Aircraft Tolerance Optimization Considering Quality, Manufacturing & Performance

Abstract

Manufacturing tolerance allocation is a design challenge that plays an important role in balancing the cost and weight objectives for an aircraft. The purpose of this paper is to explore an approach to optimize manufacturing tolerances by combining the individual objectives of the quality, manufacturing and design teams. We illustrate this approach on a fatigue critical lap joint structure that consists of a wing spar and a strap that must tolerate the manufacturing errors associated with location and size of the fastener holes. These errors are modeled with industrial data collected from the wing assemblies of a business jet. A cost model is formulated in terms of the quality cost, manufacturing cost and performance cost, and optimal tolerance is found by minimizing the sum of these costs (i.e. total cost). It was found that as the aircraft size grows bigger the weight increases more quickly than the quality cost requiring the use of tighter tolerance. A sensitivity analysis is also performed to identify the input variables that have significant impact on the optimal tolerance and corresponding total cost. Nomenclatured = Fastener hole diameter e = Fastener edge distance h = Spar height L = Spar length nf = Total no. of fasteners nf-pf = Total no. of fasteners per foot PQN = Probability of quality notification PCV = Probability of constraint violation t = Thickness T = Tolerance w = Width w 0 = Zero tolerance width W = Weight Iini = Initial inspection interval I*ini = Initial inspection constraint C = Cost ET = Engineering time LT = Labor time EC = Engineering cost (hourly) LC = Labor time cost (hourly) λ = Tradeoff ratio

Probabilistic Manufacturing Tolerance Optimization of Damage-Tolerant Aircraft Structures Using Measured Data

“…The edge distance deviation data are then modeled with a semiparametric distribution. More details on the semiparametric distribution are provided in [20]. The edge distance data can also be approximated by a logistic distribution with the following parameters: (location) μ −5.5 × 10 −4 in: and (scale) σ 0.0138 in: A normal fit to data has a mean of μ −7.9 × 10 −4 in: and a standard deviation of σ 0.0248 in:…”

“…where P QR (probability of quality review), C QRPF (cost of quality review per fastener), P CV (probability of constraint violation), and C Al (per pound cost of aluminum) are uncertain. We elect to treat the cost of aluminum C Al as deterministic because the optimum tolerance and total cost are not sensitive to C Al , e.g., a 1% change in C Al only leads to a 0.1% change in C total and a 0.02% change in the optimal tolerance (refer to [20] for more details). The cost of a useful load C UL (ratio of sales price to useful load) is also treated as deterministic because exact values of the promised sales price and promised useful load are fully known at the time of tolerance design.…”