Cellulosic ethanol can be used as a sustainable alternative transportation fuel. A major obstacle to restrict large-scale cellulosic ethanol manufacturing is low bulk density of cellulosic biomass that increases costs during transportation, storage and application of biomass. Biomass pelleting can significantly increase density of biomass. Pellets with high density can be handled, transported, stored and utilized easily. Ring-die pelleting, a traditional pelleting method, is widely used in biomass densification industry. This paper reported an experimental study to compare pellet quality (such as equilibrium moisture content, density, and durability) and microstructure of corn stover processed by a ring-die pellet mill with three different die sizes. Results showed that round and bigger die (32 mm instead of 9 mm) results in higher pellet density, durability, and lower equilibrium moisture content.

The manufacturing industry is evolving and starting to use 3D models as the central knowledge artifact for product data and product definition, or what is known as Model-based Definition (MBD). The Model-based Enterprise (MBE) uses MBD as a way to transition away from using traditional paper-based drawings and documentation. As MBD grows in popularity, it is imperative to understand what information is needed in the transition from drawings to models so that models represent all the relevant information needed for processes to continue efficiently. Finding this information can help define what data is common amongst different models in different stages of the lifecycle, which could help establish a Common Information Model. The Common Information Model is a source that contains common information from domain specific elements amongst different aspects of the lifecycle. To help establish this Common Information Model, information about how models are used in industry within different workflows needs to be understood. To retrieve this information, a survey mechanism was administered to industry professionals from various sectors. Based on the results of the survey a Common Information Model could not be established. However, the results gave great insight that will help in further investigation of the Common Information Model.

Advances in information technology triggered a digital revolution that holds promise of reduced costs, improved productivity, and higher quality. To ride this wave of innovation, manufacturing enterprises are changing how product definitions are communicated — from paper to models. To achieve industry’s vision of the Model-Based Enterprise (MBE), the MBE strategy must include model-based data interoperability from design to manufacturing and quality in the supply chain. The Model-Based Definition (MBD) is created by the original equipment manufacturer (OEM) using Computer-Aided Design (CAD) tools. This information is then shared with the supplier so that they can manufacture and inspect the physical parts. Today, suppliers predominantly use Computer-Aided Manufacturing (CAM) and Coordinate Measuring Machine (CMM) models for these tasks. Traditionally, the OEM has provided design data to the supplier in the form of two-dimensional (2D) drawings, but may also include a three-dimensional (3D)-shape-geometry model, often in a standards-based format such as ISO 10303-203:2011 (STEP AP203). The supplier then creates the respective CAM and CMM models and machine programs to produce and inspect the parts. In the MBE vision for model-based data exchange, the CAD model must include product-and-manufacturing information (PMI) in addition to the shape geometry. Today’s CAD tools can generate models with embedded PMI. And, with the emergence of STEP AP242, a standards-based model with embedded PMI can now be shared downstream. The on-going research detailed in this paper seeks to investigate three concepts. First, that the ability to utilize a STEP AP242 model with embedded PMI for CAD-to-CAM and CAD-to-CMM data exchange is possible and valuable to the overall goal of a more efficient process. Second, the research identifies gaps in tools, standards, and processes that inhibit industry’s ability to cost-effectively achieve model-based-data interoperability in the pursuit of the MBE vision. Finally, it also seeks to explore the interaction between CAD and CMM processes and determine if the concept of feedback from CAM and CMM back to CAD is feasible. The main goal of our study is to test the hypothesis that model-based-data interoperability from CAD-to-CAM and CAD-to-CMM is feasible through standards-based integration. This paper presents several barriers to model-based-data interoperability. Overall, the project team demonstrated the exchange of product definition data between CAD, CAM, and CMM systems using standards-based methods. While gaps in standards coverage were identified, the gaps should not stop industry’s progress toward MBE. The results of our study provide evidence in support of an open-standards method to model-based-data interoperability, which would provide maximum value and impact to industry.

