Strategies for reuse of components are important in order to create a closed loop manufacturing system. Over decades, the notion has been gaining ground due to environmental and legislative reasons. Reuse of components is desirable and in many cases might be economically beneficial. However, the implementation of reuse strategies has been hindered by the requirement of reliable methodologies to assess the remaining life and reuse potential of used components. The estimation of the remaining life is problematic as the useful life of a component is affected by several causes of obsolescence. The common causes are due to physical and technological issues. So far, little research has attempted to address these issues simultaneously, and integrating them. This thesis seeks to develop methodologies that aid in predicting the integrated remaining lifetime of used components. There are three core parts of this research. First, the methodology determines the remaining life of used components from the physical lifetime perspective. This was derived from the estimation of physical failure using failure rate data, and the statistical analysis of usage intensity age as obtained from customers survey. Second, the research presents the use of the technological forecasting technique to predict the remaining technological life. As it is influenced by the technology progress, the forecast was developed on the basis of product technology clusters and market trend extrapolation analysis. Finally, the resulting estimations from the two aspects were combined to obtain an integrated assessment for estimating the remaining life of components. The potential for components in a product to be reused is justified when the remaining life is greater than the average expected lifespan of the product. Two cases of domestic appliances – televisions and washing machines were used to highlight and demonstrate the validity of the proposed methodology. The results show that the proposed method provides the practitioners with a promising tool for end-of-life decision making. This is in particularly attractive when used as a preliminary decision support tool prior to the time consuming and costly processes such as disassembly and quality testing.

The use of cutting fluids in machining process is very essential for achieving desired machining performance. Due to the strict environmental protection laws now in effect, there is a wide-scale evaluation of the use of cutting fluids in machining. Consequently, minimal quantity lubrication (MQL), which uses very small quantity of cutting fluids and still offers the same functionality as flood cooling, can be considered as an alternative solution. This thesis presents an experimental study of face milling of automotive aluminum alloy A380 under four different lubrication/cooling conditions: dry cutting, flood cooling, MQL (Oil), and MQL (Water). Experiments were design using Taguchi method for design of experiments. Empirical models for predicting surface roughness and cutting forces were developed for these four conditions in terms of cutting speed, feed and depth of cut. Optimization technique using Genetic Algorithms (GA) was used to optimize performance measures under different lubrication/cooling conditions, based on a comprehensive optimization criterion integrating the effects of all major machining performance measures. Case studies are also presented for two pass face milling operation comparing flood cooling condition with MQL. The comparison of the results predicted by the models developed in this work shows that the cutting force for MQL (Oil) is either lower or equal to flood cooling. The surface roughness for MQL (Oil) is comparable to flood cooling for higher range of feed and depth of cut. A comparison of the optimized results from the case studies, based on value of utility function, shows that the optimum point for two pass face milling operation having MQL (Oil) as finish pass has highest utility function value.

Strategies for reuse of components are important in order to create a closed loop manufacturing system. Over decades, the notion has been gaining ground due to environmental and legislative reasons. Reuse of components is desirable and in many cases might be economically beneficial. However, the implementation of reuse strategies has been hindered by the requirement of reliable methodologies to assess the remaining life and reuse potential of used components. The estimation of the remaining life is problematic as the useful life of a component is affected by several causes of obsolescence. The common causes are due to physical and technological issues. So far, little research has attempted to address these issues simultaneously, and integrating them. This thesis seeks to develop methodologies that aid in predicting the integrated remaining lifetime of used components. There are three core parts of this research. First, the methodology determines the remaining life of used components from the physical lifetime perspective. This was derived from the estimation of physical failure using failure rate data, and the statistical analysis of usage intensity age as obtained from customers survey. Second, the research presents the use of the technological forecasting technique to predict the remaining technological life. As it is influenced by the technology progress, the forecast was developed on the basis of product technology clusters and market trend extrapolation analysis. Finally, the resulting estimations from the two aspects were combined to obtain an integrated assessment for estimating the remaining life of components. The potential for components in a product to be reused is justified when the remaining life is greater than the average expected lifespan of the product. Two cases of domestic appliances – televisions and washing machines were used to highlight and demonstrate the validity of the proposed methodology. The results show that the proposed method provides the practitioners with a promising tool for end-of-life decision making. This is in particularly attractive when used as a preliminary decision support tool prior to the time consuming and costly processes such as disassembly and quality testing.

