Energy Efficiency In Manufacturing Research Articles

Enhancing and Explaining Energy Management in Smart Manufacturing Production via ML Within the Context of Industry 4.0

Objectives: This study aims to explore the integration of big data and machine learning to enhance energy efficiency in manufacturing, focusing on the opportunities presented by Industry 4.0 and cyber-physical production systems. Theoretical Framework: The research leverages supervised learning techniques to analyze and predict machine-specific energy consumption patterns, utilizing energy disaggregation as a foundational concept. Method: Various machine learning algorithms, including Lasso regression, Linear regression, and Decision Tree, will be employed to develop predictive models for energy usage. Additionally, Explainable Machine Learning (XML) techniques will be utilized to ensure interpretability and clarity in the prediction outcomes. Results and Discussion: The proposed framework aims to elucidate the relationship between equipment utility and energy consumption, providing comprehensible explanations that enhance decision-making processes within intelligent industrial environments. Research Implications: This work highlights the significant potential of XML in transforming machine learning applications in manufacturing, paving the way for improved energy efficiency and operational effectiveness. Originality/Value: The introduction of an innovative framework that combines machine learning with explainability in the context of energy consumption marks a valuable contribution to the fields of manufacturing and sustainable industry practices.

Regional differences and catch-up analysis of energy efficiency in China’s manufacturing industry under environmental constraints

For coordinated regional growth and the development of high-quality manufacturing, China must narrow its regional energy efficiency gap and catch up inter-regionally. This paper focuses on whether China’s inter-provincial manufacturing energy efficiency has technological diffusion and a catch-up effect and explores its possible influencing factors, which are important for narrowing the differences in China’s manufacturing energy efficiency and promoting the improvement of the overall level of efficiency. Between 2011 and 2020, 30 Chinese manufacturing industries will be evaluated using a non-radial distance function model under environmental conditions. By employing the Dagum Gini coefficient method, regional disparities were analyzed, with hyper-variable density and efficiency discrepancies between regions making a noteworthy contribution. This paper evaluated a catch-up effect by constructing a frontier productivity model that considered the influence of China’s manufacturing energy efficiency. Results show a general rise in energy efficiency, particularly in coastal regions, higher than inland ones. The Gini coefficient of energy efficiency in manufacturing experienced a slight increase; however, when comparing it to the regional efficiency frontier, the catch-up effect and technology diffusion effect of China’s provincial manufacturing energy efficiency become more pronounced when taking into account the national efficiency frontier; the sub-regional manufacturing energy efficiency catch-up effect has different performances; the catch-up and technology diffusion effect is more evident after controlling for Economic development, innovation levels, the environmental regulation, and the proportion of high-energy-consumption output value and other influencing factors.

Thermophysical Characterization of Materials for Energy-Efficient Double Diaphragm Preforming

The Double Diaphragm Preforming (DDPF) process uses vacuum pressure and heat to pre-shape dry carbon fabric reinforcements between flexible diaphragms in liquid composite molding (LCM). Accurate modeling of heat transfer within DDPF requires knowledge of the thermophysical properties of the constituent materials under processing conditions. This study investigates the thermal conductivity of silicone diaphragms and carbon fabrics with embedded thermoplastic binder webs, utilizing transient plane source (TPS) and modified transient plane source (MTPS) methods, respectively. Additionally, the specific heat of silicone, carbon fibers (both chopped and powdered), and binder were measured using differential scanning calorimetry (DSC). Mixed samples comprising chopped fibers with 1.8 wt% binder and powdered fibers with 3.6 wt% binder was also analyzed with DSC. This study also examined the influence of reheating cycles on the specific heat of carbon fiber—3.6 wt% binder samples—and the effect of compaction and vacuum on the thermal conductivity of carbon fabrics with an embedded binder. While silicone exhibited linear-specific heat behavior, the thermoplastic binder showed non-linearity due to phase change. The combined carbon fiber-binder samples demonstrated slight non-linear specific heat variations depending on reheating cycles. The thermal conductivity of the fabric preforms decreased with the addition of thermoplastic binder and under vacuum-compaction conditions. The established temperature-dependent specific heat relationships and thermal conductivity provide valuable data for optimizing DDPF preforming parameters and enhancing energy efficiency in composite manufacturing.

