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Guest Editorial

J. Manuf. Sci. Eng. 2018;141(2):020301-020301-0. doi:10.1115/1.4042187.
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This special issue on Sustainable Life Cycle Engineering was jointly sponsored by the ASME Design Engineering Division (DED) and Manufacturing Engineering Division (MED) with the aim of disseminating the latest research findings on various design and manufacturing-focused principles related to sustainability, such as reduction of materials and energy use, reduction of undesirable environmental emissions, closing the product lifecycle loop, and new business models and paradigms to influence sustainable manufacturing practices.

Commentary by Dr. Valentin Fuster

Research Papers

J. Manuf. Sci. Eng. 2018;141(2):021001-021001-13. doi:10.1115/1.4041924.

As energy efficiency increases in importance, researchers have identified manufacturing processes as opportunities where energy consumption can be reduced. Drawing is one widely employed, energy intensive manufacturing process, which could benefit by analysis of energy consumption during operation. To optimize the energy consumption of the drawing process, this paper developed an explicit model to quantify the process energy for the cylindrical drawing process by analyzing the dynamic punch force during the process. In this analysis, the evolution of the stress and strain was analyzed in the drawn part by considering all the structure parameters of the drawn part. The stress and strain analyses were integrated into an overall process energy model, and the behavior of the model was classified into three categories, based on their physical mechanisms, i.e., deformation energy, bending energy, and friction energy. The model was validated using numerical experiments designed by the Taguchi method where two different kinds of materials were tested over 18 runs. The results from the numerical experiments were compared with those from the model, and show that the maximum variation of the process energy predicted by this model is less than 10% for a given part. Sensitivity analysis was performed on the model to understand the contributions of the process parameters on the process energy to guide process optimization for lower energy consumption. The established model can assist in the rapid design of drawn parts with lower embodied energy.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021002-021002-9. doi:10.1115/1.4041925.

Circular economy has emerged as a response to increasing environmental problems. As opposed to linear economy, circular economy aims at the preservation of energy, material, and labor contents of used products. A critical process in circular economy is product recovery which involves the recovery of materials or components from returned products through various recovery options including recycling, refurbishing, and remanufacturing. All recovery options require some level of disassembly and disassembly operations that are generally carried out in a disassembly line. Like assembly lines, disassembly lines must be balanced in order to ensure the effective operation of the line. Mathematical programming techniques, metaheuristics, and various heuristic procedures were employed in order to solve different types of disassembly line balancing problem (DLBP). However, the use of multi-attribute decision making techniques is limited to few studies. In this study, we propose a DEMATEL-based disassembly line balancing approach which does not require extensive knowledge in operations research and computer programming. A solution can be obtained by carrying out basic matrix operations and following the steps of the approach. Two numerical examples are also provided in order to present the applicability of the proposed approach. The results indicate that the proposed approach presents a satisfactory performance compared to the previously proposed approaches.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021003-021003-11. doi:10.1115/1.4041950.