3D printing has been recognized as an efficient and sustainable technology in the fields of advanced manufacturing. In the past few years, a considerable research, including basic theoretical research, technology innovation and industries application, have been conducted to promote 3D printing for a better performance in manufacturing. However, the benefits of 3D printing from environmental perspective are still to be seen and its sustainability is also a mystery. This paper presents a critical review about the qualitative and quantitative environmental impact of 3D printing to provide a comprehensive understanding of 3D printing for the public and provide a better guide for the future research. In addition, based on the principle of multi-objective optimization, this paper proposes a novel framework for 3D printing processes sustainability assessment and improvement by integrating the product Computer Aided Design (CAD) and Life Cycle Assessment (LCA). At last, recommendations about major concerns when analyzing the sustainability of 3D printing are put forward, which might be considered for the coming research.

Increasing manufacturing complexity continues to be one of the most significant challenges facing the manufacturing industry today. Due to these rapid changes in manufacturing systems, one of the most important factors affecting production is recognized as the frequent production setup or changeovers, consequently affecting the overall production lead times and competitiveness of the company. Developing responsive production setup and process capability is increasingly important as product ranges and varieties in manufacturing companies are growing rapidly and, at the same time, production business models are operating more towards being customer-oriented. Furthermore, although different conventional methods have been used to manage complexity in production changeovers, sustainability and competitiveness development in a manufacturing company needs to be scientifically addressed by managing manufacturing complexity. In this paper, a sustainable manufacturing-oriented approach is presented in mind of managing manufacturing changeover complexities. A case study is carried out specifically concerning changeover complexity in a pharmaceutical company, aiming at minimizing complexities in production changeover and waste, increasing plant flexibility and productivity, and ultimately the sustainable competitiveness of the company in managing manufacturing changes.

This research experimentally investigates the characteristics of micro end-milling process of titanium alloy using nanofluid minimum quantity lubrication (MQL) with chilly CO 2 gas. In the nanofluid MQL, hexagonal boron nitride (hBN) particles having a lamellar structure are used. They have high aspect ratio and enable sliding against other particles, which can provide better lubricity. In addition, the chilly CO 2 gas enhances a cooling effect during the micro end-milling process. A series of micro end-milling experiments are conducted in the meso-scale machine tool system, and milling force, coefficient of friction, surface roughness and tool wear are observed and analyzed according to varying lubrication and cooling conditions. The results show that the nanofluid MQL with chilly gas can be effective for reducing milling forces, coefficient of friction, tool wear and improving surface quality.

Linear motor drives (LMDs) are well known to provide significant advantages in terms of positioning speed and precision over traditional screw drives (SDs), making them better suited for high-speed, high-precision machine tools. However, their use in such machine tools is limited by their tendency to consume a lot of electrical energy and cause thermal issues that help drive up costs. A hybrid feed drive (HFD) has been proposed as a possible solution to this dilemma. The HFD combines LMD and SD actuation to achieve speeds and accuracies similar to LMDs while consuming much less energy. This paper explores control strategies to further improve the performance of the HFD without unduly sacrificing its efficiency. First, it highlights two performance limitations of the controller proposed for the HFD in prior work, namely, imperfect tracking and suboptimal feedback gains. Then it compares two approaches for achieving perfect tracking with regard to performance and energy efficiency. Finally, it presents an approach for optimizing the feedback gains of the HFD to achieve the best positioning performance. Simulations and experiments are used to demonstrate significant gains in precise positioning using the methods proposed in this paper, while maintaining superb energy efficiency relative to an equivalent LMD.

Studying the goals, ideas and trends of the concept of cybertronic production systems (CTPS) leads to the hypothesis that CTPS supports a sustainable development. The paper proposes an approach for simulating and verifying this hypothesis. Cybertronic production systems are formed by the application of cybertronic systems (CTS) in production. To examine the behavior of cybertronic systems depending on their level of interconnection, intelligence and independence a multi-agent system of a production system is implemented. This simulation system can be used as virtual test field to evaluate impacts of changes in the production system on all level of detail (process, machine tool, factory level) by using sustainability indicators on factory level.

Ice thermal storage system is a novel technology used in HVAC&R systems for improving energy efficiency of manufacturing facilities. In this paper, the investigation of ice formation in a typical ice thermal storage system was conducted to study the factorial effect of the new structure of thin layer ring placed in a rectangular cavity filled with water on solidification process. Numerical method was used for the computational solutions of fluid flow domain. The performance of the thin layer ring was successfully demonstrated by achieving an increased ice generation area and shortening the time period of ice formation for a typical ice thermal storage system. More detailed research on material, thickness and arrangement of the ring has been comparatively conducted for heat transfer enhancement. The parameters of thin layer ring from three perspectives have been investigated statistically with nine combinations of model are carried out by combining different levels of each factor via Taguchi Method. The results show the contribution ratio of each factor of thin layer ring on improving energy efficiency of ice thermal storage system.

