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research-article  
Brian Davis, David Dabrow, Ryan Newell, Andrew Miller, John Schueller, Guoxian Xiao, Steven Y. Liang, Karl T. Hartwig, Nancy Jean Ruzycki, Yongho Sohn and Yong Huang
J. Manuf. Sci. Eng   doi: 10.1115/1.4038442
Severe plastic deformation (SPD) processing such as equal channel angular extrusion (ECAE) has been pioneered to produce ultra-fine grained (UFG) metals for improved mechanical and physical properties. However, understanding the machining of SPD-processed metals is still limited. This study aims to investigate the differences in chip morphology when machining ECAE-processed UFG and coarse-grained (CG) titanium (Ti) and understand the chip formation mechanism using metallographic analysis, digital imaging correlation, and nano-indentation. The chip morphology is classified as aperiodic saw-tooth, continuous, or periodic saw-tooth, and changes with the cutting speed. The chip formation mechanism of the ECAE-processed Ti transitions from cyclic shear localization within the low cutting speed regime (such as 0.1 m/s or higher) to uniform shear localization within the moderately high cutting speed regime (such as from 0.5 to 1.0 m/s) and to cyclic shear localization (1.0 m/s). The shear band spacing increases with the cutting speed and is always lower than that of the CG counterpart. If the shear strain rate distribution contains a shift in the chip flow direction, the chip morphology appears saw-tooth, and cyclic shear localization is the chip formation mechanism. If no such shift occurs, the chip formation is considered continuous, and uniform shear localization is the chip formation mechanism. Hardness measurements show that cyclic shear localization is the chip formation mechanism when localized hardness peaks occur, whereas uniform shear localization is operative when the hardness is relatively constant.
TOPICS: Machining, Titanium, Shear (Mechanics), Cutting, Metals, Nanoindentation, Imaging, Extruding, Flow (Dynamics), Deformation
research-article  
Marlon Hahn, Nooman Ben Khalifa and Arash Shabaninejad
J. Manuf. Sci. Eng   doi: 10.1115/1.4038369
The stamping of fibre metal laminates (FMLs) at thermoforming temperature of the thermoplastic matrix was investigated. The studied FML types consisted of a unidirectional carbon fibre reinforced core that was attached to metal cover layers either made of a steel or magnesium alloy. An analytical model was established in order to predict the process forces during forming, which are: the blankholder force required to make the metal covers yield plastically, the punch force, and the corresponding load distribution on the individual layers (outer layer, core layer, and inner layer). The global forces were primarily verified through experimental force measurements, while numerical simulations were mainly performed to assess the resulting load distribution by the help of strain distributions in the cover layers. The results showed that the introduced model can be applied successfully if the stamp-forming process is dominated by friction-induced tensional loading rather than by bending.
TOPICS: Metals, Fibers, Laminates, Metal stamping, Stress, Force measurement, Carbon fibers, Magnesium alloys, Computer simulation, Steel, Friction, Temperature
research-article  
Xing Zhang, Wei Zhang, Jun Zhang, Bo Pang and Wanhua Zhao
J. Manuf. Sci. Eng   doi: 10.1115/1.4038371
A general calibration method of cutter runout and specific cutting force coefficients (SCFCs) for flat-end cutter is proposed in this paper, and a high accuracy of cutting force prediction during peripheral milling is established. In the paper, the cutter runout, the bottom edge cutting effect and the actual federate with limitation during large tool path curvature are concerned comprehensively. Firstly, based on the trochoid motion, a tooth trajectory model is built up and an analytical instantaneous uncut chip thickness (IUCT) model is put forward for describing the cutter/workpiece engagement (CWE). Secondly, a non-contact identification method for cutter runout including offset and inclination is given, which constructs an objective function by using the cutting radius relative variation between adjacent teeth, and identifies through a numerical optimization method. Thirdly, with consideration of bottom edge cutting effect, the paper details a three-step calibration procedure for SCFCs based on an enhanced thin plate milling experiment. Lastly, a series of milling tests are performed to verify the effectiveness of the proposed method. The results show that the approach is suitable for both constant and non-constant pitch cutter, and the generalization has been proved. Moreover, the paper points out that the cutter runout has a strong spindle speed dependent effect, the milling force in cutter axis direction exists a switch-direction phenomenon, and the actual federate will be limited by large tool path curvature. All of them should be considered for obtaining an accurate milling force prediction.