The manufacturing industry remains a critical force in the quest for global sustainability. An increasing number of companies are modifying their operations in favor of more sustainable practices. It is hugely important that manufacturers, irrespective of the subsector they belong to, or their organizational size, implement practices that reduce or eliminate negative environmental, social and economic impacts generated by their manufacturing operations. Consequently, scholars have called for additional studies concerning sustainable manufacturing practices, not only to address the paucity of related literature, but also to contribute to practitioners’ understanding of how to incorporate sustainability into their operations. However, apart from expanding the knowledge of sustainable manufacturing practices, it is first key to understand the ground set of values, or principles, behind sustainable manufacturing operations. For that reason, the purpose of this thesis is to contribute to the existing body of knowledge regarding sustainable manufacturing principles and practices. The results presented in this thesis are based on three studies: a systematic literature review exploring sustainability principles applicable to manufacturing settings, and two empirical studies addressing sustainable manufacturing practices. In general, it is concluded from the literature that there is a little knowledge about sustainability principles from a manufacturing perspective. In relation to the most common sustainable manufacturing practices, it is concluded that these practices mainly refer to energy and material management, and waste management. Similarly, the study of the adherence of sustainable manufacturing practices to sustainable production principles concluded that the principles concerning energy and materials conservation, and waste management were found to create the highest number of practices. Although most manufacturers still engage in reactive sustainable manufacturing practices driven by regulatory and market pressures, some industrial sectors were found to be more prone to develop proactive sustainable manufacturing strategies than others. Furthermore, SMEs were found to lag behind large organizations regarding adherence to sustainable manufacturing principles.

Cette thèse se focalise sur la proposition et l’application des approches pour la modélisation de l’éco-fabrication. Ces approches permettent de préparer et simuler une démarche de fabrication des produits en assurant le couplage entre les objectifs écologiques et économiques.Les approches développées dans cette thèse sont basées sur les notions d’aide à la décision ainsi que l’optimisation multi objectifs. L’aide à la décision permet l’intervention en deux différents niveaux : le choix des impacts environnementaux à quantifier ainsi que le choix du scénario final de fabrication. Pour l’optimisation multi objectifs, elle assure le couplage entre les deux piliers principaux de l’éco-fabrication : l’écologie et l’économie. Au niveau de l’aide à la décision multi critères, les méthodes Evamix et Promethee ont été appliqués, tandis que les essaims particulaires ont été développés dans le cadre de l’optimisation multi objectifs.Ces approches ont été appliquées tout d’abord aux quelques opérations d’usinage : tournage et fraisage. Finalement, la chaîne de fabrication de l’acide phosphorique ainsi que celle d’acide sulfurique ont été le sujet de l’application des deux approches développées. / This thesis focuses on the proposal and implementation of approaches for modeling sustainable manufacturing. These approaches are used to prepare and simulate a process of manufacturing products providing coupling between environmental and economic objectives.The approaches developed in this thesis are based on the concepts of decision support as well as multi-objective optimization. The decision support allows intervention in two different levels: the choice of indicator to quantify the environmental impacts and the choice of the final manufacturing scenario. For multi-objective optimization, it provides the coupling between the two main pillars of sustainable manufacturing: ecology and economy. In terms of multi criteria decision aid methods, Evamix and Promethee were applied, while particulate swarms were developed as part of the multi-objective optimization. These approaches have been applied initially to some machining operations: turning and milling. Finally, the production line of phosphoric acid and sulfuric acid were the subject of application of the two approaches developed.

