Items can be placed face up, face down, or in other varying positions. You can also use Object Modeling, or Voxelization, to predict and reconstruct 3D objects so that the machine can more accurately determine the size and shape of the actual object, and place the object in the proper position.

How will AI robots disrupt the manufacturing industry?

Existing players in the industry generally choose to focus on continuous innovation and improve existing products and services in order to serve existing customers. At this time, some small companies with fewer resources can seize the opportunity to target neglected markets and gain a foothold in these markets. AI robots will bring disruptive innovation to the manufacturing industry.

Disruptive innovation is divided into the following two types: low-level market innovation and new market innovation. What AI robots bring is disruptive innovation to new markets. New market innovation refers to innovation brought about by new companies aiming at new markets which existing companies have not yet served.

With the automotive and electronic manufacturing industries accounting for 60% of industrial robotic arms, many manufacturers focus on continuous innovation to do what they are best at and what customers need most to further improve speed and accuracy. Warehousing, food manufacturing, and material preparation procedures have been neglected. Customers are not lacking high-speed and high-precision robotics, but they are looking for robotic arms that are more flexible and able to learn to perform differing tasks with flexibly. Seeing this unmet demand, AI Robotics Company began to apply artificial intelligence to robots so that robotic arms could be used in new markets such as material preparation, packaging, and warehousing. Lower-level cameras used in machine learning models automate procedures such as material preparation and cargo sorting whuch could only be done manually in the past. Robotic arms can be used in more places, and over a vast range of industries.

Challenges and opportunities brought about by AI robotics

The combination of AI and robots brings many possibilities, but these changes will by no means come overnight. Even if robotic arm companies begin to invest in AI, they must also think about how to rebuild their organization and development strategies to minimize the negative impact of transformation and meet the demand presented by each company's management.

On the other hand, developing new markets is by no means a simple matter. Start-up companies still need to work closely with manufacturers to develop solutions that better meet customer needs. The manufacturing process is even more complex and diverse than warehousing. Although start-up companies understand AI and robotics technology, they do not necessarily understand the manufacturing process. This also gives Taiwanese manufacturers the best opportunity to seize the opportunity to grow and transform.

Taiwan's development of artificial intelligence in the manufacturing industry not only brings the advantages of understanding application cases and mastering data, but also achieves the goal of industrial transformation by using new technologies such as AI robotics.

If Taiwanese manufacturers can take the lead in cooperating with these start-up companies, they can not only improve production efficiency and quality through process automation, but can also provide customized solutions for processes that were difficult to perform in the past. By moving away from the strategies of mass manufacturing and price-cutting competition, international startups can become a testing ground for a new generation of AI robots, and develop exclusive solutions for the electronics or semiconductor manufacturing industry, thereby increasing export production.

Published: Aug 12, 2021 Source :bnext

  • Technology Industry
  • Automation Industry
  • Manufacturing Industry
  • Smart Industry
  • Tech Industry
  • IT Industry
  • Asia / Pacific
  • Global
  • Smart Technology
  • Smart Transformation
  • AI
  • Industrial Robots
  • Automated Manufacturing
  • Smart Manufacturing
  • Robotic Arm

Further reading

You might also be interested in ...

The Characteristics of Non-Woven Fabric and its Use in Agriculture
Non-woven fabrics have been produced in the early stages of the development of the petrochemical industry. At that time, they only used their softness and bulkiness for packaging, covering, filling, and other materials. Due to the rapid development of materials science, the application scope of non-woven fabrics has expanded to various fields of industry and commerce, civil engineering, medical treatment, agriculture and environmental engineering, and its existence can be seen everywhere.
The Growth Driver of the Global Cloud Server Market
The cloud server market is a cloud infrastructure service that allows service providers and end-users to use virtual networks to build architectures.
Ultra-Precision Machining System Technology, Cross-Domain Integration Added Value
Superfinishing machining technology is an important support technology for modern high-tech warfare, and is the development foundation and development direction of modern high-tech industries and science and technology.
A Zero-Waste Era that Considers Economic Growth
In the past, excessive consumption, wanton mining, indiscrete manufacturing, and careless abandonment of wastes, led to the rapid depletion of natural resources. Now, the pursuit of a circular economy, with the goal of zero waste through recycling of resources, is being implemented in an attempt to solve environmental problems.
The Trend of the Food Industry in 2021
The capital expenditure for automation in the global food and beverage industry is approximately US$19 billion. This includes 9 billion US dollars in process equipment automation, 7 billion US dollars in packaging and material handling equipment, and the remaining 3 billion US dollars in production line automation.
Explore Opportunities in the Maternal and Child Market
The market for women and infants will fluctuate with changes in the market population, and consumption patterns will also respond to changes in society, moving towards online and offline channels for simultaneous sales. Consumers will also pay more attention to the safety, comfort, and fashionable compatibility of products.
Four Major Markets Drive the Growth of Global Communications Industry Output Value
The four major markets of "consumer", "5G telecommunication network", "enterprise network" and "data center" will drive the growth of the global communications market in 2021.
The Printing Industry Under the Influence of Internet Technology and Digital Media
The printing industry is very complex. It not only has many internal sub-industries but also involves many upstream and downstream related industries. It can be said that it is a huge industry system. The printing industry has a long history of development. Starting from the invention of printing in ancient China, printing technology has continued to innovate, forming a large industry, which we are almost engaged every day. Digital printing, 3D printing, other new emerging printing technologies will overturn and play new roles in printing industry markets.
Integrate Technology to Build a Strong Machine Tool Industry Ecological Chain
In the face of rapid changes in the international situation, seeking to expand exports and increase the added value of exports, we must also consider how to build a stronger industrial chain through industrial structure adjustment and international market layout strategies to improve the overall industry’s ability to withstand external shocks.
Combining the Advantages of the Optoelectronic Industry, and Upgrading the Manufacturing of the Technology Industry
The optoelectronic industry is one of Taiwan’s important technological industry development industries, and includes many daily visible technological products. For example, a smartphone with more than one hand includes LED flash lighting, solar chargers, display panels, and touch panels. Control panels, optical lenses, image sensors, surface-emitting lasers, infrared sensors, laser focus sensors, and other upstream optoelectronic related products, as well as LiDAR technology in self-driving cars, are all optoelectronic related products that are cutting-edge, key components that are indispensable in technology products.
How to Deal with the Recycling of Used Batteries in Electric Vehicles?
The world is studying how to deal with the increasing number of discarded electric vehicle batteries.
Global New Plastic Economy Commitment
Plastic issues cover a wide range from waste such as bottles and cans to microplastics and are inseparable from the lives of the general public. The environmental problems caused using many plastics have long been widely recognized around the world. Blindly recycling can no longer solve the huge plastic pollutants. Seeking a comprehensive transformation of the system is the fundamental way.