The deficiency of temporal information in life cycle assessment (LCA) may misrepresent the environmental impacts of products throughout the life cycle or at a particular time in the future. For the environmental assessment of energy-consuming products, background data obtained from the LCA database fail to incorporate emissions or extractions reflecting the future situation. To overcome this knowledge gap, we developed a system dynamics (SD) model to predict the evolution of energy structure in China till 2030 and further determined the time-varying emissions of unit electric power combined with the ecoinvent 3.1 database. Additionally, dynamic characterization factors (CFs) of global warming potential (GWP) were integrated into the life cycle impact assessment (LCIA). This study took the PCL803 large-scale centrifugal compressor as an illustrative example in which the temporal-dependent electricity was included in the dynamic life cycle inventory and the dynamic CFs of GWP were included in the LCIA. Environmental impacts were quantified and compared using the traditional and prospective LCA. Results indicated that the environmental burdens under the electricity variation were approximately 13% less than those of conventional LCA, and the GWP under dynamic CFs would be further reduced by 14.5%. The results confirmed that, when socio-economic progress, technical improvement, and dynamic CFs are not considered, the environmental assessment will lead to an overestimation of environmental loads. Therefore, the relevant time-varying parameters should be considered for accurate assessment.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021004-021004-13. doi:10.1115/1.4041746.
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Remanufacturing has gained attention from industry, but the literature lacks the scientific comprehension to realize efficient remanufacturing. This hinders a company from commencing or improving remanufacturing efficiently. To fill this gap, the paper proposes a set of practical success factors for remanufacturing. To do so, it analyzes remanufacturing practices in industry through interviews with staff from remanufacturing companies with long experience. The practical success factors are found to be (1) addressing product and component value, (2) having a customer-oriented operation, (3) having an efficient core acquisition, (4) obtaining the correct information, and (5) having the right staff competence. Next, the paper further analyzes remanufacturing processes theoretically with both cause and effect analysis and means-ends analysis. Since the factors show that, among other things, the product/service system (PSS) is highly relevant to remanufacturing in multiple ways, theories on the PSS are partly utilized. As a result, the distinctive nature of remanufacturing underlying in the processes is found to have high variability, high uncertainty and, thus, also complexity. The obtained insights from practice and theory are found to support each other. In addition, a fishbone diagram for remanufacturing is proposed based on the analysis, including seven m's, adding two new m's (marketing and maintenance) on top of the traditional five m's (measurement, material, human, method, and machine) in order to improve customer value. The major contribution of the paper lies in its insights, which are grounded in both theory and practice.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021005-021005-12. doi:10.1115/1.4042150.

Remanufacturing has recently received significant interest due to its environmental and economic benefits. Traditionally, the reassembly processes in remanufacturing systems are managed using a product-oriented model. When a product is returned and disassembled, the used components may be processed incorrectly, and the quality of the remanufactured products may not meet customer needs. To solve these problems, a component-oriented reassembly model is proposed. In this model, returned components are inspected and assigned scores according to their quality/function and categorized in a reassembly inventory. Based on the reassembly inventory, components are paired under the control of a reassembly strategy, and these pairs are then assembled into reassembly chains. Each chain represents a product. To evaluate the performance of different reassembly strategies under uncertain conditions, we describe the reassembly problem using an agent-environment system. The platform is modeled as a Markov decision process (MDP), and a reassembly score iteration algorithm (RSIA) is developed to identify the optimal reassembly strategy. The effectiveness of the method is demonstrated via a case study using the reassembly process of diesel engines. The results of the case study show that the component-oriented reassembly model can improve the performance of the reassembly system by 40%. A sensitivity analysis is carried out to evaluate the relationship between the parameters and the performance of the reassembly system. The component-oriented model can reassemble products to meet a larger variety of customer needs, while simultaneously producing better remanufactured products.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021006-021006-8. doi:10.1115/1.4041926.

There has been an increasing trend for manufacturers to shift toward sustainable manufacturing strategies in response to an ever-growing pressure from fluctuating energy price and environmental crisis. Reducing energy consumption is considered as an important step to achieve the sustainability of a production system. This paper proposes an event-based control methodology to improve the production energy efficiency through strategically switching appropriate stations to energy saving mode. Based on an event-based analysis of production dynamics, an analytical approach is developed to quantitatively predict the system level production loss resulted from an energy saving control event (ESCE). A genetic-based control algorithm is proposed to balance the trade-off between the gain from energy saving and the expense of throughput loss. The energy improvement analysis results in a fundamental understanding of production energy dynamics and a significant decrease of energy cost for a manufacturing facility. Numerical case studies are performed to validate the effectiveness of the proposed method. It is found that the control method can effectively reduce energy cost, while only slightly impacting production.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021007-021007-8. doi:10.1115/1.4041747.

Due to an increase in the number of applications for 3D printers, the use of thermoplastic resins such as acrylonitrile butadiene styrene (ABS) and poly lactic acid (PLA), which are typical filament materials for fused filament fabrication (FFF) type 3D printers, has also increased significantly. This trend has produced an interest in recycled filaments, both to reduce the manufacturing cost of fabricated products and to lower greenhouse gas emissions. Also, this recycling system is very useful to make functional filament such as highly conducting or high strength filament by combining carbon nanotube or polydopamine during recycling process. This study presents the design procedures of system for making recycled filaments for 3D printers from waste polymer. The system integrates four main parts for recycling filament: a shredder, which crushes polymer waste into small pieces; an extruder, which extrudes filament from the crushed pieces; a sensing and control component, which regulates the diameter of the extruded filament via a closed-loop control system, and a spooler. Additionally, the dimensional accuracy, the mechanical strength of pristine, and recycled filaments were investigated and compared.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021008-021008-14. doi:10.1115/1.4041481.

