Development of a Computer-Based methodology for tolerance selection and optimization applied to the automotive sector

The design optimization of industrial products has always been an essential activity to improve product quality while reducing time-to-market and production costs. Although cost management is very complex and comprises all phases of the product life cycle, the control of geometrical and dimensional variations, known as Dimensional Management (DM), allows compliance with product and process requirements. Hence, the tolerance-cost optimization becomes the main practice to provide an effective application of Design for Tolerancing (DfT) and Design to Cost (DtC) approaches by enabling a connection between product tolerances and associated manufacturing costs. However, despite the growing interest in this topic, a profitable application in the industry of these techniques is hampered by their complexity: the definition of a systematic framework is the key element to improving design optimization, enhancing the concurrent use of Computer-Aided tools and Model-Based Definition (MBD) practices. The present doctorate research aims to define and develop an integrated methodology for product/process design optimization, to better exploit the new capabilities of advanced simulations and tools. By implementing predictive models and multi-disciplinary optimization, a Computer-Aided Integrated framework for tolerance-cost optimization has been proposed to allow the integration of DfT and DtC approaches and their direct application for the design of automotive components. Several case studies have been considered, with the final application of the integrated framework on a high-performance V12 engine assembly, to achieve both functional targets and cost reduction. From a scientific point of view, the proposed methodology provides an improvement for the tolerance-cost optimization of industrial components. The integration of theoretical approaches and Computer-Aided tools allows to analyse the influence of tolerances on both product performance and manufacturing costs. The case studies proved the suitability of the methodology for its application in the industrial field, providing the identification of further areas for improvement and refinement.

Asset management of wastewater networks

Asset Management (AM) is a set of procedures operable at the strategic-tacticaloperational level, for the management of the physical asset’s performance, associated risks and costs within its whole life-cycle. AM combines the engineering, managerial and informatics points of view. In addition to internal drivers, AM is driven by the demands of customers (social pull) and regulators (environmental mandates and economic considerations). AM can follow either a top-down or a bottom-up approach. Considering rehabilitation planning at the bottom-up level, the main issue would be to rehabilitate the right pipe at the right time with the right technique. Finding the right pipe may be possible and practicable, but determining the timeliness of the rehabilitation and the choice of the techniques adopted to rehabilitate is a bit abstruse. It is a truism that rehabilitating an asset too early is unwise, just as doing it late may have entailed extra expenses en route, in addition to the cost of the exercise of rehabilitation per se. One is confronted with a typical ‘Hamlet-isque dilemma’ – ‘to repair or not to repair’; or put in another way, ‘to replace or not to replace’. The decision in this case is governed by three factors, not necessarily interrelated – quality of customer service, costs and budget in the life cycle of the asset in question. The goal of replacement planning is to find the juncture in the asset’s life cycle where the cost of replacement is balanced by the rising maintenance costs and the declining level of service. System maintenance aims at improving performance and maintaining the asset in good working condition for as long as possible. Effective planning is used to target maintenance activities to meet these goals and minimize costly exigencies. The main objective of this dissertation is to develop a process-model for asset replacement planning. The aim of the model is to determine the optimal pipe replacement year by comparing, temporally, the annual operating and maintenance costs of the existing asset and the annuity of the investment in a new equivalent pipe, at the best market price. It is proposed that risk cost provide an appropriate framework to decide the balance between investment for replacing or operational expenditures for maintaining an asset. The model describes a practical approach to estimate when an asset should be replaced. A comprehensive list of criteria to be considered is outlined, the main criteria being a visà- vis between maintenance and replacement expenditures. The costs to maintain the assets should be described by a cost function related to the asset type, the risks to the safety of people and property owing to declining condition of asset, and the predicted frequency of failures. The cost functions reflect the condition of the existing asset at the time the decision to maintain or replace is taken: age, level of deterioration, risk of failure. The process model is applied in the wastewater network of Oslo, the capital city of Norway, and uses available real-world information to forecast life-cycle costs of maintenance and rehabilitation strategies and support infrastructure management decisions. The case study provides an insight into the various definitions of ‘asset lifetime’ – service life, economic life and physical life. The results recommend that one common value for lifetime should not be applied to the all the pipelines in the stock for investment planning in the long-term period; rather it would be wiser to define different values for different cohorts of pipelines to reduce the uncertainties associated with generalisations for simplification. It is envisaged that more criteria the municipality is able to include, to estimate maintenance costs for the existing assets, the more precise will the estimation of the expected service life be. The ability to include social costs enables to compute the asset life, not only based on its physical characterisation, but also on the sensitivity of network areas to social impact of failures. The type of economic analysis is very sensitive to model parameters that are difficult to determine accurately. The main value of this approach is the effort to demonstrate that it is possible to include, in decision-making, factors as the cost of the risk associated with a decline in level of performance, the level of this deterioration and the asset’s depreciation rate, without looking at age as the sole criterion for making decisions regarding replacements.