TOPICS: Calibration, Cutting, Milling, Switches, Trajectories (Physics), Optimization
Technical Brief  
Li-Wei Chen, Yung-Hung Chen and Yuan-Chuan Hsu
J. Manuf. Sci. Eng   doi: 10.1115/1.4038370
A simulation and experimental method are used to determine the parameters of a tailored tempering process for a lab-scale B-pillar that is made from CSC-15B22 high-strength steel. The finite element software, DEFORM-3D, is used to simulate the tailored tempering process. A segmented hot stamping tool is developed for testing. Results demonstrate that the cooling and heating system are successful. On the cooled side of the tooling, the cooling rate for the sheet is more than 30?/s. The material structure of the sheets is entirely a Martensite structure, which results in an ultra-high strength material. The average hardness is measured as HV423, which translates to a tensile strength of 1350MPa. On the heated side of the tooling, the cooling rate for the sheet is less than the critical cooling rate, the microstructure of the material is ferrite and pearlite and the average hardness is measured at HV205, which translates to a tensile strength of approximately 660MPa. The study proves that a tailored tempering process allows production using integrated tooling and produces sheets that have different mechanical properties.
TOPICS: Cooling, Columns (Structural), Steel sheet, Simulation, High strength steel, Strength (Materials), Ferrites (Magnetic materials), Mechanical properties, Finite element analysis, Heating and cooling, Testing, Computer software, Metal stamping, Tooling, Tensile strength
research-article  
Sushmit Chowdhury, Kunal Mhapsekar and Sam Anand
J. Manuf. Sci. Eng   doi: 10.1115/1.4038293
Significant advancements in the field of additive manufacturing have increased the popularity of AM in mainstream industries. The dimensional accuracy and surface finish of parts manufactured using AM depend on the AM process and the accompanying process parameters. Part build orientation is one of the most critical process parameters, since it has a direct impact on the part quality measurement metrics such as cusp error, manufacturability concerns for geometric features such as thin regions and small openings, and support structure parameters. In conjunction with the build orientation, the cyclic heating and cooling of the material involved in the AM processes, leads to nonuniform deformations throughout the part. These factors cumulatively affect the design conformity, surface finish, and the postprocessing requirements of the manufactured parts. In this paper, a two-step part build orientation optimization and thermal compensation methodology is presented to minimize the geometric inaccuracies resulting in the part during the AM process. In the first step, a weighted optimization model is used to determine the optimal build orientation for a part with respect to the aforementioned part quality and manufacturability metrics. In the second step, a novel Artificial Neural Network based geometric compensation methodology is used on the part in its optimal orientation to make appropriate geometric modifications to counteract the thermal effects resulting from the AM process. The effectiveness of this compensation is assessed on an example part using a new point cloud to part conformity metric and shows significant improvements in the manufactured part’s geometric accuracy.
TOPICS: Optimization, Geometry, Additive manufacturing, Finishes, Temperature effects, Design, Heating and cooling, Artificial neural networks, Errors, Deformation
research-article  
Andrea Sanchez Valencia and Julien Loste
J. Manuf. Sci. Eng   doi: 10.1115/1.4038294
Recent changes in legislation along with environmental initiatives to drive sustainability and reduce carbon emissions have sprouted the development of energy models to characterise manufacturing processes. In the case of injection moulding, much work has been performed in coupling sensors with control statistical systems to promptly identify process’ instabilities, such as pressure drops or fluctuations in the filling point. Latest energy models for injection moulding make use of injection pressure and temperature parameters that are a function of the machine, mould geometry and process characteristics. The latest state-of-the-art way to measure energy consumption is through the use of energy loggers, which provide power data at the end of the production cycles. Although seemingly correlated, little has been published on the extrapolation of cavity signals for their use in energy calculations. In this study, the advantages and disadvantages of using cavity sensors in injection moulding are explored; a novel approach to the use of cavity sensors’ pressure and temperature data is proposed by exploring their input in an energy model for the estimation of specific energy consumption. The model was validated against power data obtained via an energy logger; the averaged energy reported by the model indicated a range of 60-67% accuracy.
