Flexible aggregation operators to support hierarchization of Engineering Characteristics in QFD

Quality Function Deployment (QFD) is a management tool for organizing and conducting design activities of new products and/or services together with their relevant production and/or supply processes, starting from the requirements directly expressed by the end-users. It is organized in a series of operative steps which drive from the collection of the customer needs to the definition of the technical characteristics of the production/supply processes. The first step entails the construction of the House of Quality (HoQ), a planning matrix translating the Customer Requirements (CRs) into measurable product/service technical characteristics (Engineering Characteristics – ECs). One of the main goals of this step is to transform CR importances into an EC prioritization. A robust evaluation method should consider the relationships between CRs and ECs while determining the importance levels of ECs in the HoQ. In traditional approaches, such as for example Independent Scoring Method, ordinal information is arbitrarily converted in cardinal information introducing a series of controversial assumptions. Actually, the current scientific literature presents a number of possible solutions to this problem, but the question of attributing scalar properties to information collected on ordinal scales is far from being settled. This paper proposes a method based on ME-MCDM techniques (Multi Expert / Multiple Criteria Decision Making), which is able to compute EC prioritization without operating an artificial numerical codification of the information contained in the HoQ. After a general description of the theoretical principles of the method, a series of application examples are presented and discussed

The SLAC Polarized Electron Source

The SLAC PES, developed in the early 1990s for the SLC, has been in continuous use since 1992, during which time it has undergone numerous upgrades. The upgrades include improved cathodes with their matching laser systems, modified activation techniques and better diagnostics. The source itself and its performance with these upgrades will be described with special attention given to recent high-intensity long-pulse operation for the E-158 fixed-target parity-violating experiment.Comment: 6 pages, 4 figures, Workshop on Polarized Electron Sources and Polarimeters (PESP 2002), September 4-6, 2002, Danvers, M

Analysis of residual plastic deformation of blanked sheets out of automotive aluminium alloys through hardness map

Reducing overall vehicle weight is essential to reduce fuel consumption and pollutant emission and to improve noise, vibration, and harshness (NVH) performances. The substitution with lighter alloys can involve the grand majority of vehicle components, depending on the market sector. In several applications, e.g., chassis, pulleys, and viscodampers, metal sheets are formed in several steps, each of whom work-hardens the material reducing the available residual plasticity. Typically, the process is designed via FEM, whose results are affected by the initial conditions, often neglected, and is performed on pre-processed materials from suppliers. In this regard, correctly simulating the first step of the process is critical. However, the related initial conditions, in terms of residual stress and strain induced by former preliminary operations, are often neglected. This work proposes a quick and economical experimental procedure based on a hardness map to estimate initial conditions and to validate FEM results. The procedure allows evaluating the material's residual plasticity, which is necessary to process engineers to design following manufacturing steps. The approach is demonstrated on an industrially relevant case study, i.e., the blanking of an AA 5754, in use for water pump pulleys

Minimization of defects generation in laser welding process of steel alloy for automotive application

Laser welding (LW) thanks to its flexibility, limited energy consumption and simple realization has a prominent role in several industrial sectors. LW process requires careful parameters' tuning to avoid generating internal defects in the microstructure or a poor weld depth, which reduce the joining mechanical strength and result in waste. This work exploits a supervised machine learning algorithm to optimize the process parameters to minimize the generated defects, while catering for design specifications and tolerances to predict defect generation probability. The work outputs a predictive quality control model to reduce non-destructive controls in the LW of aluminum for automotive applications