Simulating Manufacturing Mitigates Product Risk

Simulation has been a dominant force in early development cycles to optimize product design and propel next-level innovation. Now, simulation is taking root as part of early manufacturing process planning as companies seek more resiliency and risk mitigation in increasingly volatile and fast-changing markets.

The surge of additive manufacturing (AM) for production use cases—in particular, metal AM—has underscored the need for more prolific and more in-depth simulation of processes outside of traditional design analysis. AM is still an evolving solution set with a litany of new technologies and materials that are not as widely understood as traditional manufacturing methods like injection molding, casting or machining. The lack of clarity and standardization highlights the need for simulation-led manufacturing process planning as companies aim for first-time-right production as opposed to wasting time and money on scrap and product designs that are not effectively manufacturable.

With the pandemic and post-pandemic period punctuated by supply chain disruption, geopolitical instability and a push for greater nearshoring and onshoring, companies are confronting greater complexity. That has players scrambling to transform existing manufacturing processes with digital technologies, including AM, simulation and more recently, artificial intelligence (AI) and data-driven insights.

“Companies are looking at how to avoid the disruption of COVID or geopolitical conflicts through simulation of products and now manufacturing plants,” says Prashanth Mysore, senior director for global strategy, DELMIA at Dassault Systèmes.

Manufacturing process simulation isn’t necessarily new, but there is now increased demand, with a number of clear use cases starting to emerge. Many traditional physics-based simulation and generative design tools are being expanded with support for design for manufacturing (DfM) capabilities that enable engineers to specify manufacturing constraints as another parameter to generate designs—for example, requirements that might determine whether the optimal design is multiple parts or a single, multidimensional component.

The rise of AM for production use cases has fielded an emerging category of AM-specific simulation tools for optimizing support structures, build orientation and other printer parameters. Finally, there is a long-standing class of manufacturing process simulation tools being used to simulate individual industrial assets, manufacturing cells and even entire production lines to optimize performance, boost product quality and promote safety and sustainability.

“Both [small- to mid-sized businesses (SMBs)] and large manufacturers are looking at technology to shorten new product introductions, accelerate time to market and elevate product safety and sustainability,” Mysore says. “One answer is to simulate the manufacturing process upfront because it doesn’t cost you money and you don’t waste anything.”

Simulation-Driven DfM on the Rise

While there’s not yet a magic silver bullet for determining the right manufacturing method for a particular design, generative design tools inch closer to that goal by allowing manufacturing constraints to be specified as yet another parameter in an early design study. The wrong choice of manufacturing process or materials used can create a host of manufacturing issues and drive up costs, and traditional trial-and-error approaches fall short in delivering results in a timely fashion.

Design tools that enable first-time-right production, many through application of simulation, allow engineers to explore the most cost-effective and optimal performance approach, regardless of whether the method is AM, casting or injection molding.

“Deciding on a manufacturing method is part of simulation-led design,” says Ravi Kunju, chief product and strategy officer at Altair. “When designing a product, you must design to meet both performance and manufacturing process requirements. You don’t want to land on a part you can’t manufacture.”

Through capabilities such as graphics processing unit (GPU) rendering and computational fluid dynamics (CFD), Altair’s Inspire unifies simulation and generative design capabilities for optimizing design and manufacturing of parts in one environment, Kunju says. The platform lets engineers and designers use manufacturing constraints as yet another design parameter, but also run virtual simulations of different manufacturing techniques, including 3D printing and injection molding, as an element of more robust manufacturing process planning.

As part of the Inspire family, Altair offers DfM solutions for an array of production methods, including Inspire Cast for casting simulation, Inspire Extrude Metal for metal extrusion, Inspire Extrude Polymer for polymer extrusion, Inspire Form for sheet metal forming and Inspire Mold for injection-molded components. Inspire Print3D brings similar capabilities to bear for the design and process simulation of parts made by powder bed fusion and metal binder jetting platforms.

The Inspire modules can be leveraged to design parts for a particular manufacturing method, but also to optimize an existing part design to ensure its manufacturability.

“The platform enables optimization and generative design for engineers, but also for someone in the business of making parts, it enables a quick analysis so there aren’t quality issues,” says Kunju. “We’re bringing both of those worlds together.”

AM Drives Simulation Interest

The Ansys portfolio expands use of simulation across the entire workflow, from design for AM (DfAM) and other manufacturing methods through mitigating product variability during production. With the increasing use of AM for production, the industry is seeing rising interest in simulation applications to reduce variability of output. At the same time, emerging technologies like AI, GPUs and high-performance computing cloud environments are helping companies do more intelligent simulation of manufacturing processes, says Enrique Escobar, lead application engineer for Ansys.

“More and more people are using simulation tools applied to manufacturing, especially using tools for AM,” Escobar says. “Manufacturing technology is more complex and therefore, more aspects need to be considered. That’s where simulation technology has a role.”