TOPICS: Injection molding, Energy consumption, Cavities, Sensors, Pressure, Temperature, Machinery, Manufacturing, Fluctuations (Physics), Carbon, Sustainability, Cycles, Geometry, Pressure drop, Signals, Emissions
research-article  
Bijan Nili, Ghatu Subhash and James S. Tulenko
J. Manuf. Sci. Eng   doi: 10.1115/1.4038295
A coordinated experimental and computational analysis was undertaken to investigate the temperature field, heat generation, and stress distribution within a spark plasma sintering (SPS) tooling-specimen system during single- and multi-pellet fabrication of uranium dioxide (UO2) fuel pellets. Different SPS tool assembly configurations consisting of spacers, punches, pellets, and a die with single or multiple cavities were analyzed using ANSYS finite element (FE) software with coupled electro-thermo-mechanical modeling approach. For single-pellet manufacture, the importance of the die dimensions in relation to punch length and their influence on temperature distribution in the pellet were analyzed. The analysis was then extended to propose methods for reducing the overall power consumption of the SPS fabrication process by optimizing the dimensions and configurations of tooling for simultaneous sintering of multiple pellets in each processing cycle. For double-pellet manufacture, the effect of the center punch length (that separates the two pellets) on the temperature distribution in the pellets was investigated. Finally, for the multiple pellet fabrication, the optimum spacing between the pellets as well as the distance between the die-cavities and the outer surface of the die wall were determined. A good agreement between the experimental data on the die surface temperature and FE model results was obtained. The current analysis may be utilized for further optimization of advanced tooling concepts to control temperature distribution and obtain uniform microstructure in fuel pellets in large-scale manufacturing using SPS process.
TOPICS: Manufacturing, Sintering, Simulation, Plasmas (Ionized gases), Tooling, Temperature distribution, Temperature, Fuels, Dimensions, Cavities, Computer software, Cycles, Energy consumption, Finite element model, Uranium, Stress concentration, Finite element analysis, Modeling, Optimization, Heat
research-article  
Daniel Mejia, Aitor Moreno, Ander Arbelaiz, Jorge Posada, Oscar E. Ruiz-Salguero and Raul Chopitea
J. Manuf. Sci. Eng   doi: 10.1115/1.4038207
In the context of CNC-based (Computer Numeric Control) sheet metal laser cutting, the problem of heat transfer simulation is relevant for the optimization of CNC programs. Current physically-based simulation tools use numeric or analytic algorithms which provide accurate but slow solutions due to the underlying mathematical description of the model. This manuscript presents: (1) an analytic solution to the laser heating problem of rectangular plates for curved laser trajectories and convective cooling, (2) a GPU implementation of the analytic solution for fast simulation of the problem, and (3) an integration within an interactive environment for the simulation of sheet metal CNC laser cutting. This analytic approach sacrifices the material removal effect of the laser cut in favor of an approximated real-time temperature map on the sheet metal. The articulation of thermal, geometric and graphic feedback in virtual manufacturing environments enables interactive redefinition of the CNC programs for better product quality, lower safety risks, material waste and energy usage among others. The error with respect to FEA in temperature prediction descends as low as 3.5 %.
TOPICS: Sheet metal, Simulation, Laser cutting, Optimization, Computer numerical control machine tools, Lasers, Temperature, Heat transfer, Cooling, Safety, Product quality, Algorithms, Finite element analysis, Plates (structures), Computers, Energy consumption, Errors, Feedback, Heating, Virtual assembly, Graphics processing units
research-article  
Kaveh Bastani, Babak Barazandeh and Zhenyu (James) Kong
J. Manuf. Sci. Eng   doi: 10.1115/1.4038184
The problem of fault diagnosis for dimensional integrity in multistation assembly systems is addressed in this paper. Fault diagnosis under this context is to identify the process errors which significantly contribute to the large product dimensional variation based on sensor data. The main challenges to be resolved in this paper include (1) the number of measurements is less than the process errors, which is typical in practice, but results in an ill-posed estimation problem, and (2) there exists spatial correlation among the dimensional variation of process errors, which has not been addressed yet by existing literature. A spatially correlated Bayesian learning algorithm (SCBL) to address these challenges is developed. The SCBL algorithm is based on the relevance vector machine (RVM) by exploiting the spatial correlation of dimensional variation from various process errors, which occurs in some circumstances of assembled parts and is well defined in GD&T standards. The proposed algorithm relies on a parametrized prior including the spatial correlation, and eventually leads sparsity in fault diagnosis, hence the issues with ill-posedness and structured process errors will be addressed. A number of simulation studies are performed to illustrate the superiority of SCBL algorithm over state-of-the-art algorithms in sparse estimation problems when spatial correlation exists among the nonzero elements. A real auto body assembly process is also used to demonstrate the effectiveness of proposed SCBL algorithm.
