Investigation on quantitative assessment of energy consumption and the associated sustainability performance of CNC milling machines

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.The increasing trend of energy prices increment and more and tighter environmental legislation, has led to manufacturing industry and enterprises paying more attention to investigation of more prominent energy/resource efficient production methods, quantitative analysis on energy consumption in manufacturing systems and corresponding timely decision makings. This is further evidenced and supported by the development of latest ISO standards such as ISO 14000, ISO 20140, ISO/TC 39/SC 2, N1760 and ISO 14955 for this cause. Therefore, developing a comprehensive methodological approach for quantitative analysis of energy consumptions and the associated sustainability aspects of CNC machines and operations is the key driver for this research, albeit incorporating its implementation and application perspectives on shopfloor machining operations is the predominant goal as well. The research presented consists of two inter-related parts. The first part discusses the development of the systematic integrated ERWC approach used for the modelling and simulation tenacities in CNC machines and machining operations by taking account of energy consumption (E), resource utilization (R) and waste resulted in production (W), and collectively the resultant carbon footprint (C). The ERWC modelling and analysis is explored in details with support of the MATLAB-based simulations developed and relevant case carried out. The second part of the research is focused on evaluation of the methodological approach by design of a special testing workpiece and the well-designed CNC machining experiments. The experiments are carried out on the Bridgeport 3-axis CNC milling machine, so the maximum output power of the machine can be determined using the designed testing workpiece and appropriate testing procedures. In the experiments, the milling machine is opted with the clamped power logger for power data-acquisition. The results are used to further validate the model, approach and simulations developed. The contributions to knowledge are largely raised from developing the integrated ERWC modelling approach, innovative design of the testing workpiece, and their implementation perspectives on the 3-axis CNC milling machine, as supported with original research thoughts and exploration

Sustainability-Based Expert System for Additive Manufacturing and CNC Machining

The development of technologies which enable resource efficient production is of paramount importance for the continued advancement of the manufacturing industry. In order to ensure a sustainable and clean energy future, manufacturers should be able to contrast and validate existing manufacturing technologies on a sustainability basis. In the post COVID-19 era of enterprise management, the use of artificial intelligence to simulate human expert decision making abilities will open new doors to achieving heightened levels of productivity and efficiency. The introduction of innovative technologies such as CNC machining and 3D printing to production systems has redefined the manufacturing landscape in a way that has compelled users to investigate into their sustainability. For the purposes of this study, cost effectiveness, energy and auxiliary material usage efficiency have been considered to be key indicators of manufacturing process sustainability. The objective of this research study is to develop a set of expert systems which can aid metal manufacturing facilities in selecting Binder Jetting, Direct Metal Laser Sintering or CNC Machining based on viable product, process, system parameters and inherent sustainability aspects. The expert systems have been developed using the knowledge automation software, Exsys CorvidÒ. Comprehensive knowledge bases pertaining to the objectives of each expert system have been created using literature reviews and communications with manufacturing experts. An interactive environment which mimics the expertise of a human expert has been developed by the application of suitable logical rules and backward chaining. The programs have been verified by analyzing and comparing the sustainability impacts of Binder Jetting and CNC Machining during fabrication of a stainless steel 316L component. According to the results of the study, Binder Jetting is deemed to be characterized by more favorable indicators of sustainability in comparison to CNC Machining, for fabrication of components feasible for each technology

Investigation into alternative cooling methods for achieving environmentally friendly machining process

© 2015 The Authors. Published by Elsevier B.V. The machining of metals has traditionally involved the use of large quantities of water and oils for dissipating the cutting tool temperature, improving the surface finish of parts and increasing tool life. Invariably, the cutting fluid has become contaminated with use, has required being environmentally disposed and has accounted for approximately 17% of the total production cost of parts. A Streamline Life Cycle Assessment (SLCA) of machining of parts has been carried out to investigate the environmental and energy saving benefits associated with the replacement of traditional cooling method, with Minimum Quantities of Liquid (MQL) combined with cold compressed air

