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Dr. Lin Li
Univesity of Illinois at Chicago, Chicago, IL, USA

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Research Keywords & Expertise

0 Energy-efficient manufacturing
0 Electricity demand response of manufcaturing systems
0 Environmental sustainability of additive manufcaturing
0 Economic viability of renewable energy manufacturing
0 Electric vehicle battery remanufacturing

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Electricity demand response of manufcaturing systems
Environmental sustainability of additive manufcaturing
Economic viability of renewable energy manufacturing

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Journal article
Published: 25 August 2021 in Journal of Manufacturing Processes
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Ultrasonic metal welding is a high-frequency vibration process that creates joints for components and has attracted extensive research attentions in recent years. Although this technology has been applied in many areas such as electronics, automotive, and aerospace, its wide adoption is still hindered by unsatisfactory welding quality. In such cases, performing process monitoring to enhance the welding quality is of great significance. In current literature, the exploration in monitoring and inspection of the ultrasonic welding process is still limited due to the ineffective offline monitoring and the lack of investigation of the anvil state impacts on welding results. To address this problem, an acceleration sensor monitoring system is proposed in this study aiming for enhancing the welding quality. In the established system, the acceleration signals are collected and decomposed by variational modal decomposition (VMD) to capture the characteristics of the intrinsic mode functions (IMFs) energy distributions. In addition, the particle swarm optimization (PSO) algorithm is adopted for penalty term selection while the system frequency and sampling rate are used for mode number determination. The proposed method is experimentally validated and suggests high effectiveness and robustness for anvil state identification in ultrasonic metal welding.

ACS Style

Xinhua Shi; Suiran Yu; Lin Li; Jing Zhao. Anvil state identification based on acceleration signals in ultrasonic metal welding of lithium batteries. Journal of Manufacturing Processes 2021, 70, 67 -77.

AMA Style

Xinhua Shi, Suiran Yu, Lin Li, Jing Zhao. Anvil state identification based on acceleration signals in ultrasonic metal welding of lithium batteries. Journal of Manufacturing Processes. 2021; 70 ():67-77.

Chicago/Turabian Style

Xinhua Shi; Suiran Yu; Lin Li; Jing Zhao. 2021. "Anvil state identification based on acceleration signals in ultrasonic metal welding of lithium batteries." Journal of Manufacturing Processes 70, no. : 67-77.

Journal article
Published: 28 April 2021 in Journal of Cleaner Production
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The emergence of environmental and sustainability regulations as well as the limited availability of fossil fuels have brought the notion of gradually substituting petroleum products with biofuels into the limelight. The commercialization and large-scale adoption of cellulosic biofuel (second-generation biofuel) and microalgae biofuel (third-generation biofuel) have been hampered by their poor economic performance. Although the production wastes of biofuel can be utilized as input materials to enhance resource utilization efficiency, environmental sustainability and economic viability in each generation, rarely existing research has been conducted on the integration between these two generations. To fill this research gap, a novel industrial symbiosis (IS) design for the co-production of second- and third-generation biofuels is proposed in this paper. The material flow, energy flow, cost performance, and environmental impact models are established considering the interactions of stakeholders in the IS system. Four scenarios, consisting of the baseline case without IS and three IS cases with different microalgae species, are comprehensively compared in terms of various economic and sustainability performance indicators. Compared to the baseline case, the results reveal that the synergies in the bioenergy IS system render an annual manufacturing cost reduction of >10% in all IS scenarios. It is also discovered that the symbiotic design can lead to a 36% reduction in greenhouse gas (GHG) emissions, a 9.4% decrease in eutrophication potential (EP), and a 7.5% reduction in acidification potential (AP) when adopting the same species of microalgae. The synergies among the bioenergy IS system stakeholders are proved to be viable and beneficial to enhance the economic viability and environmental sustainability.

ACS Style

Lin Li; Yuntian Ge; Minkun Xiao. Towards biofuel generation III+: A sustainable industrial symbiosis design of co-producing algal and cellulosic biofuels. Journal of Cleaner Production 2021, 306, 127144 .

AMA Style

Lin Li, Yuntian Ge, Minkun Xiao. Towards biofuel generation III+: A sustainable industrial symbiosis design of co-producing algal and cellulosic biofuels. Journal of Cleaner Production. 2021; 306 ():127144.

Chicago/Turabian Style

Lin Li; Yuntian Ge; Minkun Xiao. 2021. "Towards biofuel generation III+: A sustainable industrial symbiosis design of co-producing algal and cellulosic biofuels." Journal of Cleaner Production 306, no. : 127144.

