Artificial intelligence: the driver of Industry 4.0

A lot of the hype surrounding artificial intelligence in manufacturing focuses on industrial automation, but that’s just intelligence An aspect of the factory revolution—a natural next step in the quest for efficiency. Artificial intelligence also brings the ability to reveal new business avenues for manufacturing tables. As part of the emerging Industry 4.0 paradigm, we will provide an overview of AI’s ability to drive industrial automation and open up new business opportunities. Additionally, we'll cover how manufacturers can use this powerful technology to increase efficiency, improve quality, and better manage their supply chains.

Artificial Intelligence Manufacturing Use Cases

#1: Predicting Quality and Yield

Reducing production losses and preventing inefficiencies in the production process have always been a priority for manufacturers across all industries challenges faced. This is as true today as ever-increasing demand meets increasing competition.

On the one hand, consumer expectations are high; global consumption habits are gradually "Westernizing" even as the population surge continues. According to multiple surveys in recent years, the global population will grow by 25% by 2050, equivalent to 200,000 new mouths every day.

On the other hand, consumers have never had so many products to choose from. Recent surveys suggest that this abundance of choice means consumers are increasingly likely to abandon their favorite brands permanently, for example, if the product is no longer on the shelf.

Given these trends, manufacturers can no longer accept process inefficiencies and their associated losses. Every loss in waste, yield, quality or throughput erodes their bottom line and gives competitors an extra inch - assuming their production processes are more efficient.

The challenge faced by many manufacturers, especially those with complex processes, is that they eventually hit a ceiling when it comes to process optimization. Some inefficiencies have no obvious root cause, leaving process experts unable to explain them.

Predict Quality and Yield uses AI-driven process and machine health solutions to uncover the hidden causes of many of the perennial production losses manufacturers face. This is accomplished through continuous multivariate analysis, using uniquely trained machine learning algorithms to gain insights into individual production processes.

The specific artificial intelligence/machine learning technique used here is called supervised learning, which means the algorithm is trained to identify trends and patterns in data. Automated recommendations and alerts can then be generated to notify production teams and process engineers of impending issues and seamlessly share critical knowledge on how to prevent losses before they occur.

#2: Predictive maintenance

Predictive maintenance is one of the most famous applications of industrial artificial intelligence. Rather than performing maintenance according to a predetermined schedule, predictive maintenance uses algorithms to predict the next failure of a component, machine or system and then alerts personnel to perform focused maintenance procedures to prevent failure. These alerts occur at the right time to avoid unnecessary downtime.

These maintenance systems rely on unsupervised machine learning techniques to formulate predictions. Predictive maintenance solutions can help reduce costs while, in many cases, eliminating the need for planned downtime, thereby strengthening the bottom line and improving the employee experience.

With machine learning preventing failures, systems can continue to operate without unnecessary interruptions or delays. The required maintenance is very specific - technicians are informed of the components that need to be inspected, repaired and replaced; which tools to use and which methods to follow.

Predictive maintenance can also extend the remaining useful life (RUL) of machines and equipment, as secondary damage can be prevented while requiring less labor to perform maintenance procedures. Improving RUL can increase sustainability efforts and reduce waste.

#3: Human-robot collaboration

According to the International Federation of Robotics (IFR), as of 2020, there were approximately 1.64 million industrial robots in operation around the world. There are fears that robots will take away jobs, but the industry is seeing workers embrace programming, design, and maintenance.

Humans also work alongside robots to improve efficiency and productivity on and off the factory floor. As robots become more entrenched in manufacturing, artificial intelligence will play an important role. It will ensure the safety of human workers and give robots more autonomy to make decisions that can further optimize processes based on real-time data collected from the production floor.

#4: Generative Design

Manufacturers can also leverage artificial intelligence during the design phase. With a well-defined design brief as input, designers and engineers can use AI algorithms (often called generative design software) to explore all possible configurations of a solution.

The briefing can include limitations and definitions of material types, production methods, time constraints and budget constraints. The set of solutions generated by the algorithm can then be tested using machine learning. The testing phase provides additional information about which ideas or design decisions work and which don’t. From there, additional improvements can be made until an optimal solution is reached.

#5: Market Adaptation and Supply Chain

Artificial intelligence permeates the entire Industry 4.0 ecosystem and is not limited to the production workshop. Artificial intelligence algorithms can optimize the supply chain of manufacturing operations, helping manufacturers better respond to and predict changing markets.

Algorithms can construct market demand estimates by considering demand patterns categorized by various factors such as date, location, socio-economic attributes, macroeconomic behavior, political status, weather patterns, etc. Manufacturers can use this information to plan the path forward. Some of the processes that can be optimized using these insights include inventory control, staffing, energy consumption, raw materials, and financial decisions.

Industry 4.0 and Collaboration

AI is popular, but it requires collaboration to be used correctly. First, manufacturers should weigh the pros and cons of buying versus building the technology and expertise required. An Industry 4.0 system consists of many elements and stages that are unique to manufacturers:

• Historical data collection.
• Capture real-time data through sensors.
• Data aggregation.
• Connect through communication protocols, routing and gateway devices.
• Integrate with PLC.
• Dashboard for monitoring and analysis.
• Artificial Intelligence Applications: Machine Learning and Other Technologies.

Industrial artificial intelligence is no longer a distant aspiration. Manufacturers can now use these technologies to address their specific business challenges and needs. As Industry 4.0 evolves and becomes more complex, manufacturers will need the agility and visibility that AI brings.

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