Simple Solutions That Work! Issue 16

FLEXIBLE MANUFACTURING & ENGINEERING TRENDS 31 DAVID C. SCHMIDT Vice President Finite Solutions, Inc. ARTICLE TAKEAWAYS: • Casting Simulation is much more efficient than shop-floor trial-and-error • Optimization can maximize simulation paybacks • For larger production volumes, savings of over $100,000 in melt costs are possible C asting process simulation has been used by many foundries to design the process for production of castings before castings are made or before equipment is built or altered. Computer modeling has the ability to evaluate process designs in much less time, and at much less cost, than building equipment and producing sample castings. In effect, we have replaced the traditional trial-and-error on the foundry floor with trial-and-error on the computer. The advantage is that the time and cost have been reduced. However, we are still dependent upon the foundry engineer to interpret simulation results and decide what changes are required for the next design iteration. And, once an acceptable result has been achieved, we still do not know if the result is optimum. For example, is this the smallest riser size that would produce a sound casting, or could we have gone smaller? To advance beyond the trial- and-error stage, OPTICast ™ was developed to apply optimization methods to simulation, so that the design of a given casting with its rigging could be automatically modified to produce an optimum condition, thereby maximizing simulation payback. Optimization requires the identification of three basic parameters: 1. Design Variables These are features of a design that can change as the system searches for an optimum condition. Design variables may be geometric features such as the diameter and height of a riser. They may also be process specifications such as the metal pouring temperature. For geometric variables, a scaling factor is used on both horizontal and vertical dimensions to adjust the feature size within an operating ‘envelope.’ For process data, you specify the allowable range for that item. 2. Constraints Constraints are values of process data above or below which a result is not allowed. Constraints may be specified as a minimum or a maximum condition value. One or more constraints may be specified for each optimization run. An example would be a maximum allowable porosity level. 3. Objective Function The objective function specifies what is trying to be achieved with a given process design. The user selects an objective function and specifies whether the function is to be minimized or maximized. You might select minimizing shrinkage porosity, or you might want to maximize process yield. Only one objective function can be specified for each optimization run. OPTIMIZATION SEQUENCE Optimization takes place according to the process flow diagram shown in Figure 1. The sequence of an optimization run is, first, for the user to create an initial process design, i.e., a three-dimensional model of the casting with gating and risering, and all relevant material data. This is the same data required for any casting simulation. The user then selects the design variables, constraints and objective function and launches an optimization run. Optimization consists of running a series of simulations automatically, varying the values of the design variables, checking to make sure that constraints are not violated, Process Optimization to Maximize Simulation Payback Continued on next page