Journal article
Published: 26 February 2021 in Materials & Design
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The integration of shape memory materials into additive manufacturing has added a new dimension of time to conventional 3D printing and enabled innovative product designs with high tailorability and adaptability. To date, most studies on shape memory effects mainly adopt experimental approaches to characterize the material responsiveness to various stimulation conditions considering a single thermomechanical loading cycle. The information regarding the cyclic shape memory behaviors as well as the potential additive manufacturing-induced impacts on the achieved shape memory performance is limited. In this paper, the shape memory behaviors of the stereolithography printed thermo-responsive structures are theoretically modeled by jointly considering the influences from both the printing process and the shape memory process. The cyclic shape memory effects are analytically characterized and experimentally validated using methacrylate copolymers under iterative thermomechanical loadings. Meanwhile, case studies are presented to provide insights into shape memory behaviors upon the impacts of various levels of critical process parameters. The results indicate an exceptional prediction accuracy of 96.24% and 95.73% for the established shape fixity and recovery models, respectively. It is also observed that the printing process parameters, including layer thickness and scan speed, have considerable impacts on the shape memory performance of the printed parts.

ACS Style

Jing Zhao; Muyue Han; Lin Li. Modeling and characterization of shape memory properties and decays for 4D printed parts using stereolithography. Materials & Design 2021, 203, 109617 .

AMA Style

Jing Zhao, Muyue Han, Lin Li. Modeling and characterization of shape memory properties and decays for 4D printed parts using stereolithography. Materials & Design. 2021; 203 ():109617.

Chicago/Turabian Style

Jing Zhao; Muyue Han; Lin Li. 2021. "Modeling and characterization of shape memory properties and decays for 4D printed parts using stereolithography." Materials & Design 203, no. : 109617.

Journal article
Published: 01 November 2020 in Applied Energy
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Cellulosic biofuel is considered a promising alternative to traditional fossil fuels. Currently, the commercialization of cellulosic biofuel is proceeding at a slower pace than expected due to the high cost of the cellulosic biofuel supply chain (CBSC), especially in the manufacturing process. In this paper, a new model for a four-echelon CBSC incorporating 12 conversion pathways is proposed to minimize total supply chain cost by considering multiple time periods, various biomass types and spatial distributions, biomass and biofuel inventories, logistics, and different demand levels. Case studies from the state of Illinois in the U.S. are used to demonstrate the effectiveness of the proposed model. It is manifested that the model can select appropriate conversion pathways for biorefineries given the abundance of local resources to minimize the overall cost. Compared to the two cases adopted a single conversion pathway, the optimal result achieves 14.6% and 4.6% reductions on the unit biofuel cost, respectively. In addition, the results of sensitivity analysis indicate that the biorefinery construction cost and the biofuel throughput are the main economic drivers in the current supply chain design.

ACS Style

Yuntian Ge; Lin Li; Lingxiang Yun. Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways. Applied Energy 2020, 281, 116059 .

AMA Style

Yuntian Ge, Lin Li, Lingxiang Yun. Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways. Applied Energy. 2020; 281 ():116059.

Chicago/Turabian Style

Yuntian Ge; Lin Li; Lingxiang Yun. 2020. "Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways." Applied Energy 281, no. : 116059.

Journal article
Published: 01 August 2020 in Additive Manufacturing
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The emergence of 4D printing has revolutionized the additive manufacturing industry by enabling dynamic shape memory effects ensured by the use of smart materials. In addition to 3D fabrication, 4D printed products need to undergo shape programming and recovery cycles to achieve desired shape memory effects. Due to the new process and material characteristics, energy consumption models established for 3D printing are no longer applicable for 4D printing. In current literature, the environmental sustainability for 4D printing has not yet been evaluated, leading to unknown environmental impacts that could be caused by 4D printing processes and/or materials. In this research, theoretical models for quantifying the energy consumption in 4D printing thermal-responsive polymers are established by jointly considering the compositional design for materials. Experiments and case studies are performed to validate the proposed models and further investigate some critical factors that can affect energy consumption, e.g., values of process parameters like layer thickness, and thermo-temporal conditions in shape memory cycles. The case study results show that overall energy consumption can be reduced by 1) increasing the concentrations of multi-functional crosslinkers in material composition, and 2) setting the shape programming and recovery temperatures as 10 to 15℃ above the material glass transition temperature without compromising the shape fixity and recovery ratios. In addition, by adjusting the influential parameters throughout different stages in 4D printing, the total energy consumption can be reduced by 37.33%, which corresponds to a reduction of 259.52 pounds of CO2 emissions per kilogram methacrylate resin.

ACS Style

Muyue Han; Yiran Yang; Lin Li. Energy Consumption Modeling of 4D Printing Thermal-responsive Polymers with Integrated Compositional Design for Material. Additive Manufacturing 2020, 34, 101223 .