Due to the increasing concern on environmental sustainability, many efforts have been made to improve the energy efficiency and reduce carbon emissions of manufacturing processes, including abrasive machining processes. Oilstones, as the abrasive tool of honing machines, are the key parts to remove material. However, the theoretical models and methods that can be used to support the selection of oilstone parameters for reduced carbon emissions are lacking. To fill this gap, this paper proposes a method to optimize shape and distribution of abrasive grains for minimized carbon emissions while maintaining surface quality. First, the carbon emissions boundary is defined, and a carbon emissions calculation model is established from a macroperspective. As each grain contributes to the total carbon emissions, the behavior of grains during honing is then described and analyzed to obtain the carbon emissions model from a microperspective. Surface area of oilstones and the required total volume of material removal are kept constant to meet the physical size limit of oilstones and machining requirement of workpiece. Third, a shape and distribution optimization model is developed to minimize carbon emissions. A modified particle swarm optimization (PSO) algorithm is adopted to solve this problem. Finally, the proposed method is applied to a case study to validate its effectiveness. Results show that carbon emissions can be reduced by up to 30% using the proposed model. The proposed method provides a new green manufacturing strategy for the honing process and a possibility to customize abrasive tools to meet the environmental challenges.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021009-021009-13. doi:10.1115/1.4042107.

The purpose of this research is to present a generic method to estimate product disassembly time at detail stage by utilizing Boothroyd and Dewhurst classification form. Disassembly time is critical in decision-making process of end-of-life (EOL) operations, such as reuse, recycling, and remanufacturing. Theoretical assembly time for a design can be estimated using well-established Boothroyd and Dewhurst's method, given an assembly sequence. This method breaks single component assembly time into acquisition time, manual time, and insertion time. However, in disassembly processes, component symmetry features are, in most cases, unnecessary. Based on this fact, a hypothesis is made that a component's disassembly time can be estimated by considering replacing time, part removal time, and elements of surrounding components, including visibility, accessibility, and any additional effort. Fastening component disassembly time can be estimated by replacement time and time consumed by thread number. An assembly model is designed to verify this hypothesis with a predefined disassembly sequence. Totally, 31 undergraduate students took part in the manual assembly and disassembly experiment. Difference between theoretical and manual assembly times was found to be 7.4% while the difference between theoretical and manual disassembly times was 2.4%. Statistical evaluation indicated that the theoretical disassembly time falls within manual disassembly time with 95% confidence interval. To further validate the methods, two case studies are carried out with distinct products under same experimental environment. The approach proposed in this study can estimate disassembly time of a product at detail design stage when disassembly sequence is provided. Future work will focus on automating this method while incorporating selective and destructive disassembly time estimations.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021010-021010-11. doi:10.1115/1.4041948.

Product improvement, usually through changes in design and functionality, is relying more and more on the continuous analysis of large amounts of data. Product data can come from many sources with varying effort in obtaining the data, e.g., condition monitoring and maintenance data. Intelligent products, also known as “product embedded information devices” (PEID), are already equipped with sensors and onboard computing capabilities and therefore able to generate valuable data such as the number of user interactions during the use phase. The internet of things (IoT) makes data transfer possible at any time to close the loop for the product lifecycle data and methods like machine learning promote new uses of those data. This paper proposes a methodology to capture the most relevant data on product use and human–product interaction automatically and utilize it as part of data-driven product improvement. Product engineers and designers will gain insights into the use phase and can derive design changes and quality improvements. The methodology guides the user through research on product use dimensions based on the principles of user-centered design (UCD). The findings are applied to define what usage elements, such as specific actions and context, need to be available from the use phase. During systems development, machine learning is suggested to fuse sensor data to efficiently capture the usage elements. After product deployment, use data are retrieved and analyzed to identify the improvement potential. This research is a first step on the long way to self-optimizing products.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021011-021011-8. doi:10.1115/1.4042006.

