Toward cost-efficient tolerancing of 3D-printed parts: a novel methodology for the development of tolerance-cost models for fused layer modeling

Tolerance allocation methods significantly contribute to the qualification of Additive Manufacturing (AM) for (small-)series production ensuring high performance and efficiency. However, their usage prerequisites the availability of quantitative, reliable information on the impact of the assigned tolerances on the resulting manufacturing costs. The given article proposes a novel methodology for the systematic development of tolerance-cost curves for a cost-efficient tolerancing of 3D-printed parts. The proposed structured workflow aims at serving as a general guideline for both researchers and practitioners, while the exemplarily chosen perspective from Fused Layer Modeling (FLM) illustrates its adaption to a specific AM technology. The indirect, non-apparent interrelations between tolerances and resulting costs are modelled with the aid of an activity-based cost model, whereas the individual costs elements are mapped as function of the values for the machine-specific process parameters for AM, e.g., layer height or printing speed, which are required to achieve the assigned design tolerances. The total procedure covers all relevant steps, viz. the identification and quantification of the single cost items, the design of benchmark artifacts, adapted to given manufacturing and measuring techniques, the empirical determination of data on cost and geometrical accuracy by design of experiments and tolerance-cost curves. Its exemplary application to an academic use case shows its general applicability and benefits, but also its current limitations.

Environmental and Economic Impact of Retrofitting Techniques to Prevent Out-of-Plane Failure Modes of Unreinforced Masonry Buildings

This paper presents an innovative methodology to assess the economic and environmental impact of integrated interventions, namely solutions that improve both structural and energy performance of existing masonry buildings, preventing out-of-plane modes and increasing their energy efficiency. The procedure allows the assessment of the environmental and the economic normalized costs of each integrated intervention, considering seismic and energy-saving indicators. In addition, the work introduces in relative or absolute terms two original indicators, associated with seismic displacement and thermal transmittance. The iso-cost curves so derived are thus a powerful tool to compare alternative solutions, aiming to identify the most advantageous one. In fact, iso-cost curves can be used with a twofold objective: to determine the optimal integrated intervention associated with a given economic/environmental impact, or, as an alternative, to derive the pairs of seismic and energy performance indicators associated with a given budget. The analysis of a somehow relevant case study reveals that small energy savings could imply excessive environmental impacts, disproportionally increasing the carbon footprint characterizing each intervention. Iso-cost curves in terms of absolute indicators are more suitable for assessing the effects of varying acceleration demands on a given building, while iso-cost curves in terms of relative indicators are more readable to consider a plurality of cases, located in different sites. The promising results confirm the effectiveness of the proposed method, stimulating further studies.

Application of Particle Swarm Optimization in Solving Economic Dispatch with Multiple Fuel Options

Minimizing electricity generation cost which includes fuel cost, emission cost, operation/maintenance cost and network loss cost of multiple operating units has been a major issue in the power sector. The economic dispatch has the objective of allocating different loads to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. Conventional optimization methods assume generator cost curves to be continuous and monotonically increasing, but modern generators have a variety of nonlinearities in their cost curves making this assumption inaccurate, and the resulting approximate dispatches cause a lot of revenue loss. Computational intelligence optimization like Particle Swarm Optimization performs better for such problems. To know the effectiveness and efficiency in solving economic dispatch, this paper proposes the application of particle swarm optimization. The mathematical model of economic dispatch is developed and then, Particle Swarm Optimization is developed to solve the economic dispatch problem using 3-generator and 6-generator system with multiple fuel option. The test results clearly demonstrated that particle swarm optimization which is capable of achieving global solutions is simple, excellent computationally efficiency and has better and stable dynamic convergence characteristics with a high probability.

Moving away from the "unit cost". Predicting country-specific average cost curves of VMMC services accounting for variations in service delivery platforms in sub-Saharan Africa

Background One critical element to optimize funding decisions involves the cost and efficiency implications of implementing alternative program components and configurations. Program planners, policy makers and funders alike are in need of relevant, strategic data and analyses to help them plan and implement effective and efficient programs. Contrary to widely accepted conceptions in both policy and academic arenas, average costs per service (so-called "unit costs") vary considerably across implementation settings and facilities. The objective of this work is twofold: 1) to estimate the variation of VMMC unit costs across service delivery platforms (SDP) in Sub-Saharan countries, and 2) to develop and validate a strategy to extrapolate unit costs to settings for which no data exists. Methods We identified high-quality VMMC cost studies through a literature review. Authors were contacted to request the facility-level datasets (primary data) underlying their results. We standardized the disparate datasets into an aggregated database which included 228 facilities in eight countries. We estimated multivariate models to assess the correlation between VMMC unit costs and scale, while simultaneously accounting for the influence of the SDP (which we defined as all possible combinations of type of facility, ownership, urbanicity, and country), on the unit cost variation. We defined SDP as any combination of such four characteristics. Finally, we extrapolated VMMC unit costs for all SDPs in 13 countries, including those not contained in our dataset. Results The average unit cost was 73 USD (IQR: 28.3, 100.7). South Africa showed the highest within-country cost variation, as well as the highest mean unit cost (135 USD). Uganda and Namibia had minimal within-country cost variation, and Uganda had the lowest mean VMMC unit cost (22 USD). Our results showed evidence consistent with economies of scale. Private ownership and Hospitals were significant determinants of higher unit costs. By identifying key cost drivers, including country- and facility-level characteristics, as well as the effects of scale we developed econometric models to estimate unit cost curves for VMMC services in a variety of clinical and geographical settings. Conclusion While our study did not produce new empirical data, our results did increase by a tenfold the availability of unit costs estimates for 128 SDPs in 14 priority countries for VMMC. It is to our knowledge, the most comprehensive analysis of VMMC unit costs to date. Furthermore, we provide a proof of concept of the ability to generate predictive cost estimates for settings where empirical data does not exist.