AMA Style

Muyue Han, Yiran Yang, Lin Li. Energy Consumption Modeling of 4D Printing Thermal-responsive Polymers with Integrated Compositional Design for Material. Additive Manufacturing. 2020; 34 ():101223.

Chicago/Turabian Style

Muyue Han; Yiran Yang; Lin Li. 2020. "Energy Consumption Modeling of 4D Printing Thermal-responsive Polymers with Integrated Compositional Design for Material." Additive Manufacturing 34, no. : 101223.

Journal article
Published: 24 June 2020 in Additive Manufacturing
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The application of additive manufacturing technology has recently shifted from the fabrication of prototypes to functional end-use products. Consequently, the mechanical strength of products has become of significant importance. To quantify the global and local mechanical strength, it is necessary to characterize the micro-structures and their variation within the product. The extent of bonding between adjacent filaments, both within and between layers, as well as porosity are two of the most important parameters that directly contribute to the mechanical strength of parts in extrusion-based additive manufacturing. However, most of the existing models in the literature either significantly underestimate these parameters or fail to quantify or address their variation along the deposition path and build direction. Hence, in this paper, a hybrid physics-based and data-driven approach is proposed to quantify the extent of filament bonding, porosity, and their distribution within a geometry of interest by characterizing the temperature profile of filaments and their deformation. The proposed models for inter-layer and intra-layer bonding have an average accuracy of 95% and 94%, respectively. In addition, it is observed that the porosity variation model performs better for top layers compared to bottom layers with an average of 51% higher accuracy.

ACS Style

Azadeh Haghighi; Lin Li. A hybrid physics-based and data-driven approach for characterizing porosity variation and filament bonding in extrusion-based additive manufacturing. Additive Manufacturing 2020, 36, 101399 .

AMA Style

Azadeh Haghighi, Lin Li. A hybrid physics-based and data-driven approach for characterizing porosity variation and filament bonding in extrusion-based additive manufacturing. Additive Manufacturing. 2020; 36 ():101399.

Chicago/Turabian Style

Azadeh Haghighi; Lin Li. 2020. "A hybrid physics-based and data-driven approach for characterizing porosity variation and filament bonding in extrusion-based additive manufacturing." Additive Manufacturing 36, no. : 101399.

Journal article
Published: 23 June 2020 in Additive Manufacturing
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Owing to the unique layer-by-layer production method, additive manufacturing can provide higher design freedom and enhanced manufacturing complexity and capabilities, compared to traditional subtractive manufacturing. Currently, additive manufacturing technologies are mostly limited to small-scale rapid prototyping and tooling, due to the insufficient production throughput caused by their relatively low efficiency. Hence, to facilitate high-volume production using additive manufacturing, it is critical to evaluate and enhance the process efficiency in the early process planning stage by adjusting the values of critical process parameters. More specifically, in the mask image projection stereolithography process, layer-wise exposure time is the most influential factor that affects the build time as well as the product performance. The majority of existing studies on stereolithography process planning do not incorporate comprehensive curing models, leading to the lack of evaluation on the resulting performance of fabricated products. In this research, a novel process planning algorithm is proposed aiming to enhance the process efficiency by leveraging the dynamic time of exposure while ensuring achieved dimensional accuracy, surface quality, and mechanical properties. The case study results show that dynamic time of exposure outperforms constant exposure time as it can reduce the total build time by 6.25 % while improving dimensional accuracy, surface roughness, and hardness by 0.59 %, 27.94 %, and 3.07 %, respectively. Also, the sensitivity analysis results suggest that the incident irradiance of the light source and the scale size of the fabricated part are the most critical factors affecting the selection of exposure time, as a variation of 20 % of these two factors can lead to 19.27 % and 38.54 % changes in total production time, respectively.

ACS Style

Jing Zhao; Yiran Yang; Lin Li. Efficiency-aware process planning for mask image projection stereolithography: Leveraging dynamic time of exposure. Additive Manufacturing 2020, 36, 101407 .

AMA Style

Jing Zhao, Yiran Yang, Lin Li. Efficiency-aware process planning for mask image projection stereolithography: Leveraging dynamic time of exposure. Additive Manufacturing. 2020; 36 ():101407.

Chicago/Turabian Style

Jing Zhao; Yiran Yang; Lin Li. 2020. "Efficiency-aware process planning for mask image projection stereolithography: Leveraging dynamic time of exposure." Additive Manufacturing 36, no. : 101407.