The improvement of the waste management efficiency and sustainability in the electronics sector requires the disassembly and reuse of valuable electronic components, instead of their recycling for precious materials recovery. In this context, this study proposes a robotic system for the disassembly of electronic components, grounded on the revamping of an existing soldering machine. First, the feasibility of an automated process for the end of life (EoL) management of electronic boards is investigated: the disassembly and reuse of electronic components represents a potential cost saving opportunity for producers of industrial electronic boards, other than an effective means to improve the environmental sustainability of the electronics sector. Then, the automatic system has been designed; it is mainly composed by a wave soldering machine, a two-axis manipulator equipped with a suction cup for components picking, and a central control unit to coordinate the motion. Finally, the prototype of the disassembly equipment has been realized. The experimental tests aimed at setting the most relevant process parameters (e.g., working temperatures) and verifying the performance of the developed disassembly equipment. Results confirmed the effectiveness and the reliability of the prototype: all the 450 microprocessors disassembled from 50 boards resulted to be not damaged and thus directly reusable in new boards without the need of additional treatments (e.g., washing).

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021012-021012-10. doi:10.1115/1.4041568.

In metal cutting operations, energy efficiency can have significant consequences for the environment and for sustainable development (such as ever-increasing demand for cost saving and quality improvements), particularly when the processes are practiced on a very large scale. The energy efficiency state is a cutting process condition that coexists with other conditions such as cutter state, workpiece quality state, or machine tool state. It must be monitored by operators to avoid system failure of low energy efficiency state, on-line energy efficiency state monitoring is becoming more and more important in intelligent manufacturing and green manufacturing. The idea of energy efficiency state identification is proposed and the monitoring strategy of energy efficiency state is established for this subject. A combined application method of continuous wavelet transform (CWT) and fast independent component analysis (FICA) is proposed for feature extraction of low or high energy efficiency state. The feature of energy efficiency state is extracted by CWT on the premise of determining the state of high and low energy efficiency based on modeling of energy efficiency state and experiment data. The feature signal is reconstructed by FICA and the reconstruction signal is verified by short time Fourier transform (STFT). The feature tracing of cutting system is carried out. It is illustrated that the feature of energy efficiency state can be extracted and the different energy efficiency states also can be identified for milling processes. The proposed method will be helpful for energy efficiency state monitoring.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021013-021013-17. doi:10.1115/1.4041833.

Cyber-physical systems (CPS) enable unprecedented communication between product designers and manufacturers. Effective use of these technologies both enables and requires a new paradigm of methods and models to identify the most profitable and environmentally friendly production plans for a manufacturing network. The operating system for cyber-physical manufacturing (OSCM) and the paired network operations administration and monitoring (NOAM) software are introduced. These technologies guide our development of a mixed integer bilevel programming (BP) model that models the hierarchy between designers and manufacturers as a Stackelberg game while considering multiple objectives for each of them. Designers select and pay manufacturers, while manufacturers decide how to execute the order with the payment provided by the designer. To solve the model, a tailored solution method combining a decomposition-based approach with approximation of the lower level Pareto-optimal solution set is proposed. The model is applied to a case study based on a network of manufacturers in Wisconsin and Illinois. With the proposed model, designers and manufacturers alike can take full advantage of CPS to increase profits and decrease environmental impacts.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021014-021014-9. doi:10.1115/1.4042125.