The Ansys Additive Suite delivers capabilities that span DfAM, validation, print design, process simulation and materials exploration to help avoid build failures and ensure accurately designed and produced parts. Additive Print enables users to check parts for distortion, identify stress and thermal strains, and predict blade crashes—the goal being to eliminate guesswork by showcasing how parts will behave during a build and allowing for changes that ensure first-time-right production.

In one example, Ansys worked with Meltio, a manufacturer of wire laser metal deposition printers based on directed energy deposition (DED) technology, to optimize its offerings. AM-DED promises larger build envelopes, more manufacturing freedom and greater cost benefits, but distortions and residual stresses can potentially be observed in printed components.

Meltio used the Ansys Additive Suite to simulate thermomechanical behavior during the DED process for a turbine blade component—a test use case selected due to its manufacturing complexity and potential for high deformations. The simulation was employed to understand the influence of various process parameters like cooling and print times, and the temperature results helped identify overheating regions—in this case, overheating observed towards the tip of the blade geometry. A comparison of the simulations and a scan of a real printed part confirmed the analysis.

“The detailed analysis helped provide an understanding of the different aspects of turbine blade components and showcases how simulation can help achieve accuracy and prevent deformation and defects on a part,” Escobar explains.

One way to increase simulation’s role in evaluating and examining manufacturing considerations is to make analysis capabilities more available to non-experts. Typically, simulation is performed by expert users who leverage the capabilities without in-depth knowledge of what’s happening on the shop floor, according to Tommaso Tamarozzi, product director, additive manufacturing, inspection and simulation for Oqton, a 3D Systems company.

With an implicit CAD engine at its core, 3DXpert lets users perform DfAM, build preparation, process simulation and inspection in the same software environment, opening up simulation access to workers who understand manufacturing, Tamarozzi says. With the software, users can orient and nest parts in the build envelope, plan for supports and anticipate conditions in the build chamber to predict failures and verify part builds. 3DXpert Build Simulation is used to predict deformation and parts overheating, Manufacturing OS Build Monitoring delivers real-time anomaly data to fuel adjustments to reduce scrap or post-processing time, and 3DXpert Build Inspect performs root-cause analysis to help increase repeatability.

“We’re trying to make simulation accessible, not only for the few who know about design and physics, but for the people who know nothing about simulation and a lot about process forming like 3D printing,” he says.

Multiphysics simulation provider COMSOL is also seeing its software, specifically its Application Builder module used to build custom simulation models, being tapped for AM printability use cases. In one example, the Manufacturing Technology Center (MTC), which helps U.K. manufacturers with advanced manufacturing, created a COMSOL simulation model and app to help factory staff optimize operations inside an AM powder bed fusion factory.

Specifically, heat and humidity inside the factory could negatively impact product quality and worker safety. The COMSOL app, based on a CFD model, considers environmental conditions, outside weather and specifics around AM machine operation to help factory staff in charge of day-to-day decision making to alter ventilation and production schedules to optimize operations based on changing conditions.

“The simulation app delivers a vastly simplified user interface that encapsulates all they know about their manufacturing process,” says Bjorn Sjodin, senior vice president of product management for COMSOL. “By shielding users from that complexity, they can experiment and see how the part comes out.”

Simulating the Factory Floor

Simulation also can play a role in modeling and optimizing the performance of individual industrial assets all the way through production cells and entire factory floor operations. Dassault Systèmes’ DELMIA software portfolio, powered by the 3DEXPERIENCE platform, enables manufacturers to simulate individual machines such as cobots, AM printers or computer-numerically controlled (CNC) machines to ensure optimal cycle times, reduce potential crashes and promote repeatability of parts production.

There are human simulation capabilities to ensure production lines and work cells are set up to promote worker safety and in compliance with federal and state regulations. Manufacturers can also enlist DELMIA to simulate at a cell level or go as far as to create an entire virtual twin of complete factory floor operations to promote efficiencies, enhance sustainability and ensure optimal part accuracy and yields.

The 3DEXPERIENCE platform delivers integration and a contextualized data model. “This lets an operations manager see all their work at a holistic level for better optimization of the manufacturing process,” Mysore says.

Siemens Digital Software Industries’ Tecnomatix can also be used to model, simulate, and optimize a digital twin of manufacturing assets and full processes, from robots to automation to material handling systems. The platform’s range of simulation and discrete event capabilities enables manufacturers to simulate and validate manufacturing process plans to streamline and scale production cycles; analyze work cells for operator comfort and safety; design kinematic models to optimize tooling, robots and fixtures; and create and build products that can be easily maintained, among other functions.

Given the learning curve for AM, in particular, customers are targeting simulation at multiple intervals, including mapping out and optimizing print facility layouts and preparing for scaling production by virtually evaluating strategies like adding shifts or restructuring production schedules, according to Chris Weber, Siemens’ director of additive manufacturing software solutions for the Americas.

While engineers still have to go through a manual process to determine the optimal manufacturing method, the evolution of AI and other technologies should advance simulation software to the point where much of that decision-making process can be automated. “We have the foundations, but we’re not there yet,” Weber says. “We clearly see a day coming that through the use of AI and the data maintained in parts catalogs, we can accelerate the decision of the best way to make that part.”

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