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Written by: Rahul Garg, vice president, industrial machinery and mid-market program at Siemens Digital Industries Software
A principal challenge for machine builders is competing and responding to the demand for increased machine customization and complexity in fulfilling specified requirements. Intelligent Performance Engineering (IPE) helps solve these challenges for companies developing new engineering practices that keep pace with the growing sophistication of new machine introductions.
Digitalization and Machine Complexity
Rapidly advancing technologies require companies to continuously adapt their process to remain competitive in the Industry 4.0 landscape. Customers are demanding machines that meet their unique specifications and fulfill performance requirements that are increasing almost daily.
It is crucial to differentiate a product from global competition by serving customers rapidly and more economically. Though these objectives are a backbone of manufacturing, dynamic technologies propel companies to consider and evaluate the best methods to meet customers' needs and challenges while using digitalization. Therefore, in stretching the limits of technology advancements, there is an ongoing need to address the growing demands of flexibility and complexity. However, you cannot accomplish this goal without quickly evaluating machine behaviors and providing that data back into the model.
Intelligent Performance Engineering delivers the capability to ensure innovative machine designs are delivered as promised through a digital thread to support consistency and high performance. IPE is a promising innovation that provides several critical design parameters, including personalization, global competition and simulation, while adopting practices to ensure safety, reliability and cost-effectiveness. Also, IPE provides better integration between designers, analysts and live data, enabling original equipment manufacturers (OEMs) to adopt practices to improve engineering speed and deliver optimum performance.
So, implementing IPE in conjunction with a digital thread is vital to balancing a customer's needs to improve machine reliability and performance while evaluating, verifying and testing designs.
Measuring Performance with Simulation
As corporations deliver machines with faster cycle rates, higher reliability and constricted delivery schedules, simulation performance occurs upfront versus conventional testing of multiple physical prototypes, which takes considerable time and expense. Therefore, IPE simulation and testing collaborate proficiently to address the essential needs of modern machines.
One of the most critical aspects of designing new industrial equipment or modifying existing designs is verifying and testing to analyze the performance before it reaches the customer. Consequently, the resolution is for OEMs to adopt a collection of digital simulation and analysis tools to understand the design choices that affect performance and failure for a component, device or machine.
It costs much less to address the design process' problems rather than trying to address potential issues with your machine design in the product development cycle.
In conventional methods, there still exists some manual handoffs between design and simulation processes. For example, engineers can use design-level simulation to provide a baseline design assessment or a definitive design analysis leading to more advanced simulation. As enterprises attempt to deliver machines with faster cycle rates and compressed delivery schedules, teams are under pressure to perform simulation early in the design phase rather than testing several physical prototypes and assuming these physical tests will suffice. Therefore, the objective is to work efficiently with simulation and testing.
Machine Data Analysis and Implementation
The manual aspects of running a simulation serve to verify several equipment characteristics using multiple tools. As a result, basic simulations do not always reflect interdependency issues such as electromagnetic interference, structural loads, heat and vibration. These issues also relate to smarter equipment with increasing complexity of wiring, electronics and software. The designer may need to run a fundamental analysis to validate design safety, but hinders the designer from exploring the performance ramifications of engineering trade-offs. Also, a designer may over-engineer a design, thus bypassing the need to run as many tests as possible. However, this may lead to additional cost, weight or reduced machine performance to meet safety requirements. Therefore, the analyst may adopt complex processes to address simulation tool intricacies and lack of design tool integration. And any delays in the process may result in performing risky analyses on outdated designs.
The digitalization process, which includes the digital twin, requires optimum integration levels—an essential for OEMs. A comprehensive digital twin improves the process of simulating various characteristics of components and equipment more precisely to enable faster delivery of more reliable and smarter machines. Additionally, a digital thread automates the information sharing process between engineering teams, analyst production, test teams and service engineers. This progression allows teams to evaluate capabilities and limit product variations efficiently.
Intelligent Performance Engineering improves dependability to address risk by building highly accurate models that predict product behavior during lifecycle phases. This unique approach in today's marketplace understands industrial machine and equipment manufacturers' process needs and challenges. The resulting data on those issues identifies three crucial areas:
- Multi-physics simulation gives customers the ability to balance multiple attributes under one umbrella. For instance, there is the ability to decipher thermal and stress analysis at the same time. So, when the heat rises, resulting in a potential impact on product stability, there are multi-physics simulation models implemented together to address several needs that are typically isolated. Consequently, balancing the performance characteristics across the physics domains becomes very valuable.
- Integrated design and simulation focus on performance engineering to address the front-end product design commissioning process that ensures models are consistent with high variability—managing many changes while not losing data and keeping it in sync.
- Closed-loop validation expands the test horizon to look at the testbed, machine prototype or machine in operation as the testing environment. Subsequently, you receive more information from machine use by getting real-time feedback as part of the test analysis.
Conclusion
The industrial equipment industry is becoming more complex with faster delivery expectations, leading industrial OEMs to build more effective and trustworthy simulations. Developing industrial machinery requires discovering the best balance between productivity, accuracy, reliability and efficiency—and performing these processes digitally to out-innovate the competition. Siemens Digital Industries Software drives the transformation to enable a digital enterprise where engineering, manufacturing and electronics design meet tomorrow.
Xcelerator is a comprehensive, integrated portfolio of software, services and an application development platform. The portfolio accelerates the transformation of businesses into digital enterprises. It unlocks a powerful industrial network effect—essential requirements to leverage complexity as a competitive advantage, no matter the industry or company, to transition seamlessly to create tomorrow's complex, efficient machines.
To learn more, watch the video Intelligent Performance Innovation Through Simulation, or visit the Siemens Digital Industries Software website.
About the Author
Rahul Garg is vice president for the industrial machinery and mid-market program at Siemens Digital Industries Software, responsible for global business development. He and his team deliver strategic initiatives and develop solutions, working with industry-leading customers to provide thought leadership on new, emerging issues in the machinery industry. Rahul’s 25-year career includes delivering software-based solutions for product engineering and manufacturing innovation globally. He has held leadership positions in research and development, program management, sales and P&L management, focusing on industrial machinery and heavy equipment since 2007.