2026 ASEE Annual Conference & Exposition

Engineers must deal with design parameters that need to be carefully selected to achieve design objectives. In electronic circuit design, for example, design engineers must choose the proper values for resistance, inductance, capacitance etc. to ensure the circuit performs certain tasks such as filtering, amplification, regulating voltages, and signal conditioning. In mechanical design, the design engineer must choose proper values for mass, geometrical dimensions, material strength, etc. Design engineers would do calculations and/or simulations to ensure the products work as designed. However, this is not enough, because when the designs are implemented through manufacturing processes variations in the design parameters will be introduced. For example, if a resistor is used in an electronic circuit design, the resistance value of the resistor populated on the circuit board will not be equal to the designed value exactly. Instead, resistance is the nominal value +/- a small variation. The design engineer needs to consider these kinds of variations in all design parameters.
Common practice in industry reveals an important aspect of verifying design requirements, that is, it involves the use of statistics. In theory, it is impossible to prove that requirements will be met 100% of the time. There are always failures related to any product. The real question is whether the requirements are met for a given confidence level. For example, can we prove that the requirement will be met 95% of time?
If we can conduct some statistical analysis during the early stage of designing products, for example during the feasibility study or before mass production of the products, the process of verifying the design requirement can be greatly improved. The cost will go down, and it will take less time to complete the verification. There is such a tool that can be used for exactly this purpose: Monte Carlo Simulation.
The concept of using Monte Carlo Simulation to verify an engineering design is important for Engineering Technology students. Since majority of Engineering Technology students graduated with a four-year BS degree will work as engineers, not technicians, they will have product design responsibilities.
In this paper, we present a simple Low Pass Filter circuit design to illustrate how to apply the Monte Carlo Simulation methodology. Monte Carlo Simulation was a Lean Six Sigma tool introduced in a course in the ESET program (ESET 329, Applied Statistics and Six Sigma) at Texas A&M University, and it worked well for students in the class.