Artificial intelligence (AI) is transfiguring operations across industries with its powerful algorithms, natural language processing, machine learning components, and beyond. We stand at the forefront of the 4.0 industrial revolution, where industries and businesses are emphasizing automation and machinery over manual tasks of manufacturing and workforce optimization. Artificial intelligence in manufacturing is a shifting paradigm that has changed how products are designed, manufactured, and even distributed across the global supply chain.
As the Industrial Revolution 4.0 continues to turn the world upside down, more technologies are taking over human tasks. AI is one such technology that is said to be on the verge of replacing humans sooner when it comes to manufacturing. From generative AI to digital twins, this technology is dominating the global manufacturing sector in an unprecedented manner. Ahead of 2025, AI is expected to bring some more revolutionary changes that would reshape the manufacturing realm like never before.
This article will guide people on how this ground-breaking technology is reimagining manufacturing procedures and what we can expect from it in the near future. Let us take a look at the game-changing contributions of this technology to the manufacturing sector worldwide.
Artificial Intelligence: In Pursuit of Industry 4.0
AI is now at the frontier of pursuing Industry 4.0. Over the past few years, the collection of Big Data through the Internet of Things (IoT) has accelerated the growth of information retrieval and analysis techniques, widely known as artificial intelligence or AI. Such advancements are revolutionizing many manufacturing industries throughout the world and acting as the driving force behind the establishment of smart factories, where operations are executed intelligently and automatically throughout the cycles of the manufacturing process.
While referring to specific goals in the manufacturing industry, the term ‘Industrial AI’ was coined. It spans machine learning, where pattern recognition for highly non-linear data, fast computation, robustness to repetitive tasks, unstructured data analysis, and high interoperability are keys.
On the other hand, deep learning is gradually replacing traditional data analysis techniques within the manufacturing domain. Today, it is capable of capturing complex data patterns while training on data. This makes it successful in natural language processing, object detection, realistic image synthesis, and speech recognition. However, despite its huge potential, some manufacturing firms are still unwilling to adopt it directly to manufacturing sites as a result of a lack of awareness about its ideal incorporation.
Artificial Intelligence in Manufacturing Operations: A Review
AI and Machine Learning (ML) can notably improve manufacturing productivity, efficiency, and sustainability. However, its application is not without challenges, including issues with human resources, data acquisition and management, infrastructure, security risks, and several others. Despite these challenges, there is no doubt that AI is revolutionizing manufacturing operations in a way that will leave lasting positive impacts on profitability, the environment, and operations altogether. With this review article, we will come to know about various applications of AI in the manufacturing sector.
Applications of AI and ML in Manufacturing Operations
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Operations
‘Operations’ in manufacturing is a broader term that embraces the use of production facilities and resources, from predictive maintenance to process optimization. The predictive and analytic capabilities of AI and ML models offer useful insights to human operators to facilitate planning, improve manufacturing efficiency, support real-time decision-making, and ensure safety.
Predictive Maintenance
Under the planning phase of the manufacturing operations, predictive maintenance involves the analysis of sensor data drawn from equipment to anticipate any potential equipment failures and schedule their maintenance to prevent unwanted downtime.
According to a USA study, Typical Manufacturing Facilities have an average downtime of 15 hours per week, with losses of nearly $20,000 per minute of production line interruption for large automobile companies. Use of predictive maintenance can reduce such downtime beforehand to ensure uninterrupted production.
Quality Assurance
Quality assurance in manufacturing is crucial for customer satisfaction. With AI models, manufacturing firms can prevent quality failures to improve customer satisfaction. The use of Convolutional Neural Networks (CNNs) is now outperforming traditional quality control methods, particularly in additive manufacturing. Check for AI models in manufacturing can detect random defects in wafer maps and electron microscope images used in predicting the semiconductor performances.
Supply Chain Management
One of the most crucial parts of manufacturing, supply chain management involves complexities as it spans multiple countries for global production companies. With the use of predictive analytics and real-time data analysis, the complex tasks of supply chains can be optimized, inventory levels can be managed effectively, and production can be planned efficiently. Also, the use of digital twins in large-scale manufacturing operations is used to create virtual replicas of manufacturing units, test minor changes to system designs, and more.
Security
Deep learning-based computer vision can visually determine unsafe employee behaviors and identify the presence of unauthorized personnel within a manufacturing facility. AI models can also improve equipment safety within factories through intelligent access control systems to ensure workers’ safety and reduce downtime.
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Design
AI or ML-based predictive models ensure productivity enhancement for product designers, simplify workflow, and escalate implementation, eliminating the drawbacks of costly experiments. Designs generated using AI tools can cater to user requirements, and such tools are developed on Generative models. They can also be used to make modifications. Among these models, GANs are the most popular due to their ability to consistently generate high-grade image data, and they also allow multi-modal inputs in the design process. In terms of experimentation, the ML models are used to simulate experiments, optimize variables, and predict accurate outcomes in comparison to traditional experiments.
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Automation and Machine-Human Interaction
AI can assist in dealing with shortages of human expertise by enabling robots to copy human competencies. Robots in manufacturing can perform tasks such as autonomous vehicle management, package moving, and more. They are fundamentally used for work that is too complex for humans to perform. Using NLP, the robots copy human behavior and support them in executing production tasks.
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