Getting smart: How smart manufacturing is changing the way we do business

In manufacturing, analysts are predicting that technology innovations, productivity and business growth are being driven by the demand for mass customization and converging technology advancements for the next generation of manufacturing infrastructures. This is called “smart manufacturing.”

Going digital

Smart manufacturing is fundamentally a digital approach that assists companies in optimizing every step of the manufacturing machine process, from creating the machine to executing it, manufacturing, and extending into the service life.

Also, it helps to create a growth path for addressing a dynamic marketplace. It offers a host of benefits by improving manufacturing throughput, uptime and performance while minimizing costs, including overhead, operations and capital.

These innovative capabilities are serving to empower smart manufacturing further, allowing machine manufacturers and designers to create more value by closing the loop between manufacturing operations and engineering. All of this explains the growing popularity of smart manufacturing.

Machining capabilities

Machine builders are designing-in the following capabilities to enable smart manufacturing:

• Connectability. Customers expect machines to communicate with the other machinery in their plant, which is facilitated via a machine builder (OEM).
• Adaptability. With all of the information generated by sensors and actuators, smart machines can recognize changes in upstream products and processes and adjust to these dynamic operating conditions.
• Predictability. An increasing emphasis is on the simulation and predictability of a machine’s performance in the field, requiring a high-fidelity digital twin of the machine.
• Extendability. It is now possible to extend the life of a machine in the customer facility with predictive maintenance and adaptive performance. The objective is to create more value for a manufacturing customer and to optimize the cash flow.

Smart manufacturing brings intelligence into all the aspects of the manufacturing process, encompassing the Internet of Things (IoT), Industrial Internet of Things (IIoT) and Industry 4.0. It is the integration of intelligence in the actual machines, parts, materials, products, buildings and supply chain. It then applies that intelligence within a connected, open end-to-end process and infrastructure.

With smart manufacturing, data is the master, not the system.

Maximizing with robotics

The digital twin is fundamental to implementing smart manufacturing. Using a digital twin that encompasses the mechanical, electrical and programmable logic control (PLC) enables a comprehensive approach to simulate the machine.

For example, when making the machine tool component, an inspection process is added to the manufacturing process for describing what data is to be measured and collected to create traceability and a close-the-loop process based on a high-fidelity digital twin.

The next crucial step is linking the digital twin of the product with the digital twin of the machine. It’s not only building the part and executing it, but also managing delivery, manufacturing, operations and quality.

With smart manufacturing, data is the master, not the system.

There is a need to manage manufacturing operations to drive greater efficiencies by coordinating all these activities to deliver the correct parts at the right time.

The advancements in part manufacturing, from additive to higher-performance multi-axis and combination mill-turn machining centres, require CAM software that can take advantage and maximize production capacity. Companies are also incorporating model-based definition into 3D models to leverage the information into robotic inspection programs.

In addition, robotics is becoming a staple in today’s manufacturing environment, with robotic machining and human-assist collaborative robots (cobots). Advanced robotics integration is a part of the smart manufacturing solution to simulate robot performance and integration on the factory floor.

Using plant-level simulation

Advanced plant-level simulation capabilities track and trace materials through the factory from raw material to project rack to machine and optimize the layout to decrease both high-traffic areas and dead zones.

Smart manufacturing also addresses the massive complexity of the machine bill-of-materials (BOM). Each function needs its own view of the BOM that fits the purpose, with traceability back to the single source of truth, requiring advanced analytics and capabilities to schedule, manage operations and execute with quality.

It’s important to have this traceability from the engineering BOM to the manufacturing BOM. For example, the recipe for every part needs to include the CAM code used to make it, and the quality inspection plan (and results) so that the customer has 100 per cent traceability of quality from end to end.

All of these capabilities are helping companies take advantage by adopting innovative processes for improving the overall performance of the machine, thus refining products, processes, resolving failures and improving operations of machinery.

What’s changing for machinery suppliers?

Technological advancements are driving industrial machinery companies to fully realize Industry 4.0, with staggering implications. The following trends are re-shaping the engineering, manufacturing and service operations for most machinery suppliers:

Consumer-driven customization

Machines automate processes to help companies lower costs and expedite delivery of their goods to the end user. Hence trends in the broader consumer market ultimately end up defining what machinery customers need.

A typical consumer product’s development cycle is compressing – lot sizes are smaller, and product life spans are shorter. So, machinery customers need machines that are more flexible and adaptable to an ever-changing product mix, often with customized features or functions that require machine builders to innovate more quickly.

Smart machines

Machinery component suppliers have completely embraced IoT-enabled devices. Thus, machinery manufacturers are on a steep learning curve in knowing how to take advantage of available information.

The number of I/O (input/output device-driven) channels and different communication protocols (wired networks and wireless 5G) provides an order of magnitude increase in information flow compared to recent years.

That means automation code developers are forced to choose which channels to use while building more intelligent machines.

Hyper-automation

Discrete programming is enabling machine users to gain insights from all of the IoT information. The hyper-automation trend requires vast amounts of data and cloud-based analytics to accelerate learning about machine behaviour and performance to automate machine functions.

Hyper-automation is also enabled by the emergence of low-code tools that help machine users mine data analytics for many business processes – manufacturing optimization, engineering reliability and cost reductions.

Global, highly innovative competition has always existed.

Still, now the challenge comes from more flexible, agile start-up companies that begin from the basis of machine learning and are not encumbered by existing business processes or legacy customer engagements.

Some offer production-as-a-service and other innovative software-enabled service monitoring tools and machine optimizations – even on competitor’s machines.

A comprehensive approach

A machine builder must possess a comprehensive approach to smart manufacturing, allowing machine manufacturers, designers and engineers to create additional value to their machines and manufacturing process through multiple high-tech means.

Having an executable digital twin is crucial, along with possessing the software for realizing and implementing all steps in the manufacturing process – including creation, execution and service life.

Bill Davis is the solution director of industrial machinery and heavy equipment industry for Siemens Digital Industries Software.

This article originally appeared in the June 2020 issue of Manufacturing AUTOMATION.