Journal article
Published: 13 June 2020 in Sustainability
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Electric vehicles (EVs) have obtained increasing public interest due to the associated economic and environmental benefits. Recently, studies regarding the economic advantages of adopting EVs as energy storages for commercial/residential buildings are emerging. In fact, according to the U.S. Energy Information Administration, the industrial sector consumes more energy than all of the other sectors combined, which is about 54% of the world’s total delivered energy. The energy consumption pattern in manufacturing facilities is based on production schedules and the heat transfer between machines and the ambient surroundings, thus, differs greatly from commercial/residential buildings. However, little research attention has been given to analyse the synergies of integrating EVs and manufacturing facilities to improve energy efficiency. To fill this research gap, in this study, a comprehensive model is established to evaluate the economic and environmental performance of an energy sharing system that consists of the EVs, power grid, and manufacturing facilities (EPM) under Time-of-Use (TOU) electricity tariff. The model is formulated as a mixed integer nonlinear programming format by considering practical production schedules, heat exchange between machines and ambient surroundings, as well as the heating, ventilation, and air conditioning (HVAC) system. The case study results indicate that the presented EPM energy sharing system has great potential to reduce energy cost and CO2 emissions. In addition, compared to the results from winter scenarios, it is shown that more cost savings can be achieved in summer days.

ACS Style

Xiaolin Chu; Yuntian Ge; Xue Zhou; Lin Li; Dong Yang. Modeling and Analysis of Electric Vehicle-Power Grid-Manufacturing Facility (EPM) Energy Sharing System under Time-of-Use Electricity Tariff. Sustainability 2020, 12, 1 .

AMA Style

Xiaolin Chu, Yuntian Ge, Xue Zhou, Lin Li, Dong Yang. Modeling and Analysis of Electric Vehicle-Power Grid-Manufacturing Facility (EPM) Energy Sharing System under Time-of-Use Electricity Tariff. Sustainability. 2020; 12 (12):1.

Chicago/Turabian Style

Xiaolin Chu; Yuntian Ge; Xue Zhou; Lin Li; Dong Yang. 2020. "Modeling and Analysis of Electric Vehicle-Power Grid-Manufacturing Facility (EPM) Energy Sharing System under Time-of-Use Electricity Tariff." Sustainability 12, no. 12: 1.

Journal article
Published: 05 June 2020 in Journal of Manufacturing Processes
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Additive manufacturing, as one of the most promising advanced manufacturing technologies, has been obtaining increasing public interest since its first emergence in the 1980s. Owing to its unique layer-wise production method, additive manufacturing can fabricate complex parts and reduce the production time. Limited by current processes and materials, the overall performance of additive manufactured products is not always exceptional especially in terms of print quality, mechanical property, and sustainability. To address this issue, post-curing is often used to further alter the part performance. In this paper, a comprehensive evaluation is conducted for three most popular post-curing processes, i.e., conventional oven, microwave oven, and Ultraviolet chamber, considering their capabilities of altering ultimate tensile strength, hardness, dimensional variations, surface roughness, production cost, and energy consumption. To characterize the relation between post-curing process parameters and resulting performance, both analytical and statistical models are established and evaluated. The case study results suggest that various post-curing processes can cause different influences on the part performance. As an example, ultimate tensile strength can be improved by 70.83 % and 15.01 % when Ultraviolet and microwave oven are used, respectively. In addition, optimal post-curing strategies under different constraints are obtained based on established models, which will provide useful insights for post-curing process planning and optimization.

ACS Style

Jing Zhao; Yiran Yang; Lin Li. A comprehensive evaluation for different post-curing methods used in stereolithography additive manufacturing. Journal of Manufacturing Processes 2020, 56, 867 -877.

AMA Style

Jing Zhao, Yiran Yang, Lin Li. A comprehensive evaluation for different post-curing methods used in stereolithography additive manufacturing. Journal of Manufacturing Processes. 2020; 56 ():867-877.

Chicago/Turabian Style

Jing Zhao; Yiran Yang; Lin Li. 2020. "A comprehensive evaluation for different post-curing methods used in stereolithography additive manufacturing." Journal of Manufacturing Processes 56, no. : 867-877.

Journal article
Published: 13 December 2019 in Journal of Cleaner Production
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Additive manufacturing (AM) or 3D printing has been implemented in a wide range of areas, owing to its superior capabilities of fabricating complex geometries with high design freedom compared to traditional manufacturing. In recent years, the potential environmental impacts that can be caused by AM processes and materials have attracted increasing attentions. Research efforts have been conducted to study and attempt to enhance the environmental performance of AM. In current literature on AM energy consumption, most studies focus on the production stage and investigate the relation between energy consumption and process parameters (i.e., layer thickness). In this work, multiple geometry characteristics (e.g., surface areas and shapes) at each printing layer are studied and linked with the power consumption of mask image projection stereolithography using machine learning based approach. The established models will be able to provide AM designers with a useful tool for estimating power consumption based on layer-wise geometry information in the design stage and promote the awareness of cleaner production in AM. In this work, effective features are selected and/or extracted from layer-wise geometry characteristics and used to train and test machine learning models. According to our results, the shallow neural network has the lowest averaged root-mean-square error (RMSE) of 0.75% considering both training and testing, and the stacked autoencoders (SAE) structure has the best testing performance with RMSE of 0.85%.