TOPICS: Manufacturing, Algorithms, Fault diagnosis, Errors, Simulation, Machinery, Sensors
research-article  
Sinan Kesriklioglu, Justin Morrow and Frank E. Pfefferkorn
J. Manuf. Sci. Eng   doi: 10.1115/1.4038140
The objective of this work is to fabricate instrumented cutting tools with embedded thermocouples to accurately measure the tool-chip interface temperature in interrupted and continuous turning. Thin-film thermocouples were sputtered directly onto the flat rake face of a commercially available tungsten carbide cutting insert using micro machined stencils and coated the measurement junction with a protective layer to obtain temperature data 1.3 µm below the tool-chip interface. Oblique interrupted cutting tests on AISI 12L14 steel were performed to observe the influence of varying cutting speeds and cooling intervals on tool chip interface temperature. An additional cutting experiment was conducted to monitor the interface temperature change between interrupted and continuous cuts.
TOPICS: Temperature measurement, Cutting, Temperature, Thermocouples, Tungsten, Junctions, Micromachining, Cooling, Steel, Cutting tools, Thin films
research-article  
Dragan Djurdjanovic, Laine Mears, Farbod Akhavan Niaki, Asad Ul Haq and Lin Li
J. Manuf. Sci. Eng   doi: 10.1115/1.4038074
Dramatic advancements and adoption of computing capabilities, communication technologies, and advanced, pervasive sensing have impacted every aspect of modern manufacturing. Furthermore, the very character of manufacturing is changing fast, with new, complex processes and new products appearing in both the industries and academe. As for traditional manufacturing processes, they are also undergoing transformations in the sense that they face everincreasing requirements in terms of quality, reliability and productivity. Finally, across all manufacturing we see the need to understand and control interactions between various stages of any given process, as well as interactions between multiple products produced in a manufacturing system. All these factors have motivated tremendous advancements in methodologies and applications of control theory in all aspects of manufacturing: at process and equipment level, manufacturing systems level and operations level. Motivated by these factors, the purpose of this paper is to give a high-level overview of latest progress in process and operations control in modern manufacturing. Such a review of relevant work at various scales of manufacturing is aimed not only to offer interested readers information about state-of-the art in control methods and applications in manufacturing, but also to give researchers and practitioners a vision about where the direction of future research may be, especially in light of opportunities that lay as one concurrently looks at the process, system and operation levels of manufacturing.
TOPICS: Manufacturing, Manufacturing systems, Control theory, Reliability
research-article  
Dazhong Wu, Connor Jennings, Janis Terpenny, Soundar Kumara and Robert Gao
J. Manuf. Sci. Eng   doi: 10.1115/1.4038002
The emergence of cloud computing, Industrial Internet of Things (IIoT), and new machine learning techniques over the past few years have shown the potential to advance manufacturing towards a higher degree of digitization, remote accessibility, and intelligence. While model-based prognostics and health management (PHM) techniques provide insight into the progression of faults in mechanical components, certain assumptions on the underlying physical mechanisms for fault development are required to develop the models. In situations where there is a lack of adequate prior knowledge of the underlying physics, data-driven methods have been increasingly investigated as a complementary approach to machinery prognostics and intelligent maintenance scheduling. However, data-driven methods typically require large volumes of training data to generate accurate predictive analytics. Consequently, computational efficiency remains a challenge, especially when large volumes of sensor-generated data need to be processed for real-time applications. This research investigates a random forest (RFs)-based algorithm for fault propagation prediction in manufacturing machines based on cloud computing and machine learning. Because the regression trees in RFs are de-correlated, the RF algorithm is parallelized using the MapReduce data processing scheme and implemented on a scalable cloud computing system with varying combinations of processors and memories. By parallelizing RFs with MapReduce on the cloud, a significant increase in the processing speed (14.7 times in terms of increase in training time) has been achieved, with a high prediction accuracy of tool wear (8 times in terms of reduction in mean squared error).