Regulating cutting fluid parameters for optimal energy and economic performance: Methods for efficient and Low-Energy electrical machining

Scholars today have been dedicated to researching economically viable and efficient methods to enhance the energy efficiency of manufacturing, aiming for sustainable production. Wire Electrical Discharge Machining (WEDM) holds a significant position in current mechanical processing due to its unique operational principle, which results in notably higher energy consumption compared to conventional processing methods. Various factors influencing machining performance are complex, among which the condition of the cutting fluid plays a crucial role. Effectively controlling the parameters of the cutting fluid to improve its discharge state during machining is vital for enhancing energy and economic efficiency. Specifically, this study analyzes the characteristics of the WEDM process, models its resource and energy consumption, and predicts its economy. A method for optimizing control of cutting fluid parameters is proposed. By correlating different cutting fluid parameters with machining performance using the selected machine tool, and recommending combinations of cutting fluid parameters that ensure quality while improving the material removal rate, the study aims to achieve optimal energy efficiency and minimal cost. The results show that the proposed models for energy efficiency and economic prediction have an average accuracy of 96.64 %. An optimized machining scheme selected reduces machining time and energy consumption by approximately 1255.6 s (about 19.14 %) and 2240112.92 J (about 17.48 %), respectively, and saves about ¥ 6.084 (approximately 14.90 %) in machining costs. Additionally, the energy consumption of the pulse system’s proportion of total energy use increased by 1.63 %. The research demonstrates a general method for specifying combinations of cutting fluid parameters aimed at optimal energy efficiency and economy during the process optimization design stage of machining, providing a reference for improving the energy efficiency of wire cutting operations.

Energy Efficiency in Additive Manufacturing: Condensed Review

Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly use these advancing technologies. This paper provides a holistic review of this important concept from the perspectives of process, materials science, industry, and initiatives. The goal of this research study is to collect and present the latest knowledge blocks related to the energy consumption of AM technologies from a number of recent technical resources. Overall, they are the collection of surveys, observations, experimentations, case studies, content analyses, and archival research studies. The study highlights the current trends and technologies associated with energy efficiency and their influence on the AM community.

Energy-efficient production control of a make-to-stock system with buffer- and time-based policies

Increasing energy efficiency in manufacturing has significant environmental and cost benefits. Turning on or off a machine dynamically while considering the production rate requirements can offer substantial energy savings. In this work, we examine the optimal policies to control production and turn on and off a machine that operates in working, idle, off, and warmup modes for the case where demand inter-arrival, production, and warmup times have phase-type distributions. The optimal control problem that minimises the expected costs associated with the energy usage in different energy modes and the inventory and backlog costs is solved using a linear program associated with the underlying Markov Decision Process. We also present a matrix-geometric method to evaluate the steady-state performance of the system under a given threshold control policy. We show that when the inter-arrival time distribution is not exponential, the optimal control policy depends on both the current phase of the inter-arrival time and inventory position. The phase-dependent policy implemented by estimating the current phase based on the time elapsed since the last arrival yields a buffer- and time-based policy to control the energy mode and production. We show that policies that only use the inventory position information can be effective if the control parameters are chosen appropriately. However, the control policies that use both the inventory and time information further improve the performance.

Industrial Energy Optimisation: A Laser Cutting Case Study

In an increasingly technological world, energy efficiency in manufacturing is of great importance. While large manufacturing corporations have the resources to commission energy studies with minimal impact on operations, this is not true for small and medium enterprises (SME’s). These businesses will commonly only have a small number of laser processing cells; thus, to carry out an energy study can be extremely disruptive to normal operations. Since rising global energy costs also have the largest impact on small businesses who lack the benefit of economies of scale, they are simultaneously the most in need of improvements to energy efficiency, while also facing the strongest practical barriers to implementing them. In this study, a laser processing energy analysis methodology was designed to run simultaneously with normal operation and applied to a laser shim-cutting cell in a UK-based SME. This paper demonstrates the methodology for identifying operating states in a production environment and Specific Energy Consumption and Scope 2 CO2 emissions results are analysed. The Processing state itself was the most impactful on overall energy performance, at 55% for single sheets of material, increasing to 71% when batch processing. Generating idealised data in this production environment is challenging with restrictions to isolating variables, these “real-world” limitations for conducting system energy analysis simultaneously with live production are also discussed to present recommendations for further analysis.