Recently, carbon emissions and global warming have become major issues, and efforts are being made to develop sustainable manufacturing systems and improve product lifespans. Waste and greenhouse gases created during manufacturing can be minimized using sustainable processes and by proactively considering the environment during product design and fabrication. Miniaturization of optical parts is key in the maturing mobile device market; the demand for ultra-small light-emitting diodes (LEDs) and aspherical lenses is growing rapidly. Small aspherical lenses are created using injection molding, wafer-level optics, and glass molding. Traditionally, injection molding was associated with excellent transferability, and is suitable for mass production. However, considerable energy is required to create high internal cavity pressures and high temperatures. Furthermore, a great deal of waste such as runners is created, and the lenses are unstable at high temperature. We sought to resolve these issues by using sustainable manufacturing concepts in the design stage. To this end, we used ultraviolet (UV)-curable resin to mold high-precision lenses exhibiting excellent heat-resistance. We proposed a methodology to mold ultra-small optical lenses using UV-curable resin to improve material and energy efficiency compared with the traditional injection molding process. We employed a prognostics to predict the life cycle of the system and improve sustainability.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021015-021015-14. doi:10.1115/1.4042076.

Emerging re-industrialization demands the fusion of the physical and the digital world for the development of sustainable manufacturing processes. Sustainability in manufacturing aims at improving the resource productivity by identifying the environmental challenges as opportunities. In the present era of the fourth industrial revolution or digital manufacturing, manufacturers strive to gain value through every bit of data collection throughout the product lifecycle. Integration of the collected information as knowledge to improve the productivity and efficiency of the system is required to realize its benefits. In the present work, a digital twin for grinding wheel as a product integrated and web-based knowledge sharing platform is developed. It integrates the data collected in each phase of the grinding wheel from the manufacturing to the conditioning phase. The developed digital twin is implemented on the surface grinding machine. The methods for the abstraction of the production information from the manufacturer and the process information while grinding are presented. The development of a predictive model for redress life identification and computation of dressing interim period using spindle motor current data is developed and integrated. The quantifiable benefits from the digital twin for productivity and efficiency are discussed through a case study. The case study scenario evident that the implementation of the digital twin for grinding wheels increases energy and resource efficiency by 14.4%. This clearly depicts the usefulness of the digital twin for energy and resource efficiency toward the sustainable grinding process.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021016-021016-12. doi:10.1115/1.4041835.

Design for environment (DfE) principles are helpful for integrating manufacturing-specific environmental sustainability considerations into product and process design. However, such principles are often overly general, static, and disconnected from production contexts. This paper proposes a visual analytics (VA)-based framework for generating DfE principles that are contextualized to specific production setups. These principles are generated through interactive visual exploration of design and process parameters as well as manufacturing process performance metrics corresponding to the production setup. We also develop a formal schema for aiding storage, updating, and reuse of the generated DfE principles. In this schema, each DfE principle is associated with corresponding product lifecycle data and the evidence that led to the generation of that principle. We demonstrate the proposed VA framework using data from an industry-led experiment that compared dry ice (DI)-based and oil-based milling for a specific production setup.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021017-021017-20. doi:10.1115/1.4041947.

The production stage of a product's life cycle can significantly contribute to its overall environmental impact. Estimates of environmental impact for a product are typically produced using life cycle assessment (LCA) methods. These methods rely on life cycle inventory (LCI) data containing impact estimates of manufacturing processes and other operations that contribute to a product's creation. The accuracy of LCI data is critical for quality assessments; however, these data are often insufficient in the types and varieties of manufacturing processes covered and are often only a coarse estimate of actual impacts. At the same time, much manufacturing research focuses on how to model, measure, assess, and reduce the environmental impacts of manufacturing processes. Recent standards emerging from ASTM International define a structured format for presenting these studies in a reusable way. In this paper, we investigate the potential for using the ASTM E3012-16 format to generate LCI datasets suitable to perform LCA by mapping from the ASTM standard into the widely adopted ecoSpold2 format. A process is presented for generating LCI datasets from ASTM models, and overlaps and gaps between the two standards are identified.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021018-021018-13. doi:10.1115/1.4042077.

Promoting excellence in sustainable manufacturing has emerged as a strategic mission in academia and industry. In particular, universities must prepare the next generation of engineers to contribute to the task of sustaining and improving manufacturing by providing appropriate types of sustainability education and training. However, engineering curricula are challenged in delivering educational training for assessing technical solutions from the three domains that define sustainability: economic, environmental, and social. In the research presented here, an educational framework is developed with an aim to improve student understanding of sustainable product design (PD) and manufacturing. The framework is founded on the analyze, design, develop, implement, and evaluate (ADDIE) model for instructional design. The developed framework is demonstrated using an example of a sustainable PD activity. This instructional design case study illustrates how engineering students would be able to investigate the impacts of raw materials, unit manufacturing processes, manufacturing locations, and design changes on product sustainability performance by integrating PD information and manufacturing analysis methods during the PD phase.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2018;141(2):021019-021019-9. doi:10.1115/1.4042078.