The U.N. projects the world population to reach nearly 10 billion people by 2050, which will cause demand for manufactured goods to reach unforeseen levels. In order for us to produce the goods to support an equitable future, the methods in which we manufacture those goods must radically change. The emerging Circular Economy (CE) concept for production systems has promised to drastically increase economic/business value by significantly reducing the world’s resource consumption and negative environmental impacts. However, CE is inherently limited because of its emphasis on recycling and reuse of materials. CE does not address the holistic changes needed across all of the fundamental elements of manufacturing: products, processes, and systems. Therefore, a paradigm shift is required for moving from sustainment to sustainability to “produce more with less” through smart, innovative and transformative convergent manufacturing approaches rooted in redesigning next generation manufacturing infrastructure. This PhD research proposes the Helical Economy (HE) concept as a novel extension to CE. The proposed HE concepts shift the CE’s status quo paradigm away from post-use recovery for recycling and reuse and towards redesigning manufacturing infrastructure at product, process, and system levels, while leveraging IoT-enabled data infrastructures and an upskilled workforce. This research starts with the conceptual overview and a framework for implementing HE in the discrete product manufacturing domain by establishing the future state vision of the Helical Economy Manufacturing Method (HEMM). The work then analyzes two components of the framework in detail: designing next-generation products and next-generation IoT-enabled data infrastructures. The major research problems that need to be solved in these subcomponents are identified in order to make near-term progress towards the HEMM. The work then proceeds with the development and discussion of initial methods for addressing these challenges. Each method is demonstrated using an illustrative industry example. Collectively, this initial work establishes the foundational body of knowledge for the HE and the HEMM, provides implementation methods at the product and IoT-enabled data infrastructure levels, and it shows a great potential for HE’s ability to create and maximize sustainable value, optimize resource consumption, and ensure continued technological progress with significant economic growth and innovation. This research work then presents an outlook on the future work needed, as well as calls for industry to support the continued refinement and development of the HEMM through relevant prototype development and subsequent applications.

A simple model that includes energy and carbon dioxide (CO2) emissions in the economics of machining is proposed, which has been published in the highly respected and cited journal, Annals of CIRP (International Academy for Production Engineering). This is a timely analysis in current government discussions on a proposed carbon tax or a carbon cap and trade regime and greater energy efficiency. The new cost model is based on life cycle analysis methodology for the initial part production. An illustrative example is given showing that the cheapest electrical grid should not be chosen, if it also has the highest CO2 emissions. Accurate pricing is important, because the more expensive product was highly dependent on the carbon price. A comprehensive review of machining economic models is covered. However, there is a dearth of actual machining data in the literature. This work includes studies in milling and single point incremental forming (SPIF) which can be used by other manufacturing engineers in their machining economic model development. The first milling study involved simple straight cuts. In general, as feed rate (FD) increased (increasing the material removal rate, MRR), the energy consumed decreased as process time decreased. In contrast, as spindle speed (N) increased, energy consumed increased, since more power is drawn by the motor, without a process time reduction. Given the inverse power relationship observed for the time, energy, process CO2 and cost against MRR, the recommended parameters were the same at the highest FD and lowest N permissible. In the second milling study with constant N for a more complex part (sprocket), similar relationships were observed. However, for sprockets made at constant chip load (allowing FD and N to change together), there were varying prescribed MRRs for time, energy, process CO2 and cost minimization. The SPIF studies showed a similar relationship to the constant N milling, and, that results for a simple part can be extrapolated to improve efficiency in more complex parts. Finally, although the energy and carbon costs represented a small contribution to the final cost, their significance increased for higher efficiency parameters or user conditions, e.g. low labour rate. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2011-12-03 19:58:07.76