Paint shop is the biggest energy consumer in automotive manufacturing plant. Large amount of conditioned air, which leads to a high energy demand, is one of the basic demands in guaranteeing high quality product and a comfortable working environment. A good design of HVAC system in the paint shop is also crucial in energy conservation. This paper presents thermodynamic simulation models of the HVAC system for both painting booth and building. Key parameter effects of the energy consumption were analyzed; different air flow routes were compared. Finally, optimal operation strategies in terms of energy, cost, and emission were discussed.

Sustainability of nanomanufacturing has triggered tremendous research interest in recent years, especially the energy consumption and environmental impacts of nanomaterial. TiO 2 nanotube is a typical material with broad application potential. In this paper a thermodynamic analysis model integrating the energy consumption and exergy losses is presented for sustainability improvement of TiO 2 nanotube synthesis process. This thermodynamic model was validated by the TiO 2 nanotube electrochemical anodization process. Comparisons of energy consumption, exergy losses and environmental impacts of five main stages showed the effective sustainability improvement potential. This work can be extended to sustainability improvement of other similar nanomaterial synthesis processes.

Improving the machinability of titanium (Ti) alloys remains unresolved for manufacturing industries because excessive tool wear and catastrophic tool failures lead to shortened tool life and low productivity with any available cutting tool system. Besides optimizing the substrate and/or coating materials for cutting tools, improving the cooling and lubricating conditions is one of the ways to improve the machinability of Ti alloys. In this paper, we explore the possibility of using a nano-platelet, lamellar-type solid lubricant of graphite Exfoliated graphite nano-platelets (xGnP ® ) grade C750 (or xGnP750) in Minimum Quantity Lubrication (MQL) machining of Ti-6Al-4V (Ti64). Due to the lamellar or layered crystal structure, each layer easily slides against adjacent layers to provide the lubricity when introduced at the tool/work material interface. Although the nano-platelets have a nano-thickness, they have a micro-sized diameter, which prevents the nano-platelets from penetrating through human skin and breathing through nose. This makes the great advantage in this approach compared to other nano-enhanced MQL processes. The milling experiment shows that the nano-platelets present in the MQL oil decreased flank wear and improved the tool life compared to traditional MQL with pure oil as well as dry machining. The presence of nano-platelets reduces the micro chipping and tool fracture caused by the effect of impact in interrupted machining.

In this paper, the micro-scale grinding processes of titanium alloy (Ti-6Al-4V) using electro-hydro-dynamic (EHD) spray with nanofluid and compressed air are experimentally investigated. In the experiments, specific micro-grinding forces and surface roughness of the ground workpiece are quantitatively analyzed as a function of nanofluid’s concentration and size of nanoparticles. In addition, the ground surface quality is qualitatively investigated by comparing the optical microscopic images. The experimental results show the effectiveness of EHD spraying with nanofluid and compressed air for reducing the specific micro-grinding forces and enhancing ground surface quality.

In this paper, we introduce a simulation-based method to implement electricity demand response for manufacturers considering both heating ventilation and air conditioning (HVAC) and manufacturing systems. Compared with the existing literature where the demand response implementation for manufacturing system and HVAC system is usually conducted separately, this paper advances the state of the art by combining two systems together in demand response. A joint simulation model is established using ProModel and EnergyPlus. ProModel is first used to simulate the demand response for the manufacturing system to obtain the demand response actions without influencing production. After that, these actions are used as input information in EnergyPlus where the HVAC and building models are developed. The interaction between the heat generated due to manufacturing machine operation and the indoor heat demand is explored. Three different demand response strategies, i.e., 1) the baseline model that neither manufacturing system nor HVAC is considered for demand response; 2) only manufacturing system is considered for demand response; and 3) both the HVAC and manufacturing systems are considered for demand response, are presented and compared. The results show that the combined model can achieve high power demand reduction during demand response event.

