Gartner Top 10 Strategic Technology Trends 2024

The use cases above prove that AI has immense potential in the manufacturing sector. Of course, the manufacturers themselves can benefit from its implementation – but so can the economy and environment. First, it can serve research purposes, allowing the companies to come up with new materials that carry desirable properties while being biodegradable or fully recyclable. In addition, it can help them optimize the usage of resources to minimize waste. Nonetheless, despite continual advances in computer processing speed and memory capacity, no programs can match human adaptability in larger domains or occupations requiring a high degree of everyday knowledge. Since the invention of the digital computer in the 1940s, it has been demonstrated that computers can be trained to execute exceedingly complex tasks with great competence, such as proving mathematical theorems or playing chess.

This type of online algorithm demonstrates the ability to realize real-time performance without the penalty of requiring labeled data from training phases. Moreover, the use of AI in the manufacturing industry has also revolutionized predictive maintenance. By analyzing real-time data from sensors and equipment, machine learning algorithms can predict equipment failures and recommend proactive maintenance actions. This proactive approach minimizes downtime, reduces maintenance costs, and ensures optimal equipment performance. Meanwhile, the adoption of artificial intelligence is trailing behind at only 29%.

AI is Transforming the Manufacturing Industry: Pros and Cons

With the advent of AI and ML, factories are experiencing a paradigm shift in terms of efficiency, productivity, and cost-effectiveness. Artificial intelligence is revolutionizing the manufacturing industry with its transformative capabilities. Manufacturing companies are leveraging the power of AI to enhance efficiency, accuracy, and productivity across various processes. Another AI advantage is that manufacturers can create better ways of designing their products.

• It is reported that the system can now automatically and accurately detect and classify any defect that would otherwise require a manual procedure, and is able to reduce about 80% of total workers’ time.
• Rather than monitoring these data points externally, the part itself will check in occasionally with AI systems to report normal status until conditions go sideways, when the part will start demanding attention.
• It reduces lost sales due to out-of-stocks by 65% and warehouse costs by 10 to 40%.
• The system is built on a semantic network as the basis for a natural language processing system comprised of automatic speech recognition, visual simulation environment, and reasoning.
• Most manufacturing companies contend with high capital investments and slim profit margins, which is why cost savings are critical to success.
• With the lifecycles of products constantly changing, factory floor layouts should be fluid too.

After they’ve been processed, they can spot any potential issues that may appear. The eCommerce giant has also been working with AI-driven Kiva robots, which work on the factory floor, moving and stacking bins. These robots can also carry, transport and store merchandise that’s as heavy as 3,000 lbs. But with so many tasks to complete, including inventory audits, tagging and labeling, avoiding the kind of errors that can have a detrimental effect on the whole supply chain is far from easy. Ultimately, using computer vision for PPE detection in the manufacturing industry helps to reduce workplace accidents while saving a company money, and it also lowers insurance premiums and it can promote a better working culture. Computer vision solutions like APRIL Eye are rectifying these issues, using image classification and object detection algorithms trained on super massive datasets to verify date and label codes at speeds of 1000+ packs per minute.

Machine Learning and Autonomous AI

4 as an organizing framework to map AI/ML technologies to existing and potential industrial HRC applications and find common themes across problem types and corresponding AI/ML solutions. Industrial AI has led to a proliferation of simulation across production, assembly, performance, inventory, and transportation. It’s the reason most of the benefits explored in this article are possible.

Historians track human progress from the Stone Age through the Bronze Age, Iron Age, and so on, gauging evolutionary development based on human mastery of the natural environment, materials, tools, and technologies. Humankind is currently in the Information Age, also known as the Silicon Age. In this electronics-based era, humans are collectively enhanced by computers, leverage unprecedented power over the natural world, and have a synergistic capacity to accomplish things inconceivable a few generations ago. When deploying AI, everyone is talking about the cloud because it’s an easy way to access computing resources, which provide virtual equipment by combining CPUs, memory, and disks to create virtual machines, with minimal maintenance. They store your data pretty cheaply, but when you start using computing resources, it becomes a lot more expensive. You want the ability to scale across different cloud providers or storage solutions, whichever is most cost effective.