ACS Style

Yiran Yang; Miao He; Lin Li. Power consumption estimation for mask image projection stereolithography additive manufacturing using machine learning based approach. Journal of Cleaner Production 2019, 251, 119710 .

AMA Style

Yiran Yang, Miao He, Lin Li. Power consumption estimation for mask image projection stereolithography additive manufacturing using machine learning based approach. Journal of Cleaner Production. 2019; 251 ():119710.

Chicago/Turabian Style

Yiran Yang; Miao He; Lin Li. 2019. "Power consumption estimation for mask image projection stereolithography additive manufacturing using machine learning based approach." Journal of Cleaner Production 251, no. : 119710.

Research article
Published: 21 February 2019 in Naval Research Logistics (NRL)
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Environmentally friendly energy resources open a new opportunity to tackle the problem of energy security and climate change arising from wide use of fossil fuels. This paper focuses on optimizing the allocation of the energy generated by the renewable energy system to minimize the total electricity cost for sustainable manufacturing systems under time‐of‐use tariff by clipping the peak demand. A rolling horizon approach is adopted to handle the uncertainty caused by the weather change. A nonlinear mathematical programming model is established for each decision epoch based on the predicted energy generation and the probability distribution of power demand in the manufacturing plant. The objective function of the model is shown to be convex, Lipchitz‐continuous, and subdifferentiable. A generalized benders decomposition method based on the primal‐dual subgradient descent algorithm is proposed to solve the model. A series of numerical experiments is conducted to show the effectiveness of the solution approach and the significant benefits of using the renewable energy resources.

ACS Style

Weiwei Cui; Lin Li; Zhiqiang Lu. Energy-efficient scheduling for sustainable manufacturing systems with renewable energy resources. Naval Research Logistics (NRL) 2019, 66, 154 -173.

AMA Style

Weiwei Cui, Lin Li, Zhiqiang Lu. Energy-efficient scheduling for sustainable manufacturing systems with renewable energy resources. Naval Research Logistics (NRL). 2019; 66 (2):154-173.

Chicago/Turabian Style

Weiwei Cui; Lin Li; Zhiqiang Lu. 2019. "Energy-efficient scheduling for sustainable manufacturing systems with renewable energy resources." Naval Research Logistics (NRL) 66, no. 2: 154-173.

Journal article
Published: 07 December 2018 in IEEE Transactions on Automation Science and Engineering
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Tolerance allocation is a design tool that is proven crucial for enhancing the cost effectiveness and productivity of manufacturing systems. The growing implementation of additive manufacturing (AM) with its unique characteristics requires novel tolerance allocation methodologies to be developed. More specifically, many of the assumptions in traditional tolerance allocation methods such as normality assumption, a priori known probability density function of data, and symmetricity of tolerances cannot be seamlessly applied to AM processes. Furthermore, as the obtained dimensional errors of components in AM processes are significantly affected by the decisions made during the manufacturing stage (e.g., selected process, material, layer thickness, and build direction), the manufacturing parameters need to be jointly considered for allocating feasible and optimum tolerances during the product design phase. In this paper, a methodology for joint dimensional tolerance allocation and manufacturing of assemblies fabricated by AM processes is proposed based on the asymmetric distribution of errors and considering assembly requirements, namely, specification and confidence level. The bootstrap statistical technique is used to estimate the unknown population's statistics. A cyclic optimization approach is adopted to tackle the formulated problem. The numerical examples are provided to illustrate the effectiveness of the proposed method.

ACS Style

Azadeh Haghighi; Lin Li. Joint Asymmetric Tolerance Design and Manufacturing Decision-Making for Additive Manufacturing Processes. IEEE Transactions on Automation Science and Engineering 2018, 16, 1259 -1270.

AMA Style

Azadeh Haghighi, Lin Li. Joint Asymmetric Tolerance Design and Manufacturing Decision-Making for Additive Manufacturing Processes. IEEE Transactions on Automation Science and Engineering. 2018; 16 (3):1259-1270.

Chicago/Turabian Style

Azadeh Haghighi; Lin Li. 2018. "Joint Asymmetric Tolerance Design and Manufacturing Decision-Making for Additive Manufacturing Processes." IEEE Transactions on Automation Science and Engineering 16, no. 3: 1259-1270.