Eco-Management and Audit Scheme (EMAS): An evaluation of success factors in the agricultural sector. The case of Italy's Emilia Romagna region.

Different tools have been used to set up and adopt the model for the fulfillment of the objective of this research. 1. The Model The base model that has been used is the Analytical Hierarchy Process (AHP) adapted with the aim to perform a Benefit Cost Analysis. The AHP developed by Thomas Saaty is a multicriteria decision - making technique which decomposes a complex problem into a hierarchy. It is used to derive ratio scales from both discreet and continuous paired comparisons in multilevel hierarchic structures. These comparisons may be taken from actual measurements or from a fundamental scale that reflects the relative strength of preferences and feelings. 2. Tools and methods 2.1. The Expert Choice Software The software Expert Choice is a tool that allows each operator to easily implement the AHP model in every stage of the problem. 2.2. Personal Interviews to the farms For this research, the farms of the region Emilia Romagna certified EMAS have been detected. Information has been given by EMAS center in Wien. Personal interviews have been carried out to each farm in order to have a complete and realistic judgment of each criteria of the hierarchy. 2.3. Questionnaire A supporting questionnaire has also been delivered and used for the interviews . 3. Elaboration of the data After data collection, the data elaboration has taken place. The software support Expert Choice has been used . 4. Results of the Analysis The result of the figures above (vedere altro documento) gives a series of numbers which are fractions of the unit. This has to be interpreted as the relative contribution of each element to the fulfillment of the relative objective. So calculating the Benefits/costs ratio for each alternative the following will be obtained: Alternative One: Implement EMAS Benefits ratio: 0, 877 Costs ratio: 0, 815 Benfit/Cost ratio: 0,877/0,815=1,08 Alternative Two: Not Implement EMAS Benefits ratio: 0,123 Costs ration: 0,185 Benefit/Cost ratio: 0,123/0,185=0,66 As stated above, the alternative with the highest ratio will be the best solution for the organization. This means that the research carried out and the model implemented suggests that EMAS adoption in the agricultural sector is the best alternative. It has to be noted that the ratio is 1,08 which is a relatively low positive value. This shows the fragility of this conclusion and suggests a careful exam of the benefits and costs for each farm before adopting the scheme. On the other part, the result needs to be taken in consideration by the policy makers in order to enhance their intervention regarding the scheme adoption on the agricultural sector. According to the AHP elaboration of judgments we have the following main considerations on Benefits: - Legal compliance seems to be the most important benefit for the agricultural sector since its rank is 0,471 - The next two most important benefits are Improved internal organization (ranking 0,230) followed by Competitive advantage (ranking 0, 221) mostly due to the sub-element Improved image (ranking 0,743) Finally, even though Incentives are not ranked among the most important elements, the financial ones seem to have been decisive on the decision making process. According to the AHP elaboration of judgments we have the following main considerations on Costs: - External costs seem to be largely more important than the internal ones (ranking 0, 857 over 0,143) suggesting that Emas costs over consultancy and verification remain the biggest obstacle. - The implementation of the EMS is the most challenging element regarding the internal costs (ranking 0,750).