This paper prescribes an energy assessment methodology, including the procedure and mathematical foundation for evaluating total use and efficiency, for mechanical and electrical product assembly processes. The majority of major manufacturers assemble components and subsystems often produced by suppliers in their supply chains. Differing measurement processes of resource use in product assembly processes yield discrepant resource use assessments. We developed a sequence of activities, including goal setting, defining baselines, measurement, analysis, reporting, evaluation, and resetting the goal for product assembly processes. These activities are to be pursued in an iterative process leading to incremental improvements towards achieving sustainable manufacturing. An application example of reflow soldering was used for verification of the proposed new process’ representativeness and completeness. This methodology provides a guideline for manufacturers to make sound decisions that can be tightly integrated with their business strategy.

Using a machine learning approach, this study investigates the effects of machining parameters on the energy consumption of a milling machine tool, which would allow selection of optimal operational strategies to machine a part with minimum energy. Data-driven prediction models, built upon a nonlinear regression approach, can be used to gain an understanding of the effects of machining parameters on energy consumption. In this study, we use the Gaussian Process to construct the energy prediction model for a computer numerical control (CNC) milling machine tool. Energy prediction models for different machining operations are constructed based on collected data. With the collected data sets, optimum input features for model selection are identified. We demonstrate how the energy prediction models can be used to compare the energy consumption for the different operations and to estimate the total energy usage for machining a generic part. We also present an uncertainty analysis to develop confidence bounds for the prediction model and to provide insight into the vast parameter space and training required to improve the accuracy of the model. Generic parts are machined to test and validate the prediction model constructed using the Gaussian Process and we consistently achieve an accuracy of over 95 % on the total predicted energy.

Manufacturers lack the proper economic tools to improve energy efficiency of the plant. This paper uses energy economic analysis to increase the overall profit of the manufacturing facility by reducing energy consumption with minimal throughput impact. A return on investment strategy is developed to give plant managers the quantitative tools to select the machine which, when replaced, will lead to the largest return on investment. Over the long term, this will lead to the largest decrease in energy costs. To increase profits on a daily basis, a control methodology is developed that uses energy opportunity windows to insert downtime events that have minimal production impact, but reduce the overall energy consumption. A simulation case study is performed to test the control methodology and the return on investment strategy to show the effect of both on the profit of the manufacturing facility.

Composite materials are widely used in various sectors such as aerospace, wind energy and automotive. The high demand especially for thermoset based glass (GFRP) and carbon fibre reinforced polymer (CFRP) composite materials has led to a rise in volumes of manufacturing scrap and end-of-life products as composite waste. Unlike thermoplastic polymers, thermoset polymers have difficulties in recycling due to their cross-linked nature. In this paper, thermoset composite recycling processes which are grouped into mechanical, thermal and chemical methods are assessed from the perspectives of energy consumption, processing rate and mechanical performance of the recycled products. The paper presents a benchmark of composite technologies as well as identifies research challenges.

Growing awareness and concern within society over the use of and reliance on fossil fuels has stimulated research efforts in identifying, developing, and selecting alternative energy sources and energy technologies. Bioenergy represents a promising replacement for conventional energy, due to reduced environmental impacts and broad applicability. Sustainable energy challenges, however, require innovative manufacturing technologies and practices to mitigate energy and material consumption. This research aims to facilitate sustainable production of bioenergy from forest biomass and to promote deployment of novel processing equipment (mobile bio-refinery units). The study integrates knowledge from the renewable energy production and supply chain management disciplines to evaluate economic targets of bioenergy production with use of qualitative and quantitative techniques. The decision support system method employs two phases: (1) classification of potential biomass harvesting sites via decision tree analysis and (2) optimization of the supply network through a mixed integer linear programming model that minimizes the costs of upstream and midstream supply chain segments. While mobile units are shown to reduce biomass-to-bioenergy supply chain costs, production and deployment of the units is limited due to undeveloped bioenergy supply chains and quality uncertainty. It is reiterated that future research must address process-related and systemic issues in pursuit of sustainable energy technology development.

Existing methods for bottleneck detection can be categorized into two: methods based on stochastic analysis and methods based on data-driven analysis. The stochastic methods are accurate in estimating bottlenecks in long term, ignoring the current improvement opportunities, while the data-driven methods tend to do the opposite. In this paper, we develop an optimal policy to integrate the two methods based on Markov decision theory. The characterization of the optimal policy is provided. In addition, to implement the policy, the optimal frequency for carrying out bottleneck analysis is investigated. Numerical experiment is performed to validate the effectiveness of the optimal policy and compare it to the existing methods.