Powder-based additive manufacturing technologies are developing rapidly. To assess their applicability, comparison of performance and environmental impacts between additive technologies and conventional techniques must be performed. Toyota manufactures over two million aluminum four-cylinder engines in the U.S. each year via die casting. The dies used in this process are traditionally repaired via tungsten inert gas (TIG) welding and only last an average of 20.8% of the number of cycles of the original die life before another repair is needed. A hybrid repair process involving machining away the damaged areas and then rebuilding them additively via powder-blown directed energy deposition (DED) has been developed. The die repaired via DED resulted in the same life as the original die. The use of DED repair eliminated the need for emergency repairs and nonscheduled downtime on the line because the DED repaired dies last for as many cycles as the original die before another repair is needed. Life cycle analyses were conducted comparing the traditional welding repair process to the DED repair process. The results show that the DED repair process results in significantly less damage to the assessed impact categories except for ionizing radiation. Therefore, it can be concluded that the DED repair process could lessen most environmental impacts compared to traditional welding repair. Further work toward increasing energy and material efficiencies of the method could yield further reductions in environmental impacts.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2019;141(2):021020-021020-7. doi:10.1115/1.4042306.

Energy consumption of computer numerical control (CNC) machines is significant and various empirical models have been developed to model the specific energy consumption (SEC) of CNC machines. However, most of the models are developed for specific machines and hence have limited applications in manufacturing industry. In this research, a general empirical SEC model for milling machine at certain power level is developed based on actual cutting experimental data. In this model, stand-by power and spindle power are used in the SEC model for the first time. The material removal rate (MRR) is used to represent the cutting parameter. The proposed model is fitted by the regression analysis and validated using experimental data. Results show that the proposed model can be applied on various milling machines with an average absolute residual ratio of 6%. The model is also validated through a series of cutting experiments on a machine center, with an accuracy of 91.5%, for the SEC calculation.

Commentary by Dr. Valentin Fuster
J. Manuf. Sci. Eng. 2019;141(2):021021-021021-8. doi:10.1115/1.4042307.

A hybrid modeling approach based on computational fluid dynamics (CFD) and finite element method (FEM) is presented to simulate and study cryogenic machining (CM) of Ti–6Al–4V alloy. CFD analysis was carried out to study the characteristics of the fluid flow and heat transfer process of liquid nitrogen (LN2) jet used as a coolant in turning operation. The velocity, turbulence, gas volume fraction, and temperature of the impingement jet were investigated. Based on the analysis results, the coefficient of heat transfer (CHT) between the LN2 and cutting tool/insert was obtained and used in the FEM analysis to model the heat transfer process between the LN2 and the tool/chip/workpiece. A three-dimensional (3D) finite element (FE) model was developed to simulate a real CM operation. CM tests were carried out to validate the 3D FE model by comparing cutting forces and chip temperature. To evaluate LN2 cooling effect on tool temperature and tool wear, a two-dimensional (2D) FE model was developed for steady-state thermal analysis of cryogenic and dry machining. Based on the predicted temperatures, the tool wear was estimated, showing that LN2 cooling can significantly improve tool life.

Commentary by Dr. Valentin Fuster

Expert View

J. Manuf. Sci. Eng. 2018;141(2):024701-024701-4. doi:10.1115/1.4041424.

Sustainment and sustainability are concepts that have pervaded recent engineering culture. Although the popular media often associates sustainability with environmental and socio-ecological constructs, it is a widely used and understood concept with application to many technology, system, and business areas that extend beyond a socio-ecological context. This paper discusses the usage of the term sustainment, proposes a general definition of sustainment that conflates ecological, social, political, economic and technological interests, and provides recommendations that broaden the perspective.

Commentary by Dr. Valentin Fuster

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