TOPICS: Wear, Machinery, Cloud computing, Manufacturing, Algorithms, Errors, Internet, Sensors, Maintenance, Physics
research-article  
Bing Yao, Farhad Imani, Aniket S. Sakpal, Edward (Ted) Reutzel and Hui Yang
J. Manuf. Sci. Eng   doi: 10.1115/1.4037891
Metal-based Powder-Bed-Fusion additive manufacturing (PBF-AM) is gaining increasing attention in modern industries, and is a promising direct manufacturing technology. Additive manufacturing (AM) does not require the tooling cost of conventional subtractive manufacturing processes, and is flexible to produce parts with complex geometries. Quality and repeatability of AM parts remain a challenging issue that persistently hampers wide applications of AM technology. Rapid advancements in sensing technology, especially imaging sensing systems, provide an opportunity to overcome such challenges. However, little has been done to fully utilize the image profiles acquired in the AM process and study the fractal patterns for the purpose of process monitoring, quality assessment and control. This paper presents a new multifractal methodology for the characterization and detection of defects in PBF-AM built components. Both simulation and real-world case studies show that the proposed approach effectively detects and characterizes various defect patterns in AM images and has strong potential for quality control of AM processes.
TOPICS: Additive manufacturing, Metals, Machining, Quality control, Simulation, Manufacturing technology, Fractals, Process monitoring, Tooling, Imaging
Review Article  
Stefania Altavilla, Francesca Montagna and Marco Cantamessa
J. Manuf. Sci. Eng   doi: 10.1115/1.4037763
Product Cost Estimation still draws the attention of researchers and practitioners, even though it has been extensively discussed in the literature for more than twenty years. This is due to its central impact in affecting company performance. Nowadays, the adoption of cost estimation methods seems to be limited despite the multitude of examples and applications available. A possible reason is related to the multitude of approaches and techniques that instead of representing a guide for spreading possible implementations, actually create confusion and ambiguity on its appropriateness for a particular application. Hence, this paper aims to provide a systematic review of the recent literature in the field of Product Cost Estimation and investigates, in detail, which are the aspects that can enable a more conscious decision on the type of technique that can be adopted. This resulted in the identification of five different perspectives, which can be taken simultaneously into account and by combining the different viewpoints, a new multilayer framework is derived, with a specific focus on the whole product life cycle. The proposed framework can be used as a decision-making tool for both researchers and practitioners. In fact, the former can benefit from the new structure, as a way to identify new areas of possible research opportunities. The latter is provided an operative guide for the application in industrial contexts.
TOPICS: Cycles, Decision making, Ambiguity
research-article  
Qiong Liu, Youquan Tian, Chao Wang, Freddy O. Chekem and John Sutherland
J. Manuf. Sci. Eng   doi: 10.1115/1.4037710
In order to help manufacturing companies quantify and reduce product carbon footprints in a mixed-model manufacturing system, a product carbon footprint oriented multi-objective flexible job-shop scheduling optimization model is proposed. The production portion of the product carbon footprint, based on the mapping relations between products and the carbon emissions within the manufacturing system is proposed to calculate the product carbon footprint in the mixed-model manufacturing system. Non-Dominated Sorting Genetic Algorithm-II(NSGA-II) is adopted to solve the proposed model. In order to help decision makers to choose the most suitable solution from the Pareto set as its execution solution, a method based on grades of product carbon footprints is proposed. Finally, the efficacy of the proposed model and algorithm are examined via a case study.
TOPICS: Manufacturing, Machine shops, Carbon, Manufacturing systems, Algorithms, Optimization, Emissions
research-article  
Roby Lynn, Mahmoud Dinar, Nuodi Huang, James Collins, Jing Yu, Clayton Greer, Thomas M. Tucker and Thomas Kurfess
J. Manuf. Sci. Eng   doi: 10.1115/1.4037631
Direct digital manufacturing (DDM) is the creation of a physical part directly from a computer-aided design (CAD) model with minimal process planning and is typically applied to additive manufacturing (AM) processes to fabricate complex geometry. AM is preferred for DDM because of its minimal user input requirements; as a result, users can focus on exploiting other advantages of AM, such as the creation of intricate mechanisms that require no assembly after fabrication. Such assembly-free mechanisms can be created using DDM during a single build process. In contrast, subtractive manufacturing (SM) enables the creation of higher strength parts that do not suffer from the material anisotropy inherent in AM. However, process planning for SM is more difficult than it is for AM due to geometric constraints imposed by the machining process; thus, the application of SM to the fabrication of assembly-free mechanisms is challenging. This research describes a voxel-based computer-aided manufacturing (CAM) system that enables direct digital subtractive manufacturing (DDSM) of an assembly-free mechanism. Process planning for SM involves voxel-by-voxel removal of material in the same way that an AM process consists of layer-by-layer addition of material. The voxelized CAM system minimizes user input by automatically generating toolpaths based on an analysis of accessible material to remove for a certain clearance in the mechanism's assembled state. The DDSM process is validated and compared to AM using case studies of the manufacture of two assembly-free ball-in-socket mechanisms.