The use of satellite remote sensing for flood risk management

Over the last decades the impact of natural disasters to the global environment is becoming more and more severe. The number of disasters has dramatically increased, as well as the cost to the global economy and the number of people affected. Among the natural disaster, flood catastrophes are considered to be the most costly, devastating, broad extent and frequent, because of the tremendous fatalities, injuries, property damage, economic and social disruption they cause to the humankind. In the last thirty years, the World has suffered from severe flooding and the huge impact of floods has caused hundreds of thousands of deaths, destruction of infrastructures, disruption of economic activity and the loss of property for worth billions of dollars. In this context, satellite remote sensing, along with Geographic Information Systems (GIS), has become a key tool in flood risk management analysis. Remote sensing for supporting various aspects of flood risk management was investigated in the present thesis. In particular, the research focused on the use of satellite images for flood mapping and monitoring, damage assessment and risk assessment. The contribution of satellite remote sensing for the delineation of flood prone zones, the identification of damaged areas and the development of hazard maps was explored referring to selected cases of study.

Characterization and management of particular classes of waste at a regional level. case study: Marche region

There are various methods to analyse waste, which differ from each other according to the level of detail of the compositio. Waste composed by plastic and used for packaging, for example, can be classified by chemical composition of the polymer used for the specific product. At a more basal level, before dividing a waste according to the specific chemical material of which it is composed it is possible and also important to classify it according to the material category. So, if the secondary aim is to consider the particular polymer that constitutes a plastic waste, or what kind of natural polymer composes a specific waste made of wood, the first aim is to classify the product category of the material that makes up the waste, so, if it is wood made, or plastic, or glass made or metal, or organic. There are not specific instruments to make this subdivision, not specific chemical tests, but only a manual recognition of the material that makes up the product or waste. The first steps of this study is a recognition of the materials of which the waste is composed, the second is a the quantification of differentiated and unsorted waste produced in the area under study, the third is a mass balance of the portions of waste sent for recovery in order to obtain information on quantities that can be effectively recovered and ready for new life cycle as raw material; the fourth and last step is an environmental assessment that provides information on the environmental cost of the recovery process. This process scheme is applied to various specific kinds of waste from separate collection generated in a specific area with the aim to find a model analysis appliable to other portions of territory in order to improve knowledge of recovery technologies.

Thermal and air quality modeling of an electric vehicle cabin with low-cost sensors and reinforcement learning

The design process of any electric vehicle system has to be oriented towards the best energy efficiency, together with the constraint of maintaining comfort in the vehicle cabin. Main aim of this study is to research the best thermal management solution in terms of HVAC efficiency without compromising occupant’s comfort and internal air quality. An Arduino controlled Low Cost System of Sensors was developed and compared against reference instrumentation (average R-squared of 0.92) and then used to characterise the vehicle cabin in real parking and driving conditions trials. Data on the energy use of the HVAC was retrieved from the car On-Board Diagnostic port. Energy savings using recirculation can reach 30 %, but pollutants concentration in the cabin builds up in this operating mode. Moreover, the temperature profile appeared strongly nonuniform with air temperature differences up to 10° C. Optimisation methods often require a high number of runs to find the optimal configuration of the system. Fast models proved to be beneficial for these task, while CFD-1D model are usually slower despite the higher level of detail provided. In this work, the collected dataset was used to train a fast ML model of both cabin and HVAC using linear regression. Average scaled RMSE over all trials is 0.4 %, while computation time is 0.0077 ms for each second of simulated time on a laptop computer. Finally, a reinforcement learning environment was built in OpenAI and Stable-Baselines3 using the built-in Proximal Policy Optimisation algorithm to update the policy and seek for the best compromise between comfort, air quality and energy reward terms. The learning curves show an oscillating behaviour overall, with only 2 experiments behaving as expected even if too slow. This result leaves large room for improvement, ranging from the reward function engineering to the expansion of the ML model.