Steps to Create Smart Machines

The development of smart machines involves a series of well-defined steps that combine advanced technologies, data analysis, and artificial intelligence to optimise performance. Each step makes machines more efficient, reliable, and adaptable to modern manufacturing needs.

Step 1: Use a Smart Machine Controller

Functionality of Controllers in Managing Data Flow

The controller is often referred to as the brain of the machine, responsible for managing data flow and ensuring seamless communication between various machine components. Smart controllers can process vast amounts of data in real-time, allowing for constant quality control, predictive maintenance, and fast troubleshooting. By analysing operational data, these controllers enable machines to adjust parameters dynamically based on production conditions, resulting in more efficient operation.

Examples of Data-Driven Decision-Making Processes

For instance, a smart machine controller might monitor factors like temperature, speed, and humidity in a production line. If any of these parameters deviate from the norm, the controller can adjust the process to maintain product quality. Such real-time decision-making helps avoid producing defective goods and reduces waste.

Step 2: Utilise Smarter Devices

Importance of Advanced Actuators and Sensors

The foundation of smart machines lies in utilising advanced devices, such as actuators and sensors, which allow machines to interact with the physical world. These devices collect crucial data about machine performance and enable precise control of machine operations. One of the most innovative advancements in this area is the development of collaborative robots (cobots), which are designed to work alongside humans in shared workspaces.

Expanded Role of Cobots in Enhancing Machine Capabilities

Cobots are revolutionising the way machines function in modern manufacturing environments. Unlike traditional industrial robots, which are often confined to fenced-off areas for safety, cobots can work directly alongside human operators without the need for large physical barriers. This increases flexibility and reduces the footprint of manufacturing setups, making cobots ideal for smaller production spaces.

Cobots are designed with advanced safety features, including force-limiting technology, that allow them to stop instantly if they detect any form of human contact. This high level of safety, combined with ease of programming, makes cobots accessible even to small and medium-sized businesses, which may lack extensive robotics expertise. Cobots can be quickly reprogrammed for various tasks, such as packaging, assembly, or quality inspection, enhancing production efficiency and reducing downtime.

Examples of Cobots in Machine Building

In machine-building scenarios, cobots are used for tasks that require a combination of precision and adaptability. For instance, cobots can assist in material handling, machine tending, or even complex assembly tasks, helping to enhance the productivity of both the machine and the human operator. Cobots are especially effective in environments where repetitive or physically demanding tasks are common, as they can take over such duties, allowing human workers to focus on more skilled or creative tasks.

Cobots also contribute significantly to data collection and real-time monitoring. Equipped with sensors, cobots can monitor production variables such as force, torque, and speed, feeding this data back into the system for real-time analysis and optimisation. This makes them invaluable in creating more responsive and adaptable machines.

Implementation of Condition Monitoring Systems for Real-Time Data Analysis

Cobots are often integrated with condition monitoring systems, which allow machine builders to implement condition-based maintenance. By continuously monitoring the health of key components, such as motors or gearboxes, these systems can predict when maintenance is required. This predictive capability helps reduce unplanned downtime and extends the lifespan of both the cobot and the machines it supports. Furthermore, cobots can use real-time data to make adjustments during operation, ensuring optimal performance and reducing energy consumption.

Step 3: Leverage Data Effectively Through Visualisation

Analysing Collected Data to Drive Continuous Improvement

Visualisation is critical for leveraging collected data effectively. Operators can gain valuable insights into machine performance by analysing data displayed on Human-Machine Interfaces (HMIs) or through systems like Mitsubishi Electric’s GOT Drive and GOT Mobile. Continuous monitoring allows for rapid identification of inefficiencies or abnormalities, enabling operators to make real-time adjustments and drive continuous improvement.

Step 4: Implementing Artificial Intelligence on the Edge Level

AI at the Machine Level

Artificial intelligence (AI) is increasingly integrated into machines at the edge level, allowing for rapid, localised decision-making. AI can be embedded directly in machine controllers, motors, and sensors, where it processes data in real-time to make autonomous decisions. This approach ensures responsiveness and efficiency, as data does not need to be sent to a central server for analysis.

Advantages of Localised AI Integration for Responsiveness

Localised AI enhances machine responsiveness by enabling predictive maintenance and real-time optimisation without the delays associated with cloud-based solutions. For example, AI algorithms can monitor robot load and environmental conditions, adjusting operations immediately if necessary.

Applications in Predictive Maintenance and Quality Control

AI-driven systems are particularly effective in predictive maintenance and quality control. For instance, Mitsubishi Electric’s AI features enable preventive maintenance by monitoring the real-time condition of robots, motors, and other critical components. By predicting failures before they occur, AI helps ensure machines operate reliably and efficiently, minimising downtime.

Step 5: Optimisation Using ICONICS, for Example: MaaS

Machine as a Service Model

The Machine as a Service (MaaS) model is revolutionising the machine-building industry by offering a subscription-based approach to machinery. Rather than purchasing machines outright, customers can pay for machine usage monthly, providing flexibility in production. This model shifts the responsibility for machine performance and maintenance to the machine builder, ensuring machines remain operational and efficient.

Role of ICONICS in Optimisation

ICONICS plays a pivotal role in enabling the MaaS model through its advanced real-time monitoring and data visualisation platform. It provides insights into machine performance, energy usage, and overall equipment effectiveness (OEE), helping machine builders optimise operations and reduce downtime. Through ICONICS, machine builders can also deliver predictive maintenance services, allowing them to address issues proactively, further enhancing the reliability of their machines.

Energy Savings and Sustainability

In the context of energy savings and sustainability, ICONICS integrates seamlessly with tools like Mitsubishi Electric’s EcoAdviser, which is designed to monitor and optimise energy consumption. EcoAdviser uses AI to identify energy losses and provide actionable recommendations for reducing waste, aligning with the sustainability goals of Industry 5.0. This is especially relevant for machines powered by IPM motors, which can reduce energy consumption by up to 20% compared to standard motors. By optimising energy use, machine builders not only contribute to a more sustainable production process but also reduce operational costs for their clients.

Continuous Improvement and Flexibility in Operations

MaaS, supported by ICONICS and tools like EcoAdviser, offers significant benefits, including continuous improvement and operational flexibility. Machine builders are incentivised to monitor and enhance machine performance regularly, ensuring that energy usage is optimised and sustainability goals are met. This provides end-users with access to advanced, energy-efficient technologies without the need for large upfront investments. At the same time, machine builders gain a recurring revenue stream and the opportunity to offer value-added services, all while supporting global sustainability initiatives.