TOPICS: Machining, Manufacturing, Production planning, Computer-aided design, Computer-aided manufacturing, Geometry, Additive manufacturing, Anisotropy, Clearances (Engineering)
research-article  
Sebastian Barth, Michael Rom, Christian Wrobel and Fritz Klocke
J. Manuf. Sci. Eng   doi: 10.1115/1.4037598
This paper presents an innovative approach for modeling the grinding wheel structure and the resultant grinding wheel topography. The overall objective of the underlying research work was to create a mathematical-generic grinding tool model in which the spatial arrangement of the components grains, bond and pores is simulated in a realistic manner starting from the recipe-dependent volumetric composition of a grinding wheel. This model enables the user to determine the resulting grinding wheel structure and the grinding wheel topography of vitrified and synthetic resin-bonded CBN grinding wheels depending on their specification and thus to predict their application behavior. The originality of the present research results is a generic approach for the modeling of grinding tools, which takes into account the entire grinding wheel structure to build up the topography. Therefore, original mathematical methods are used. The components of grinding wheels are analyzed and distribution functions of the component's positions in the tools are determined. Thus, the statistical character of the grinding wheel structure is taken into account in the developed model. In future, the presented model opens new perspectives in order to optimize and to increase the productivity of grinding processes.
TOPICS: Grinding wheels, Modeling, Grinding, Resins
research-article  
Tomasz Bartkowiak and Christopher A. Brown
J. Manuf. Sci. Eng   doi: 10.1115/1.4037601
The objectives of this work are to demonstrate the use of multi-scale curvature tensor analysis for characterizing surfaces of stainless steel created by micro-electric discharge machining (µEDM), and to study the strengths of the correlations between discharge energies and resulting surface curvatures (i.e., principal, Gaussian or mean curvatures) and how they change with scale. Surfaces were created by µ-EDM techniques using energies from 18nJ to 16 500nJ and measured by confocal microscope. The curvature tensor T is calculated using three proximate unit vectors normal to the surface. The multi-scale effect is achieved by changing the size of the sampling interval for the estimation of the normals. Normals are estimated from regular meshes by applying a covariance matrix method. Strong correlations (R2>0.9) are observed between calculated principal maximal and minimal as well as mean and Gaussian curvatures and discharge energies. This method allows detailed analysis of the nature of surface topographies, and suggests that different formation processes governed the creation of surfaces created by higher energies.
TOPICS: Texture (Materials), Tensors, Electrical discharge machining, Stainless steel, Microscopes
research-article  
Fei Tao, Luning Bi, Ying Zuo and A Y C Nee
J. Manuf. Sci. Eng   doi: 10.1115/1.4037608
Disassembly is a very important step in recycling and maintenance, particularly for energy saving. However, disassembly sequence planning is a challenging combinatorial optimization problem due to complex constraints of many products. This paper considers partial and parallel disassembly sequence planning for solving the degrees of freedom in modular product design, considering disassembly time, cost and energy consumption. An automatic self-decomposed disassembly precedence matrix is designed to generate partial/parallel disassembly sequence for reducing complexity and improving efficiency. A Tabu search based hyper heuristic algorithm with exponentially decreasing diversity management strategy is proposed. Compared with the low-level heuristics, the proposed algorithm is more efficient in terms of exploration ability and improving energy benefits. The comparison results of three different disassembly strategies prove that the partial/parallel disassembly has a great advantage in reducing disassembly time, and improving energy benefits and disassembly profit.
TOPICS: Recycling, Maintenance, Degrees of freedom, Algorithms, Optimization, Energy consumption, Product design
research-article  
Brian K. Paul, Kijoon Lee and Hailei Wang
J. Manuf. Sci. Eng   doi: 10.1115/1.4037606
The objective of this study was to develop a strategy for miniaturizing heat exchangers used for the thermal management of sorbent beds within adsorption refrigeration systems. The thermal mass of the microchannel heat exchanger designed and fabricated in this study is compared with that of commercially available tube-and-fin heat exchangers. Efforts are made to quantify the overall effects of miniaturization on system coefficient of performance and specific cooling power. A thermal model for predicting the cycle time for desorption is developed and experiments are used to quantify the effect of the intensified heat exchanger on overall system performance.
TOPICS: Sorbents, Energy efficiency, Thermal management, Heat exchangers, Refrigeration, Cycles, Desorption, Microchannels, Cooling

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