Maximizing Energy Efficiency in Additive Manufacturing: A Review and Framework for Future Research

Additive manufacturing (AM) offers unique capabilities in terms of design freedom and customization, contributing to sustainable manufacturing. However, energy efficiency remains a challenge in the widespread adoption of AM processes. In this paper, we present a comprehensive review of the current research on energy efficiency in AM, addressing challenges, opportunities, and future directions. Our analysis reveals a lack of standardization in the measurement and reporting of energy consumption, making it difficult to evaluate and compare the energy performance of various systems. We propose a holistic framework to address energy efficiency throughout the entire life cycle of the AM process, highlighting the importance of design optimization, material selection, advanced control systems, and energy management strategies. The paper also emphasizes the need for further research on the interactions between process parameters, along with the potential of integrating renewable energy sources into AM systems. This review offers valuable insights for both academics and industry professionals, calling for standardized methodologies and a focus on energy management to optimize energy efficiency in AM processes, ultimately enhancing competitiveness and sustainability in modern manufacturing.

Recycling of polymer modified bituminous mixes with reclaimed asphalt pavement – An experimental study

Recycling is an attempt to effectively utilize the used material to manufacture new material of acceptable or if possible superior quality materials. Recycling promotes resource preservation, energy efficiency, and cost-effectiveness in manufacturing. Recycling can be effectively applied for bituminous pavements. The scarified bituminous material from the surface course can be mixed with fresh aggregates and/or fresh binder to relay new pavements performing the same function as in the original application. Thus scarified bituminous material, which is milled from asphalt surface is recycled in hot mix asphalt. Hot mix recycling has shown to be an effective method due to its low cost and capacity to produce mix of a high calibre. Thus, reclaimed asphalt pavement recycling is necessary for use for technical, economical, and environmental reasons. The purpose of the study is to assess the effectiveness of hot asphalt mixtures using recycled asphalt pavement (RAP). Bituminous material that had been scarified was obtained for the current investigation from a national highway in Belagavi, India. It was discovered that the project location has a BC-II and PMB-40 gradation. The site-recycled material underwent a bitumen extraction test to determine its gradation and binder concentration. In the current laboratory experiment, four different sorts of mixes were created and put to the test. New bituminous mixes were made using the BC-II gradation. Two mixes were proportioned, and recycled materials were used to partially replace the coarse and fine aggregates in the BC-II mix, respectively. The mixes were produced using the Marshall approach and the MS-2 criterion. The mixes were investigated in terms of their Marshall properties, moisture susceptibility through ITS & TSR tests. The recycled mix when combined with fresh aggregates and binder to compensate for loss in gradation and ageing of binder respectively results in a mix of properties similar and even better than fresh bituminous mix.

Modeling product carbon footprint for manufacturing process

To reduce carbon intensity and improve energy efficiency in product manufacturing, a comprehensive carbon emission model is essential. Most of the existing researches focus on specific aspects of part machining and lack a quantitative model of the product manufacturing process. Therefore, this paper decomposes and evolves the carbon footprint of product manufacturing from the workshop layer, forming a systematic carbon emission quantitative model. The product manufacturing process is divided into manufacturing equipment layer, part layer, product layer and workshop layer. The sources of carbon emissions in each layer are analyzed and modeled. Finally, take a certain type of wind turbine gearbox as an example. The carbon emissions model for manufacturing process of wind turbine gearbox is established, which provides directions for carbon emission reduction of related manufacturing enterprises. The results show that the rear housing, front housing, and 1st plant carrier are the main carbon emission parts of wind turbine gearbox.