A Human Perspective – Global Business in the Post COVID-19 World and The New Norm

With this predictive setup, companies can now easily apply a predict-and-fix maintenance model. The guesswork regarding what is wrong with the equipment or process is eliminated. Rather than stop the whole production to detect-and-fix the problem, AI predictions pinpoint AI in Manufacturing anomalies more quickly. In addition to manufacturer hesitancy, there is currently a lack of skills to support this technology. IBM predicts that demand for data scientists will grow by 93% in the coming years, and demand for machine learning experts will grow by 56%.

With increased process and data complexity, manual feature extraction becomes difficult. DNN-based ML, which allows automated learning of features specific to each task, has attracted increasing attention. Beyond rotating machinery, ML techniques also contributed to improved fault recognition capability in other manufacturing equipment. In Ref. [104], faulty tonnage in a stamping machine is detected by SVM using vibration signal features extracted by a recurrent plot (RP) method. In Ref. [105], a novel method for detecting filament breakage and nozzle clogging in fused deposition modeling (FDM) has been developed, with an SVM as the condition classifier. The contribution of the work is the Bayesian Dirichlet method to effectively characterize the sensing signals.

Transform your business with AI

Ted Plummer, principal product manager and resident AI expert at industrial 3D printing company, Markforged. It’s painful and expensive to migrate once you have all your data in a single cloud provider. Precisely for this reason AI makes possible, within the industry and beyond, to implement a restructuring intervention that will bring benefits and improvements without comparison. Clients receive 24/7 access to proven management and technology research, expert advice, benchmarks, diagnostics and more. Chief Visionary Officer & Co-Founder of CloEE, an IoT SaaS for industrial enterprises to measure CO2 and increase efficiency.

Thanks to sensors, organizations can gain better insights into production operations, uncover potential problems before they arise and drastically improve the productivity of their manufacturing assets. Manufacturers can measure and collect data on everything from temperature and humidity to motor speed, flow rate, and more. Once sensor data is collected, it’s streamed to an IIoT solution, where the data is put to work training and improving ML models that represent the digital equivalent of the manufacturing system.

Transformation and Optimization of Processes

With predictive maintenance, organizations can see into the future and understand exactly when a piece of equipment will likely fail. Until recently, preventative maintenance was the go-to practice for manufacturers. However, with AI technology, organizations can implement predictive maintenance programs. Manufacturers must maintain production levels and customer satisfaction throughout the process.

These AI applications could change the business case that determines whether a factory focuses on one captive process or takes on multiple products or projects. In the example of aerospace, an industry that’s experiencing a downturn, it may be that its manufacturing operations could adapt by making medical parts, as well. AI has an important role in generative design, a process in which a design engineer enters a set of requirements for a project and then design software creates multiple iterations. Recently, Autodesk has collected large volumes of materials data for additive manufacturing and is using that data to drive a generative-design model.

Keras vs Tensorflow vs Pytorch: Understanding the Most Popular Deep Learning Frameworks

Traditionally, the objective of applying robotics in manufacturing has been to leverage the advantages robots have over humans such as repeatability, endurance, strength, ability to operate in hazardous environments, etc. Despite this, artificial intelligence, machine learning and cloud computing have yet to be widely adopted by industrial manufacturers. While it seems logical to control the massive amounts of complex industrial equipment needed for manufacturing with digitalization, technology is not replacing humans nearly as fast as it potentially could. Many companies are viewing the introduction of AI into the manufacturing industry with trepidation, as it requires a huge capital investment. Once intelligent machines begin to take over the daily activities of a factory floor, businesses will benefit through considerably reduced operating costs, with predictive maintenance helping additionally to reduce machine downtime.