Artificial intelligence in textile industry
Artificial Intelligence (AI) is a branch of computer science dedicated to the study of computational activities that requires intelligence when performed by humans. "Intelligence" is the computational part of the ability to achieve goals. The main purpose of AI systems is to perform functions that, if supposed to be performed by a human being, would be considered intelligent. It is a very broad concept, and one that receives so many definitions when we give the word intelligence different meanings.
another definition based on ISO organization:
Artificial intelligence is “a technical and scientific field devoted to the engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives” [ISO/IEC 22989:2022]. While this definition of artificial intelligence is accurate from the technical perspective, how does it translate for the average person?
In truth, AI is just a practical tool, not a panacea. It’s only as good as the algorithms and machine learning techniques that guide its actions. AI can get really good at performing a specific task, but it takes tones of data and repetition. It simply learns to analyze large amounts of data, recognize patterns, and make predictions or decisions based on that data, continuously improving its performance over time.
Today, this AI meaning has evolved beyond mere data processing to include the development of machines capable of learning, reasoning and problem-solving. The machine learning has become so “competent” as to generate everything from software code to images, articles, videos and music. This is the next level of AI, the so-called generative AI, which differs from traditional AI in its capabilities and application. While traditional AI systems are primarily used to analyze data and make predictions, generative AI goes a step further by creating new data similar to its training data.
In essence, AI analyses data to extract patterns and make predictions. It does this by combining large datasets with intelligent AI algorithms – or sets of rules – that allow the software to learn from patterns in the data. The way the system accomplishes this is through a neural network – an array of interconnected nodes that relay information between various layers to find connections and derive meaning from data.
To grasp how this works, we must unpack the following concepts:
- Learning: AI’s machine learning feature enables machines to learn from data, identify patterns and make decisions without explicit programming. Going one step further, advancements in deep learning empower AI software to understand more complex patterns using millions of data points.
- Reasoning: The ability to reason is crucial to AI because it allows computers to mimic the human brain. AI can make inferences based on commands it is given, or other available information, to form hypotheses or develop strategies for addressing a problem.
- Problem solving: AI’s problem-solving capability is based on the manipulation of data through trial-and-error techniques. It involves using algorithms to explore various possible paths to find the most optimal solution to complex problems.
- Processing language: AI uses natural language processing – or NLP – to analyse human language data in a way that is meaningful to computers. What is NLP? It refers to the ability of computers to understand, interpret and generate human language, using text analysis, sentiment analysis and machine translation.
- Perception: AI scans the environment through sense captors such as temperature sensors and cameras. Known as computer vision, this field of AI enables machines to interpret and understand visual data and is used in image recognition, facial recognition and object detection.
Machine Learning is a subfield of Artificial Intelligence (AI) and its main approach is to learn through experience to find patterns in a data set. It involves teaching a computer, through examples, to recognize patterns instead of programming it with specific rules.
In Machine Learning there are three main classifications with respect to the algorithms used and the type of learning of the same, namely:
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
The choice of which of the three methods will be used will depend on the data set you have and the problem you want to solve.
One of the applications that has been widely used at an industrial level is the use of vision systems, which allows the analysis and recognition of patterns in fabrics in the textile industry. One of the companies that uses this technology is COGNEX. For this, it uses a platform called Cognex ViDi that analyzes patterns in textile fabrics, such as finishes. The main objective is to integrate this technology in an industrial textile system in order to obtain predefined images of fabric samples, to carry out two analyzes.
The first analysis will be more preliminary to detect more visible defects and, at a later stage, a more detailed analysis of defects that are not visible to the naked eye.
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This renewal meets new market needs in order to add value to the product while rethinking costs. Some biological processes applied in the dyeing phase, for example, can reduce, on average, 30% in water and electricity consumption.