Manufacturing Energy Efficiency and Industry 4.0

This Special Issue of Energies was devoted to the topic of “Manufacturing Energy Efficiency and Industry 4.0”. To a great extent, this issue follows the successful previous Special Issue on “Energy Efficiency of Manufacturing Processes and Systems”, which attracted some significant attention from scholars, practitioners, and policy-makers from all over the world. In total, six papers were published. The main topics included energy efficiency improvement in both the manufacturing process and system levels, as well as how this can be facilitated through the use of Industry 4.0.

Impact of Intelligent Manufacturing on Total-Factor Energy Efficiency: Mechanism and Improvement Path

Intelligent technology is the core driving force of the fourth industrial revolution, which has an important impact on high-quality economic development. In this paper, the panel data of 30 provinces from 2006 to 2019 were selected to construct a regression model to conduct an empirical analysis on the role and mechanism of intelligent manufacturing in improving total factor energy efficiency. The research results show that first, the productivity effect, scale effect and resource allocation effect of intelligent manufacturing can significantly improve the energy efficiency of the total factor, and the conclusion is still established after endogenous treatment and robustness testing. Second, the results of the action mechanism show that labor price distortion and carbon emission trading policy are important mechanisms for intelligent manufacturing to improve total-factor energy efficiency. Specifically, the corrected labor price can enhance the motivation of enterprise research and development and innovation and solve the dilemma of the low-end industrial structure, thus improving the efficiency of total-factor energy efficiency. The carbon emission trading policy strengthens the willingness of enterprises to improve the process, eliminate backward equipment and increase the research and development of green technology, and it has a positive regulatory role in the process of improving total-factor energy efficiency in intelligent manufacturing.

Energy Consumption vs. Tensile Strength of Poly[methyl methacrylate] in Material Extrusion 3D Printing: The Impact of Six Control Settings.

The energy efficiency of material extrusion additive manufacturing has a significant impact on the economics and environmental footprint of the process. Control parameters that ensure 3D-printed functional products of premium quality and mechanical strength are an established market-driven requirement. To accomplish multiple objectives is challenging, especially for multi-purpose industrial polymers, such as the Poly[methyl methacrylate]. The current paper explores the contribution of six generic control factors (infill density, raster deposition angle, nozzle temperature, print speed, layer thickness, and bed temperature) to the energy performance of Poly[methyl methacrylate] over its mechanical performance. A five-level L25 Taguchi orthogonal array was composed, with five replicas, involving 135 experiments. The 3D printing time and the electrical consumption were documented with the stopwatch approach. The tensile strength, modulus, and toughness were experimentally obtained. The raster deposition angle and the printing speed were the first and second most influential control parameters on tensile strength. Layer thickness and printing speed were the corresponding ones for the energy consumption. Quadratic regression model equations for each response metric over the six control parameters were compiled and validated. Thus, the best compromise between energy efficiency and mechanical strength is achievable, and a tool creates significant value for engineering applications.

Mechanical Performance over Energy Expenditure in MEX 3D Printing of Polycarbonate: A Multiparametric Optimization with the Aid of Robust Experimental Design

Sustainability and energy efficiency of additive manufacturing (AM) is an up-to-date industrial request. Likewise, the claim for 3D-printed parts with capable mechanical strength remains robust, especially for polymers that are considered high-performance ones, such as polycarbonates in material extrusion (MEX). This paper explains the impact of seven generic control parameters (raster deposition angle; orientation angle; layer thickness; infill density; nozzle temperature; bed temperature; and printing speed) on the energy consumption and compressive performance of PC in MEX AM. To meet this goal, a three-level L27 Taguchi experimental design was exploited. Each experimental run included five replicas (compressive specimens after the ASTM D695-02a standard), summating 135 experiments. The printing time and the power consumption were stopwatch-derived, whereas the compressive metrics were obtained by compressive tests. Layer thickness and infill density were ranked the first and second most significant factors in energy consumption. Additionally, the infill density and the orientation angle were proved as the most influential factors on the compressive strength. Lastly, quadratic regression model (QRM) equations for each response metric versus the seven control parameters were determined and evaluated. Hereby, the optimum compromise between energy efficiency and compressive strength is attainable, a tool holding excessive scientific and engineering worth.

