Simulating the Manufacturing Process Chain
Forging and heat-treatment process development is dictated by the product requirements in service. Thus, forgers are motivated to understand how design and process changes influence material properties, machining, assembly and the product life cycle. This increases the need for versatile process-simulation tools capable of modeling the entire process chain.
Throughout the development process, forgers are faced with many questions regarding how their decisions will impact customer requirements. On one side, the customer requirements may cover a wide range: specified dimensions, minimum properties, tensile strength, cost, quality and lead time are but a few. It is important to note that trade-offs exist between many of the requirements. For example, arbitrary lightweighting could result in reduced strength. On the other side, a company’s actions may be constrained by their equipment capabilities. They may also face limits in available manufacturing operations or processing ranges of material, lubricants, etc.
Simulation is commonly used to study trade-offs during forging development. Confidence in these tools has motivated many to ask, “What else could we model and predict with simulation?” They may be interested in modeling manufacturing, installation and product performance. In other cases, users hope to predict distortion through forging, heat treatment and machining. Some might have an interest in simulating an additive-manufactured preform through forging. Process-simulation software developers have responded by adding multiple-operation (MO) capabilities to programs like DEFORM V11. These developments have made process-chain modeling quite practical.
Developing a robust simulation tool to model an entire manufacturing chain is not a trivial task. A highly specialized infrastructure is required to execute this strategy. It should be easy to add new operations or applications while retaining powerful simulation capabilities. It needs to be able to pass models and process parameters between operations. Manufacturing history ought to be propagated from start to finish. Setup procedures should be familiar to users who have been running simulations for years. Most importantly, the tool must be able to adapt to changing requirements. Hard-coding the software for a particular flow path is far too inflexible for many future process-modeling objectives. Expectations are that the solution must be efficient, capable and informative.
Users who begin to embrace MO tools and methods often start conservatively by modeling the familiar. Their goal is to simulate what they do today, but their focus is on doing it better. The resulting models may be more advanced, efficient, flexible or easy-to-use.
A process-chain example that many forgers would find familiar comes from a 2009 FIERF project studying coal-dust elimination from ring-rolling preforms (Fig. 1). Simulations studied the thermal history of a center-punch tool through a typical 5-25 part forging production run (Fig 2). One goal was to predict the steady-state temperature in the punch. Analysis revealed that the punches were overheating. This was confirmed by decreased punch strength and hardness, with excessive die wear and plastic deformation.
The setup of such a sequence is an easy task with today’s process-chain modeling tools. The operations used to forge a single part are first defined. Then this group of operations is instructed to cycle a defined number of times. Cycled operations can be added, removed or modified. The cycled sequence runs from start to finish without user intervention. New design or process iterations may reuse this project setup as a template. A modified sequence can be set up in as little as a few seconds if the change is simple. These tools provide great efficiency to any user modeling routine or advanced-manufacturing chains.
Integrating Additional Operations
Once they become comfortable modeling familiar process chains, forgers often expand their use of MO tools. They may integrate new upstream or downstream operations into their simulations. For example, some companies study the interrelation between manufacturing and microstructure evolution. They first experimentally determine the extensive microstructural data required for material modeling. Once this is in hand, simulation software provides the framework to couple complex material models with forging and heat-treatment analysis. With such a flexible process-chaining tool, users can combine a variety of dissimilar applications into advanced simulation studies.
Many such opportunities exist in the development of nickel-based superalloy turbine disks. Over the years, companies producing these critical engine components have simulated many operations in addition to forging. Objectives have included grain-size characterization from billet through aging, residual-stress prediction from solution anneal through quenching and spin-test modeling of a forged part.
A recent study on a common turbine-disk forging is used to illustrate advanced process-chain modeling. Initial simulations evaluated the impact of process design on microstructure and strength. A multiple-step forging sequence was found to provide better properties than a single-step forging operation (Fig. 3). Later simulations investigated over 100 samples across seven forging and heat-treatment operations using design of experiments (DOE) methods. They helped users optimize the average grain size present in the critical region of the forging (Fig. 4).
The single greatest benefit of process-chain modeling may be the wealth of information that it provides. Much of this information may not be available using traditional simulation approaches. For instance, forging trials can be reverse-engineered and correlated with simulation. This improves simulation accuracy and provides a deeper understanding of the process. Also, new ideas can be evaluated in a robust and safe “virtual” environment. The software can easily handle even the most radical setups. This allows risk to be minimized. It is also helpful when one must “make their case” in the regular manufacturing meeting. Extensive studies can be performed using DOE or optimization methods. They allow valuable information about an objective to be collected quickly and easily. Many design iterations can be evaluated in the time that it takes to cut a single tool.
Process-chain modeling and DOE are also helpful in improving established process and die designs. They provide the ability to examine the sensitivity of a critical output to specific input variables. This can help the user understand “what to control” during production. It will clearly indicate what may or may not have a large influence on results. The combination also provides the ability to examine risks during manufacturing. This can help the user understand what to avoid during production. For example, statistical analysis can help users determine if they will avoid an unacceptable combination of input variables. This is useful because an “optimized” parameter isn’t very beneficial if the process is not robust.
Customer requirements are always evolving. They are driven by the need for higher-performing systems. Forgers must meet these goals while determining the best conditions for economical and efficient production. Simulation will continue to take a critical role in the development of forging and beyond. Modeling tools supporting multiple-operation chains are essential to this task. To be effective, simulation software must be efficient, capable and informative. It should be flexible enough to adapt to new iterations, operations or process types. Support for integrating microstructure modeling, DOE and optimization is ideal.
Future simulation tools will build upon the state-of-the-art framework found in today’s software. Material modeling will be the thread through the process chain, with developments continuing for decades. Tools will provide the ability to span operations from initial casting through the final part in service. DOE and optimization will gain widespread use in the study of material, geometry or process changes on system performance. Probabilistic events will be considered, allowing outliers to be factored into development decisions. Finally, in-service product-life predictions will include the manufacturing history. Objectives in forging design will continue to move further downstream in the process chain for years to come.
Co-author Jim Miller is principal research scientist at Scientific Forming Technologies Corporation (SFTC), Columbus, Ohio. He may be reached at 614-451-8330 or firstname.lastname@example.org. Co-author John Walters is vice president of SFTC and a frequent contributor to FORGE. He may be reached at 614-451-8330 or email@example.com.