CAE Trends in Mechanical Engineering Analysis

The use of computer-aided engineering (CAE) in today's market is increasing, especially in new product development and sustainment; research and development (R&D); and manufacturing with emerging technology. New products in almost every industry are now built with smart technological features that provide a better experience to the end users and this technology integration adds a multi-skill requirement to any engineering solution. Challenges have emerged as products are becoming more complex to satisfy expectations in the functional, business and customer satisfaction aspects of product development.

There is an increased demand for product/system level CAE expertise, functional/technical expertise and value-added services. Specifically, companies often need:

• Recommendations for efficient/optimized designs
• Support for new technologies such as electrification, autonomous vehicle development, Big Data analytics, artificial intelligence and machine learning, the Internet of Things (IoT) and virtual reality
• Turnkey solutions (providing the solution for the problem, not just the service - industrial design, product design, testing, manufacturing, project management, etc.)

Because of this, the need for development of niche areas in CAE is becoming more important as part of the engineering solution.

Role of CAE in Engineering

The benefits of incorporating CAE in engineering and design development include:

• Non-destructive virtual testing even before the prototype is built
• Reducing the timeline for product development
• Catching problems early
• Preventing unnecessary design iterations
• Helping to choose alternate material that meets product cost and performance requirements
• Reducing the need for physical tests and prototypes
• Reducing the cost of unexpected failures
• Validating that simulations agree with reality
• Maximizing product reliability and durability
• Meeting design requirements and regulations
• Getting the right information to make important decisions
• Helping to meet targets
• Developing "what if" studies for directional inputs

Engineers must spend time in understanding the product knowledge, functionality, application and required skills to better perform CAE simulations to solve engineering problems efficiently. They also must have insight into the complete product development process to understand the importance of program requirements, milestones and timelines so they can provide appropriate recommendations for making timely decisions for the design sign-offs. Often, the use of engineering judgement could reduce unnecessary design iterations.

New Market Trends Drive Future of CAE

Technological development, be it in the automotive, medical, customer products or heavy/construction equipment industries, requires niche CAE solutions. This is driving a demand for advancing CAE within a number of specific domains:

• Electrification and autonomous requirements in transportation and other industries
• Thermal management for electronic devices
• Computational fluid dynamics
• Multi-physics and co-simulations
• Vehicle dynamics - multi-body dynamics
• Simulation needs for electro-magnetic (EMAG) requirements to reduce test costs and design iterations
• Optimized design solutions with AI machine learning techniques - Big Data analytics
• Cloud-based computation for faster simulations
• Customization and automation for process efficiencies
• Regulation changes and updates
• Reliability and predictive analysis for quick and efficient design options

Tools and technologies like the IoT, digital twin, project lifecycle management (PLM), computer-aided design (CAD), augmented/virtual reality (AR/VR), systems engineering, and 3D printing can also have interesting symbiotic relationships with simulations.

What's Ahead for CAE?

With the world of evolving technologies, what lies ahead for CAE? As in other industries with emerging technologies of Big Data, it is expected to cause significant shifts in the CAE field.

The demand of CAE is anticipated to expand significantly in the next five years:

• Electronics are expanding with the advancement of wearable technology, IoT, smart sensors, miniaturization, and persuasive connectivity - the lowered cost is likely to increase use of CAE simulations
• Simulation software is being used to analyze the effect of casing materials on integrated circuit (IC) performance, thermal management and electrical signal performance
• Increasing adoption of lithium-ion batteries in consumer electronics applications
• Technological advancements in the automotive sector such as collision avoidance, parking assistance systems, interactive infotainment and new government safety regulations for autonomous and electric vehicles.
• Advancement of technology in additive manufacturing or 3D printing with low-cost options allows users to print 3D objects of complex designs easily, which will reduce production costs and increase the ability to design complex structures
• CAD vendors are increasingly incorporating CAE into design tools, and there is now an expectation that design engineers perform quick analyses to validate the design. Meshless finite element analysis (FEA) is also a growing trend due to its quick application. Of course, we always need to be mindful of the limitations of software and the knowledge of CAE in performing the analysis - as the saying goes, "garbage in is garbage out"

Building Strong CAE Capability

Building CAE capability requires combining the right people, processes, technology, experience and governance structure. To meet future demands, we need to invest in technology advancements, knowledge management, train quality CAE engineers and develop subject matter experts (SMEs) in respective domains.

CAE engineers can provide high-value solutions to issues consistently:

• Engineers are required to keep up with the trends. We need to have strong engineering fundamentals and good engineering judgment in supporting projects.
• Projects require a “big picture view” rather than just a focus on individual tasks. We need to understand the system, subsystems, functionality and test procedures.
• We also need to build confidence in results through:
• Benchmarking and test correlation
• Adequate modeling techniques to represent physical behavior
• Material selection and representation
• Type of analysis
• Developing best practices
• Knowledge management and training programs
• Proper quality checkpoints

To provide best-in-class solutions, we as engineers must constantly ask ourselves questions, such as:

• What are the project requirements?
• What is the background of the problem, the product/system functionality and physics?
• What type of analysis should be performed?
• What are the project milestones/product development lifecycle? 
• What do you want to achieve from analysis?
• Can we simplify the problem or do simple hand calculations?
• What are the software/hardware requirements and their limitations?
• What meshing approach should we consider?
• How can we better define the load cases and boundary conditions?
• What is the acceptance criteria?
• What are checks should we perform?

• How to find possible solutions for the problem by?
• Exploring new design options
• Reviewing historical/legacy data
• Appropriate material selection process
• Understanding manufacturing process used for the parts, assembly method — know about Design for Manufacturing and Assembly (DFM/A)

• What are the technical challenges and bottlenecks in the project that we can address to ensure a faster and smoother transition from physical to virtual design validation testing?
• How can we improve efficiency through automation, embedded intelligence and smart processes? We need to explore possibilities for CAE process automation through various program languages such as Python, TCL, C, VB, Matlab etc., to create macros wherever possible.
• What is the latest enhancement/option in software to develop and explore advanced techniques that can be applied to deliver current and future requirements? We should proactively explore reduction methods for improved computational efficiency.
• How do I work with cross-functional teams for shared knowledge and quality output?
• What are the project risks, stakeholder roles and responsibilities?

• We need to consider delivery models per customer requirements, driven by:

• Limited or no space at customer location
• Flexibility and ramp-up
• Complexity and collaboration
• Improve communication and governance
• For successful project delivery, we need to support project management, including the project plan; project governance; status reports; quality in terms of corrective action/preventive action (CAPA); risk management; responsibility, authority, support, inform (RASI); communication logging and Issue resolutions.

Companies in many industries can benefit from a fully realized CAE specialization. Working collaboratively with a trusted engineering services provider, can help you achieve quality products and solutions.