Intelligent Design Tolerance Allocation for Optimum Adaptability to Manufacturing Using a Monte Carlo Approach

The quality of a product is affected by the uncertainties associated with the manufacturing processes according to its design specifications including their nominal values and the corresponding tolerances. The allocation of dimensional tolerances of components in a mechanical assembly is a major concern during the design phase of a product due to importance on its functional behavior, cost implication and manufacturing complexity. This task will be more challenging in today's intelligent manufacturing systems, when the desire is to maximize the adaptabilities in various product life cycle processes to maximize the product quality with a minimum cost. Manufacturing process have shown that the lower manufacturing uncertainty to achieve tighter specified tolerance range, the higher is its cost - generally at an exponential basis. The adequate equilibrium between the desired function characteristics and the manufacturing cost is a fundamental aspect at the design stage of a product, in order to assure its competitiveness - or even its feasibility - in the market. This work intends to present a proposal to increase adaptability of the design specifications with the detected manufacturing uncertainties by developing an intelligent tolerance allocation process. This allows manufacturing cost reduction, easiness of both assembly process and parts, without compromise of the specified mandatory functional features. The proposed methodology use a Monte Carlo simulation for the detected models of the associated manufacturing uncertainties. A implied case study is used to illustrate the effectiveness of the method.