Journal article
Published: 06 December 2018 in Materials & Design
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Owing to the unique layer-wise production method, additive manufacturing technologies have been widely adopted in rapid prototyping and tooling areas, which often require superior mechanical properties such as tensile strength and hardness. In current literature, most mechanical property studies focusing on additive manufactured materials mainly adopt experimental or simulation-based approaches, and therefore cannot be directly used to accurately estimate and predict the achieved mechanical properties. In addition, information regarding the mechanical properties of photosensitive liquid resin used in the Stereolithography additive manufacturing process is limited. Hence, in this paper, mathematical models are established to quantify the tensile strength and hardness of Stereolithography fabricated materials by estimating the solidification levels of both green parts and Ultraviolet post-cured parts. The established degree of cure model is shown to have an average prediction accuracy of around 94%. In addition, the mechanical property models have an average accuracy of 88% and 90% for tensile strength prediction, and 98% and 95% for hardness prediction of green parts and post-cured parts, respectively. It is also observed that the Ultraviolet post-curing process has the capability of significantly enhancing the studied mechanical properties.

ACS Style

Yiran Yang; Lin Li; Jing Zhao. Mechanical property modeling of photosensitive liquid resin in stereolithography additive manufacturing: Bridging degree of cure with tensile strength and hardness. Materials & Design 2018, 162, 418 -428.

AMA Style

Yiran Yang, Lin Li, Jing Zhao. Mechanical property modeling of photosensitive liquid resin in stereolithography additive manufacturing: Bridging degree of cure with tensile strength and hardness. Materials & Design. 2018; 162 ():418-428.

Chicago/Turabian Style

Yiran Yang; Lin Li; Jing Zhao. 2018. "Mechanical property modeling of photosensitive liquid resin in stereolithography additive manufacturing: Bridging degree of cure with tensile strength and hardness." Materials & Design 162, no. : 418-428.

Journal article
Published: 21 September 2018 in Journal of Manufacturing Systems
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Production system modeling (PSM) focuses on revealing the component-level principles of production procedures. Despite the extensive studies for PSM of serial production systems, assembly systems have received much less investigation. Among existing PSM researches on assembly lines, most are focused on the steady-state system performance, while the transient behavior of the inhomogeneous assembly systems is largely unexplored. This paper presents a new analytical PSM method for inhomogeneous assembly systems considering Bernoulli machines and finite buffers. By using the proposed ‘Split-View’ method, both transient and steady-state analyses of such systems can be performed. This model can be solved without using the traditional decomposition technique. The solvability of the established model has been proven theoretically. The sensitivity studies of system performance in both transient and steady states are numerically investigated. Numerical studies demonstrate the high accuracy of the method comparing with the simulation results.

ACS Style

Yukan Hou; Lin Li; Yuntian Ge; Kaifu Zhang; Yuan Li. A new modeling method for both transient and steady-state analyses of inhomogeneous assembly systems. Journal of Manufacturing Systems 2018, 49, 46 -60.

AMA Style

Yukan Hou, Lin Li, Yuntian Ge, Kaifu Zhang, Yuan Li. A new modeling method for both transient and steady-state analyses of inhomogeneous assembly systems. Journal of Manufacturing Systems. 2018; 49 ():46-60.

Chicago/Turabian Style

Yukan Hou; Lin Li; Yuntian Ge; Kaifu Zhang; Yuan Li. 2018. "A new modeling method for both transient and steady-state analyses of inhomogeneous assembly systems." Journal of Manufacturing Systems 49, no. : 46-60.

Journal article
Published: 20 September 2018 in International Journal of Production Economics
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Additive manufacturing has obtained widespread and continuously increasing interest, owing to its distinguished advantages (e.g., reduced material waste and enhanced manufacturing complexity) compared to traditional manufacturing processes. Currently, the implementation of additive manufacturing in the industrial sector is limited to small-scale production with high customization level. The cost analysis for complex production layouts especially with multiple different geometries are not fully studied, and some popular additive manufacturing processes are not well investigated for cost performance. Therefore, in this paper, a comprehensive cost model is established to theoretically evaluate the cost performance of the Mask Image Projection Stereolithography process for simultaneously fabricating multiple mixed geometries. In addition, an optimization problem is formulated to reduce the additive manufacturing costs considering the set of decision variables (layer thickness and surface stratification angle) under the constraints of throughput and part quality. The case study results indicate that 26% in cost saving can be achieved by solving the proposed optimization problem. Furthermore, a sensitivity analysis is conducted and shows that the raw material unit price and the initial investment on additive manufacturing hardware and software are the main cost drivers.