Development of Thin-film Sensors for In-process Measurement during Injection Molding

Resource and energy efficiency in manufacturing is of great significance for future developments. Digitization is becoming an important strategy for meeting these challenges and opening up different paths to sustainability. Injection molding as a high-volume process technology offers great opportunities for this approach due to its wide range of applications, producing polymer parts for use in industrial, automotive and consumer use. Due to process variation and individualizations, manufacturing ramp-ups currently produce significant volumes of scrap due to process variations. For improved process parameter setting and validation of simulation models in-process measurement data is mandatory and expected to be a key enabler for automated real-time quality monitoring in future production. One way to measure temperature and melt flow directly in the cavity with contact to the melt is to use thin-film sensors. They are applied to the surface of the tool with high hardness, high wear resistance, low coefficient of friction and high load capacity, are very small and only a few micrometers thick. This research describes the development of thin-film sensors, starting with the tool concept to integrate a sensor insert, followed by the physical operating principle. The developed thin-film layer system and its fabrication process using vacuum-based deposition techniques such as physical vapor deposition and chemical vapor deposition are explained. Combined with microstructuring techniques such as photolithography, the thin-film sensor structures were fabricated and their thermoresistive behavior is characterized. The applied sensor design with a high spatial resolution to detect both temperature and flow front movement is shown. Finally, the developed sensors were experimentally tested in the laboratory. Results of temperature measurements, sensor performance and flow front movement are presented and future potential for applications are discussed.

Technological capabilities of manufacturing heat insulating materials based on crushed cotton plant stems

Today’s priority in construction lies in three primary areas: ecology, energy efficiency and cost-effectiveness. It means that the most important task it to construct environmental, energy-efficient and cost-effective buildings and structures. To erect houses having such characteristics, primary elements of a structure (brick block, etc.) must be made subject to the conditions that provide for the above characteristics. The prevailing trend in construction is the production of environmental, energy-efficient and cost-effective construction materials. This research is intended to develop an energy efficient procedure to make heat-insulating materials based on crushed cotton plant stems. To achieve this goal, the authors have addressed the following issues: describing the process of making a heat-insulating material based on crush cotton plant stems; analysis of energy-efficient characteristics of manufacturing materials based on crushed cotton plant stems; and analysis of environmental friendliness of such a material. The research applies the deductive method based on mathematical modelling. The subject of the research is the procedure to make a material based on crushed cotton plant stems. The primary conclusion and outcome of this research is that papercrete based on crushed cotton plant stems has such advantages as cost-effectiveness, environmental friendliness, energy efficiency and low resource demand in manufacturing and operation. The resulting strength of papercrete allows using it as a heat insulating material to erect low-rise buildings.

Production and energy mode control of a production-inventory system

Energy efficiency in manufacturing can be improved by controlling energy modes and production dynamically. We examine a production-inventory system that can operate in Working, Idle, and Off energy modes with mode-dependent energy costs. There can be a warm-up delay to switch between one mode to another. With random inter-arrival, production and warm-up times, we formulate the problem of determining in which mode the production resource should operate at a given time depending on the state of the system as a stochastic control problem under the long-run average profit criterion considering the sales revenue together with energy, inventory holding and backlog costs. The optimal solution of the problem for the exponential inter-arrival, production and warm-up times is determined by solving the Markov Decision Process with a linear programming approach. The structure of the optimal policy for the exponential case uses two thresholds to switch between the Working and Idle or Working and Off modes. We use the two-threshold policy as an approximate policy to control a system with correlated inter-event times with general distributions. This system is modelled as a Quasi Birth and Death Process and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint production and energy control policy performs better compared to the pure production and energy control policies depending on the system parameters. In summary, we propose a joint energy and production control policy that improves energy efficiency by controlling the energy modes depending on the state of the system.

