Abstract

Collaborative design is a recursive process, wherein multiple engineering disciplines iteratively pursue targeted goals. The collaborative process mandates the sharing of information, enabling performance assessments for negotiation of requirement trade-offs. A layered architecture supports collaboration across the missile's subsystems allowing for optimization of critical product parameters. The multidisciplinary optimization expedites trades between performance and product resources such as mission performance, system cost, computational throughput, and memory capacity. We propose an innovative process for missile design using collaborative system design margin analysis with multidisciplinary optimization. A core principle of design margin analysis is the disciplined allocation of performance margins to critical parameters at the system level. This paradigm assures consistent performance and reliability while optimizing key metrics, especially cost. Critical to attaining that core principle is the ready access and traceability of all critical parameters at each subsystem level and for all hardware, software, and firmware components. Additionally, the collaborative system design margin analysis with multidisciplinary optimization framework analyzes and re-allocates design margins to optimize performance parameters in a collaborate manner. This article proposes to demonstrate engineering methods for rigorous evaluation and effective communication of system performance between design disciplines. Communication begins with consistent terminology throughout the design process. Our demonstration will consist of two focus areas: reliable performance measurement and robust design evaluation. Specifically, the article will show collaborative system design margin analysis with multidisciplinary optimization effectivity using signal processing examples. Prior robust design methodologies by Taguchi in conjunction with a common view of system performance lay a framework for collaborative design margin with a constrained optimization approach. Each critical engineering decision is viewed with a perspective of overall system performance, quality, and cost. The following design trades are used for demonstration; probability of target acquisition, as a function of seeker complexity and target classification capability; signal processing distortion, as a function of computational complexity; and using phase noise margin to optimize the signal processing electronics. Currently, the authors have developed a collaborative design infrastructure to demonstrate collaborative system design margin analysis with multidisciplinary optimization principles and their efficacy. The analyses and trade results of each of the design examples will highlight design options with acceptable performance as a function of applicable resources.