ACS Style

Yiran Yang; Lin Li. Cost modeling and analysis for Mask Image Projection Stereolithography additive manufacturing: Simultaneous production with mixed geometries. International Journal of Production Economics 2018, 206, 146 -158.

AMA Style

Yiran Yang, Lin Li. Cost modeling and analysis for Mask Image Projection Stereolithography additive manufacturing: Simultaneous production with mixed geometries. International Journal of Production Economics. 2018; 206 ():146-158.

Chicago/Turabian Style

Yiran Yang; Lin Li. 2018. "Cost modeling and analysis for Mask Image Projection Stereolithography additive manufacturing: Simultaneous production with mixed geometries." International Journal of Production Economics 206, no. : 146-158.

Journal article
Published: 01 September 2018 in Applied Energy
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As a promising alternative to fossil fuels, cellulosic biofuel has obtained considerable interest due to its potential for mitigating global climate change and enhancing energy security. However, the widespread adoption of cellulosic biofuel is taking place in a slower pace than expected. One major challenge is that the cellulosic biofuel production is still highly energy-intensive. In fact, the energy contained in cellulosic biofuel is less than the energy required for its production. To address this issue, in this paper, an analytical system-level energy model is proposed to characterize the fundamental relationships between total energy consumption and biofuel production parameters in cellulosic biofuel production systems. Furthermore, an optimization strategy based on Particle Swarm Optimization (PSO) is adopted to minimize the energy consumption of cellulosic biofuel production while maintaining the desired biofuel yield. A baseline case is implemented for analyzing energy consumption, and the results show that pretreatment consumes most energy among all processes and the water/biomass ratio is the most significant energy driver. In addition, the optimal solution results in a 21.09% reduction in the total energy consumption compared to the baseline case.

ACS Style

Yuntian Ge; Lin Li. System-level energy consumption modeling and optimization for cellulosic biofuel production. Applied Energy 2018, 226, 935 -946.

AMA Style

Yuntian Ge, Lin Li. System-level energy consumption modeling and optimization for cellulosic biofuel production. Applied Energy. 2018; 226 ():935-946.

Chicago/Turabian Style

Yuntian Ge; Lin Li. 2018. "System-level energy consumption modeling and optimization for cellulosic biofuel production." Applied Energy 226, no. : 935-946.

Journal article
Published: 26 June 2018 in IEEE Transactions on Smart Grid
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The Smart Grid opens many opportunities for electricity suppliers and customers to maintain grid stability, reduce electricity cost, and promote environmentally sustainable operation. Unfortunately, these benefits cannot be fully realized from industrial energy customers due to inadequate manufacturing decision-making methodology that cannot consider manufacturers and energy suppliers simultaneously. The influence manufacturers have on the grid comes from their large electricity demand contributing to peak power and their large natural gas usage due to the increasing dependency of the electricity sector on gas-fired generation. Nonetheless, the interdependency between manufacturers’ electricity and natural gas demand is not well studied in the literature. Hence, in this paper, an electricity and natural gas driven production scheduling model for manufacturers is established. The model considers timebased and event-based electricity and gas demand response. A Modified Simulated Annealing algorithm is proposed to solve the problem in reaction to real-time supply notifications so that the interaction between manufacturers and energy providers is promoted. Numerical case studies are implemented and illustrate that 66-68% in energy cost savings for the manufacturer can be achieved when using the proposed model compared to baseline scenarios. Meanwhile, the Modified Simulated Annealing algorithm outperforms various solution methods in solving the proposed problem.

ACS Style

Fadwa Dababneh; Lin Li. Integrated Electricity and Natural Gas Demand Response for Manufacturers in the Smart Grid. IEEE Transactions on Smart Grid 2018, 10, 4164 -4174.

AMA Style

Fadwa Dababneh, Lin Li. Integrated Electricity and Natural Gas Demand Response for Manufacturers in the Smart Grid. IEEE Transactions on Smart Grid. 2018; 10 (4):4164-4174.

Chicago/Turabian Style

Fadwa Dababneh; Lin Li. 2018. "Integrated Electricity and Natural Gas Demand Response for Manufacturers in the Smart Grid." IEEE Transactions on Smart Grid 10, no. 4: 4164-4174.

