Control Design Guide for Next-Generation Machines

Like generations of technologies before, smart machines will impact almost every domain of  life. They will alter how we produce goods, perform surgery, organize inventory, and even  how we educate future generations. These systems not only perform repetitive tasks at  soaring speeds and high accuracy, but they can adapt to changing conditions and operate more autonomously than ever before.

Click through to download this “Control Design Guide for Smart Machines” white paper, which explains the challenges machine builders face today and demonstrates proven methods and solutions to get ahead of the competition. Experience the impact graphical system design and customizable off-the-shelf hardware have on the design process and business success.

Click here to download the guide.

Engineers and scientists are tasked with designing machines that are dramatically more  flexible and versatile. There are two needs that are driving innovation in smart machines: one is the individuality and complexity of produced goods and the other is the ever-increasing demand for productivity and quality. Machine builders no longer design single-purpose machines — they create flexible, multipurpose machines that address today’s manufacturing needs such as smaller lot sizes, customer-specific variations of products, and the trend toward highly integrated products that combine different functionality in one device.

Modern machines can operate more autonomously than ever before. They can also prevent — as well as correct — processing errors caused by disturbances like changing
conditions in the raw material, the drift of the thermal working point, or the wear and tear of mechanical components. With an extensive network of embedded sensors, smart machines hold information about the process, the machine condition, and their environment. This improves uptime and increases the level of quality. In addition, these systems can improve their performance over time and learn through analytics by leveraging simulation models or applying application-specific learning algorithms.

These machines also exchange information with other automation systems and provide status information to a higher level control system. This allows for intelligent factories and automation lines that can adjust to changing conditions, balance the workload between machines, and inform service personal before a machine fails.