Technology peripherals
AI
How important is the role of artificial intelligence in Industry 4.0?
How important is the role of artificial intelligence in Industry 4.0?
The concept of Industry 4.0 is making waves in the technology industry, as manufacturing companies seek to leverage technological advancements to make their operations faster and more efficient. Since its introduction in 2011, Industry 4.0 has been a buzzword for the wave of process and technological changes sweeping the manufacturing industry.
It is somewhat interchangeable with digital factory and smart manufacturing, and makes many different changes to traditional manufacturing. In particular, these new factories and factory systems are more efficient because they are deeply instrumented, highly networked, extensively automated and fully data-driven.
Why Artificial Intelligence is an Important Pillar of Industry 4.0
Industry 4.0 relies on a wide range of technologies, including robotics/cobots, IoT, 3D printing, additive manufacturing, digital twins and analytics. Digital factories are filled with smart instruments that monitor or control every aspect of production and with highly granular data ranging from material quality to sub-millisecond status updates on machinery.
Artificial intelligence, including machine learning and generative and discriminative artificial intelligence, can create compelling value in most technical aspects of Industry 4.0. The value of AI often comes from raising the bar for automation by bringing a human-like level of understanding to software.
This reduces the number of places in the manufacturing process where people evaluate information and make decisions, which both reduces costs and increases productivity. Machine speed understanding can guide the robot's operation, for example, by slowing down, speeding up, or modifying its behavior to respond to changes in raw material quality, or the speed of other parts of the process.
In addition to the automation of production lines, artificial intelligence still has countless uses in the same type of environment. First, it can help build digital twins, another technology that speeds up the product development life cycle.
Secondly, artificial intelligence systems help leverage industrial IoT infrastructure, for example, by filtering event information to discover and predict potential production issues based on sensor data. AI can also aid production data analysis by revealing previously unseen patterns in production and usage data and then using this information to recommend design or process changes.
Artificial Intelligence Use Cases in Industry 4.0
Since artificial intelligence can help throughout the manufacturing process, there are many use cases in the Industry 4.0 environment. Early in the product life cycle, generative AI can help in the design phase as well as in the prototyping of physical objects through 3D printing or computer-controlled machining and additive manufacturing.
Generative AI systems can optimize designs to make them more efficient in their use of materials. For example, in apparel manufacturing, these systems control the cutting layout of garment panels on fabric bolts in a way that minimizes waste of fabric.
In other types of factories, AI systems can optimize the simplicity of manufacturing and assembling complex items by reducing the number of parts required for a design. Additionally, to speed up production, AI can reduce the number of individual cuts required to produce finished chair legs on a moving walkway, for example.
Other specific use cases currently in factories include:
- Nutella uses generative AI to design millions of unique packages for its products;
- In 3D printing , ADDMAN uses hybrid modeling tools combined with artificial intelligence to more efficiently design and prototype machine parts;
- FANUC’s factory produces computer numerical control machine tools that can learn from mistakes and improve control while operating;
- Collaborative robots can allow humans to work on production lines without humans being present, especially in oxygen-free environments or extremely hot temperatures;
- 3D Fiberglass factories like this work by responding to real-time events Speed up and slow down production lines or operations to monitor mechanical performance;
- Factories such as BMW use cameras and other sensors to monitor product quality and remove items as soon as a fault is detected to save energy and materials.
Computing capabilities, both on-site and in the cloud, will continue to increase, while artificial intelligence algorithms and technologies will not stop maturing. Manufacturers know this and understand they need the efficiency and responsiveness these tools provide in order to compete in the 21st century. As new manufacturers emerge and old factories are updated, AI will continue to expand its role in Industry 4.0.
The above is the detailed content of How important is the role of artificial intelligence in Industry 4.0?. For more information, please follow other related articles on the PHP Chinese website!
Hot AI Tools
Undresser.AI Undress
AI-powered app for creating realistic nude photos
AI Clothes Remover
Online AI tool for removing clothes from photos.
Undress AI Tool
Undress images for free
Clothoff.io
AI clothes remover
AI Hentai Generator
Generate AI Hentai for free.
Hot Article
Hot Tools
Notepad++7.3.1
Easy-to-use and free code editor
SublimeText3 Chinese version
Chinese version, very easy to use
Zend Studio 13.0.1
Powerful PHP integrated development environment
Dreamweaver CS6
Visual web development tools
SublimeText3 Mac version
God-level code editing software (SublimeText3)
Hot Topics
1385
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year