Nanoparticle-enabled increase of energy efficiency during laser metal additive manufacturing

The low energy efficiency of the laser metal additive manufacturing (AM) process is a potential sustainability concern for large-scale industrial production. Explicit investigation of the energy efficiency for laser melting requires the direct characterization of melt pool dimension and vapor depression, which is very difficult due to the opaque nature of the molten metal. Here we report the direct observation and quantification of effects of the TiC nanoparticles on the vapor depression and melt pool formation during laser powder bed fusion (LPBF) of Al6061 by in-situ high-speed high-energy x-ray imaging. Based on the quantification results, we calculated the laser melting energy efficiency (defined here as the ratio of the energy needed to melt the material to the energy delivered by the laser beam) with and without TiC nanoparticles during LPBF of Al6061. The results show that adding TiC nanoparticles into Al6061 leads to a significant increase of laser melting energy efficiency (114% increase on average, 521% increase under 312 W laser power, 0.4 m/s scan speed). Systematic property measurement, simulation, and x-ray imaging studies enable us, for the first time, to identify that three mechanisms work together to enhance the laser melting energy efficiency: (1) adding TiC nanoparticles increases the absorptivity; (2) adding TiC nanoparticles decreases the thermal conductivity, and (3) adding TiC nanoparticles enables the initiation of vapor depression and multiple reflection at lower laser power (i.e., lowers the laser power threshold for keyholing). The method and mechanisms of using TiC nanoparticles to increase the laser melting energy efficiency during LPBF of Al6061 we reported here may guide the development of feedstock materials for more energy efficient laser metal AM. • Quantified nanoparticle-enabled energy efficiency improvement. • Achieved significant increase of energy efficiency by nanoparticles. • Identified mechanisms of nanoparticle-enabled energy efficiency improvement. • Revealed effects of nanoparticles on vapor depression and melt pool evolution.

Identification of Machine Learning Relevant Energy and Resource Manufacturing Efficiency Levers

Machine learning (ML) can be a valuable tool for discovering opportunities to save energy and resources in manufacturing systems. However, the hype around ML in the context of Industry 4.0 in the past few years has led to blind usage of the approach, occasionally resulting in usage when another analysis approach would be better suited. The research presented here uses a novel matrix approach to address this lack of differentiation of when to best use ML for improving energy and resource efficiency in manufacturing, by systematically identifying situations in which ML is well suited. Seventeen generic levers for improving manufacturing energy and resource efficiency are defined. Next, a generic list of six manufacturing data scenarios for when ML is a good method of choice for analysis is created. This results in a comprehensive matrix in which each lever is evaluated along each ML scenario and given a point, providing a quantitative ML suitability score for each lever. The evaluation is conducted by drawing on past studies demonstrating whether ML is appropriate. Specifically, operation parameter and input material optimization, as well as intelligent maintenance, are the levers that score the highest and are thus identified to be most suitable for machine learning. The majority of the remaining levers is deemed to have low suitability for machine learning. This simple yet informative matrix can be used as a guideline in data-driven manufacturing energy and resource efficiency projects to provide an appraisal on the applicability of ML as the initial analysis tool of choice.

Can precise numbers boost energy efficiency?

The United States has long recognized the potential of improved energy efficiency to profitably reduce energy consumption in manufacturing. The Department of Energy (DOE) has also recognized the potential and initiated several programs to enhance energy efficiency in manufacturing. Despite the attention, studies point out that many energy‐efficiency opportunities remain unexploited. Increasingly, scholars have emphasized that behavioral factors could facilitate the uptake of energy‐efficiency practices. Accordingly, we adopt a behavioral perspective and investigate whether precision (a hitherto unexplored behavioral factor) affects the adoption of energy‐efficiency practices. Specifically, we examine whether the implementation of energy‐efficiency practices depends on whether the costs and savings associated with such practices are presented as precise (e.g., $20,431) or round (e.g., $20,400) numbers. First, we explore the impact of precision with econometric analysis using data on energy‐efficiency recommendations made by the DOE to small‐ and medium‐sized manufacturing firms. Our econometric analysis indicates that energy‐efficiency recommendations with precise cost and precise saving exhibit higher adoption rates as compared to other recommendations. Next, we use individual interactions with managers and laboratory experiments with managers and students to replicate and isolate the precision effects. Then, we elicit the mechanism by which precision affects the adoption of energy‐efficiency initiatives. We find that credibility serves as a key mechanism that drives the precision effect. Our findings suggest that credibility‐enhancing cues such as precision can be leveraged to increase the uptake of energy efficiency and possibly other process improvement practices.