The development of a sustainable manufacturing process requires a comprehensive evaluation method and fundamental understanding of the processes. Coolant application is a critical sustainability concern in the widely used machining process. Cryogenic machining is considered a candidate for sustainable coolant application. However, the lack of comprehensive evaluation methods leaves significant uncertainties about the overall sustainability performance of cryogenic machining. Also, the lack of practical application guidelines based on scientific understanding of the heat transfer mechanism in cryogenic machining limits the process optimization from achieving the most sustainable performance. In this dissertation, based on a proposed Process Sustainability Index (ProcSI) methodology, the sustainability performance of the cryogenic machining process is optimized with application guidelines established by scientific modeling of the heat transfer mechanism in the process. Based on the experimental results, the process optimization is carried out with Genetic Algorithm (GA). The metrics-based ProcSI method considers all three major aspects of sustainable manufacturing, namely economy, environment and society, based on the 6R concept and the total life-cycle aspect. There are sixty five metrics, categorized into six major clusters. Data for all relavant metrics are collected, normalized, weighted, and then aggregated to form the ProcSI score, as an overall judgment for the sustainability performance of the process. The ProcSI method focuses on the process design as a manufacturer’s aspect, hoping to improve the sustainability performance of the manufactured products and the manufacturing system. A heat transfer analysis of cryogenic machining for a flank-side liquid nitrogen jet delivery is carried out. This is performed by micro-scale high-speed temperature measurement experiments. The experimental results are processed with an innovative inverse heat transfer solution method to calculate the surface heat transfer coefficient at various locations throughout a wide temperature range. Based on the results, the application guidelines, including suggestions of a minimal, but sufficient, coolant flow rate are established. Cryogenic machining experiments are carried out, and ProcSI evaluation is applied to the experimental scenario. Based on the ProcSI evaluation, the optimization process implemented with GA provides optimal machining process parameters for minimum manufacturing cost, minimal energy consumption, or the best sustainability performance.

The approach advocated in this work implements energy/exergy analysis and indirectly an irreversibility evaluation to a continuous manufacturing process involving discrete net shape production of compact heat exchangers through a complex controlled atmosphere brazing (CAB) process. The system under consideration involves fifteen cells of a continuous ramp-up heating, melting, reactive flow, isothermal dwell, and rapid quench solidification processing sequence during a controlled atmosphere brazing of aluminum compact heat exchangers. Detailed mass, energy, and exergy balances were performed. The irreversibility sources were identified and the quality of energy utilization at different processing steps determined. It is demonstrated that advanced thermodynamics metrics based on entropy generation may indicate the level of sustainable energy utilization of transient open systems, such as in manufacturing. This indicator may be related to particular property uniformity during materials processing. In such a case, the property uniformity would indicate systems distance from equilibrium, i.e., from the process sustainable energy utilization level. This idea is applied to net shape manufacturing process considered. A metric based on exergy destruction is devised to relate the heat exchanger temperature uniformity and the quality. The idea advocated in this thesis will represent the coherent framework for developing energy efficient, economically affordable and environmentally friendly manufacturing technology.

Sustainable manufacturing involves utilizing energy resources efficiently. Currently, the state of sustainability for a given manufacturing process is described by most in a qualitative sense as opposed to using quantitative metrics. This thesis offers a segment of analysis needed to understand the state of sustainability in the context of energy resource utilization. This was accomplished by measuring the order of magnitude difference between the energy consumption of a manufacturing process vs. the theoretical minimum amount of energy required to complete the same task (aluminum T-joint bond). This analysis was completed for a TIG welding process and a controlled atmosphere brazing (CAB) process. Also, the energy Sankey diagram was constructed for the TIG welding process. The TIG welding process and CAB process consumed an average of 136.1 ± 16.5 kJ and 6,830 ± 77 kJ respectively to bond the same sample. The TIG welding process consumed O(102 kJ) more than the theoretical minimum amount needed to complete the same bond while the CAB process consumed O(104 kJ) more than the theoretical minimum. In the context of energy consumption, there are sizable margins for improvement for both metal joining processes analyzed in this study.