Optimal process planning for helical bevel gears using Taguchi design of simulated machining experiments

Abstract

Computer-aided manufacturing systems are considered as ‘what-if’ processors, whereby the user suggests manufacturing features and for each one of them a tool and a machining strategy, whereas the system computes tool paths and generates a computer numerical control program. Given the multitude of factors for which decisions are needed, complexity is high. In addition, given the executive nature of computer-aided manufacturing systems, the user relies on experience in exploring parameter value combinations in order to obtain good results. In this work, structured generation of a representative subset of the parameter combination space is advocated using the Taguchi approach. This is applied to parts that are simpler than the average part, since they consist of a single, repeating, yet geometrically complex, machining feature involving sculptured surfaces, such as helical bevel gears. The lack of multiple features reduces the solution space, enabling a manageable number of experiment runs from which good results for roughing and finishing are attained. Statistical processing of these results also point to relative significance of machining strategy parameters within the particular space explored and for the particular weighted criteria employed, that is, rest material, machining time and gouge volume. Excellent results are obtained in this way, based on the machining simulation capabilities of modern computer-aided manufacturing systems and bypassing the inherent lack of intelligence of the latter.

Optimal Selection of Backside Roughing Parameters of High-Value Components Using Abrasive Jet Processing

“…To obtain the optimal parameter combination in quality characteristics with minimum experiment times and expenses, this research embraces the Taguchi method to prepare an experiment [23][24][25] and selects six processing parameters of abrasive blasting processing techniques as experimental control factors, specifically particularly, platform revolution, gas pressure, nozzle-to-workpiece height, vacuum suction, wax-coated abrasives, additives proportion, and impact angle. If the full factorial experiment is conducted for these research parameter factors, then 36 times and 729 times experiments are required in total.…”

Optimization of forming force and Erichsen index using Taguchi design of experiments: Mathematical models and experimental validation

“…It was found that this method is widely used by researchers, for finding the effects of input factors on output parameters, 1 in various fields such as advanced manufacturing technologies 2–4 and conventional manufacturing processes. 5–10 On the first hand, the integration between this statistical method and numerical analysis was described in order to minimize the iteration number during simulation. Both finite element methods and design of experiments contribute to the optimization of the computed manufacturing processes.…”

Taguchi Techniques as an Effective Simulation-Based Strategy in the Design of Numerical Simulations to Assess Contact Stress in Gerotor Pumps

“…As an example, assistance for the management of defects found in software development by quantifying performance has been proven [38]. The multitude of parameters in Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) are also benefited by the structured generation of a representative subset of them into a solution space, as is shown in bevel gear machining simulations [39]. The mechanical performance can be modeled mathematically by using the finite element methodology, which makes the contact stress a prime candidate for designed experiments using the environment of the software packages.…”