Original articles
Published: 12 June 2018 in IISE Transactions
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Hybrid additive–subtractive manufacturing processes are becoming increasingly popular as a promising solution to overcome the current limitations of Additive Manufacturing (AM) technology and improve the dimensional accuracy and surface quality of parts. Surface roughness, as one of the most important surface quality measures, plays a key role in the fit of assemblies and thus needs to be thoroughly evaluated at the design and manufacturing stages. However, most of the studies on surface roughness modelling and analysis employ empirical approaches, and only consider the effect of a single manufacturing process. In particular, the existing surface roughness models are not applicable to hybrid additive–subtractive manufacturing processes in which a secondary process is involved. In this article, analytical models are established to predict the surface roughness of parts fabricated by AM as well as hybrid additive–subtractive manufacturing processes. A novel surface profile representation scheme is also proposed to increase the prediction accuracy. Case studies are performed to validate the effectiveness of the proposed models. An average of 4.25% error is observed for the AM case, which is significantly smaller than the prediction error of the existing models in the literature. Furthermore, in the hybrid case, an average of 91.83% accuracy is obtained.

ACS Style

Lin Li; Azadeh Haghighi; Yiran Yang. Theoretical modelling and prediction of surface roughness for hybrid additive–subtractive manufacturing processes. IISE Transactions 2018, 51, 124 -135.

AMA Style

Lin Li, Azadeh Haghighi, Yiran Yang. Theoretical modelling and prediction of surface roughness for hybrid additive–subtractive manufacturing processes. IISE Transactions. 2018; 51 (2):124-135.

Chicago/Turabian Style

Lin Li; Azadeh Haghighi; Yiran Yang. 2018. "Theoretical modelling and prediction of surface roughness for hybrid additive–subtractive manufacturing processes." IISE Transactions 51, no. 2: 124-135.

Journal article
Published: 26 April 2018 in IEEE Transactions on Systems, Man, and Cybernetics: Systems
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With increasing concerns on climate change and energy shortage, manufacturing industries must adopt more sustainable production and facility control strategies. Such strategies require operational practices that emphasize sustainability by considering economic, energy, and environmental aspects simultaneously. In this paper, a new combined production scheduling model that jointly considers energy control and maintenance implementation to address the concerns of energy consumption, intelligent maintenance, and throughput improvement simultaneously is proposed. Multiple measures are combined and evaluated using a single objective, i.e., cost minimization. Particle swarm optimization, with a local optimal avoidable mechanism and a time varying inertial weight, is used to solve the cost minimization problem to find a near optimal solution of production and maintenance schedules. A numerical case study is implemented and the results show that the cost per unit production can be reduced up to 27% compared to the existing benchmark strategies. The implications to practitioners with respect to the tradeoff between cost and throughput/energy consumption, and the model applicability considering energy tariff structure, are also discussed to provide more insights for using the proposed joint model in the real world. The proposed model advances the state-of-the-art on maintenance and energy scheduling, which is typically performed exclusively. It is expected to guide operational activities on shop floors toward sustainability.

ACS Style

Zeyi Sun; Fadwa Dababneh; Lin Li. Joint Energy, Maintenance, and Throughput Modeling for Sustainable Manufacturing Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2018, 50, 2101 -2112.

AMA Style

Zeyi Sun, Fadwa Dababneh, Lin Li. Joint Energy, Maintenance, and Throughput Modeling for Sustainable Manufacturing Systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2018; 50 (6):2101-2112.

Chicago/Turabian Style

Zeyi Sun; Fadwa Dababneh; Lin Li. 2018. "Joint Energy, Maintenance, and Throughput Modeling for Sustainable Manufacturing Systems." IEEE Transactions on Systems, Man, and Cybernetics: Systems 50, no. 6: 2101-2112.

Review
Published: 30 March 2018 in Journal of Manufacturing Science and Engineering
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Dramatic advancements and adoption of computing capabilities, communication technologies, and advanced, pervasive sensing have impacted every aspect of modern manufacturing. Furthermore, as society explores the Fourth Industrial Revolution characterized by access to and leveraging of knowledge in the manufacturing enterprise, the very character of manufacturing is rapidly evolving, with new, more complex processes, and radically, 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 ever-increasing requirements in terms of quality, reliability, and productivity, needs that are being addressed in the knowledge domain. 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.

ACS Style

Dragan Djurdjanovic; Laine Mears; Farbod Akhavan Niaki; Asad Ul Haq; Lin Li. State of the Art Review on Process, System, and Operations Control in Modern Manufacturing. Journal of Manufacturing Science and Engineering 2018, 140, 061010 .

AMA Style

Dragan Djurdjanovic, Laine Mears, Farbod Akhavan Niaki, Asad Ul Haq, Lin Li. State of the Art Review on Process, System, and Operations Control in Modern Manufacturing. Journal of Manufacturing Science and Engineering. 2018; 140 (6):061010.

Chicago/Turabian Style

Dragan Djurdjanovic; Laine Mears; Farbod Akhavan Niaki; Asad Ul Haq; Lin Li. 2018. "State of the Art Review on Process, System, and Operations Control in Modern Manufacturing." Journal of Manufacturing Science and Engineering 140, no. 6: 061010.