Low carbon manufacturing: Fundamentals, methodology and application case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The requirement and awareness of the carbon emissions reduction in several scales and application of sustainable manufacturing have been now critically reviewed as important manufacturing trends in the 21st century. The key requirements for carbon emissions reduction in this context are energy efficiency, resource utilization, waste minimization and even the reduction of total carbon footprint. The recent approaches tend to only analyse and evaluate carbon emission contents of interested engineering systems. However, a systematic approach based on strategic decision making has not been officially defined with no standards or guidelines further formulated yet. The above requirements demand a fundamentally new approach to future applications of sustainable low carbon manufacturing. Energy and resource efficiencies and effectiveness based low carbon manufacturing (EREEbased LCM) is thus proposed in this research. The proposed EREE-based LCM is able to provide the systematic approach for integrating three key elements (energy efficiency, resource utilization and waste minimization) and taking account of them comprehensively in a scientific manner. The proposed approach demonstrates the solution for reducing carbon emissions in manufacturing systems at both the machine and shop floor levels. An integrated framework has been developed to demonstrate the feasible approach to achieve effective EREE-based LCM at different manufacturing levels including machine, shop floor, enterprise and supply chains. The framework is established in the matrix form with appropriate tools and methodologies related to the three keys elements at each manufacturing level. The theoretical model for EREE-based LCM is also presented, which consists of three essential elements including carbon dioxide emissions evaluation, an optimization method and waste reduction methodology. The preliminary experiment and simulations are carried out to evaluate the proposed concept. The modelling of EREE-based LCM has been developed for both the machine and shop floor levels. At the machine level, the modelling consists of the simulation of energy consumption due to the effect of machining set-up, the optimization model and waste minimization related to the optimized machining set-up. The simulation is established using sugeno type fuzzy logic. The learning method uses on experimental data (cutting trials) while the optimization model is created using mamdani type fuzzy logic with grey relational grade technique. At the shop floor level, the modelling is designed dependent on the cooperation with machine level modelling. The determination of the work assignment including machining set-up depends on fuzzy integer linear programming for several objectives with the evaluation of energy consumption data from machine level modelling. The simulation method is applied as the part of shop floor level modelling in order to maximize resource utilization and minimize undesired waste. The output from the shop floor level modelling is machine production a planning with preventive plan that can minimize the total carbon footprint. The axiomatic design theory has been applied to generate the comprehensive conceptual model E-R-W-C (energy, resource, waste and carbon footprint) of EREE-based LCM as a generic perspective of the systematic modelling. The implementation of EREE-based LCM on both the machine and shop floor levels are demonstrated using MATLAB toolbox and ProModel based simulation. The proposed concept, framework and modelling have been further evaluated and validated through case studies and experimental results.This work is financially supported by The Royal Thai Government

A review

Funding Information: Radu Godina acknowledges Fundação para a Ciência e a Tecnologia ( FCT - MCTES) for its financial support via the project UIDP/00667/2020 and UIDB/00667/2020 (UNIDEMI). JPO acknowledges funding by national funds from FCT - Fundação para a Ciência e a Tecnologia, I.P., in the scope of the projects LA/P/0037/2020 , UIDP/50025/2020 and UIDB/50025/2020 of the Associate Laboratory Institute of Nanostructures, Nanomodelling and Nanofabrication – i3N. This activity has received funding from the European Institute of Innovation and Technology (EIT) – Project Smart WAAM: Microstructural Engineering and Integrated Non-Destructive Testing . This body of the European Union receives support from the European Union's Horizon 2020 research and innovation programme. Publisher Copyright: © 2023 The AuthorsGrowing consciousness regarding the environmental impacts of additive manufacturing (AM) processes has led to research focusing on quantifying their environmental impacts using Life Cycle Assessment (LCA) methodology. The main objective of this paper is to review the state of the art of the existing LCA studies of AM processes. In this paper, a systematic literature review is carried out where a total of 77 papers focusing on LCA, including social-Life Cycle Assessment (S-LCA), are analyzed. Accordingly, the application of LCA methodology to different AM technologies was studied and different research themes such as the goal and scope of LCA studies, life cycle inventory data for different AM technologies, AM part quality and mechanical properties, the environmental, economic, and social performances of various AM technologies, and factors affecting AM´s sustainability potential were analyzed. Based on the critical analysis of the existing research, five major shortcomings of the existing research are realized: (i) some AM technologies are under studied; (ii) more focus only on the environmental sustainability dimension of AM, neglecting its economic and social dimensions; (iii) exclusion of AM pat quality and its mechanical performance from the sustainability assessment; (iv) not enough focus on the life cycle stages after product manufacture by AM; (v) effect of different product variables on AM´s sustainability not studied extensively. Lastly, based on these shortcomings realized, the following research directions for future works are suggested: (i) inclusion of new AM materials and technologies; (ii) transition to a triple-bottom-line sustainability assessment considering environmental, economic, and social dimensions of AM; (iii) extending the scope of LCA studies to post-manufacture stages of AM products; (iv) development of predictive environmental impact and cost models; (v) integration of quality and mechanical characterization with sustainability assessment of AM technologies.publishersversionpublishe

Environmental dimensions of additive manufacturing: mapping application domains and their environmental implications

Additive manufacturing (AM) proposes a novel paradigm for engineering design and manufacturing, which has profound economic, environmental, and security implications. The design freedom offered by this category of manufacturing processes and its ability to locally print almost each designable object will have important repercussions across society. While AM applications are progressing from rapid prototyping to the production of end-use products, the environmental dimensions and related impacts of these evolving manufacturing processes have yet to be extensively examined. Only limited quantitative data are available on how AM manufactured products compare to conventionally manufactured ones in terms of energy and material consumption, transportation costs, pollution and waste, health and safety issues, as well as other environmental impacts over their full lifetime. Reported research indicates that the specific energy of current AM systems is 1 to 2 orders of magnitude higher compared to that of conventional manufacturing processes. However, only part of the AM process taxonomy is yet documented in terms of its environmental performance, and most life cycle inventory (LCI) efforts mainly focus on energy consumption. From an environmental perspective, AM manufactured parts can be beneficial for very small batches, or in cases where AM-based redesigns offer substantial functional advantages during the product use phase (e.g., lightweight part designs and part remanufacturing). Important pending research questions include the LCI of AM feedstock production, supply-chain consequences, and health and safety issues relating to AM

A data-driven approach design for carbon emission prediction of machining

The issue of carbon emission reduction for manufacturing industry attracts increasing attention. As a major contributor in the manufacturing industry, machining has generated large amounts of carbon emissions through the resource consumption, energy consumption, and waste disposal. The carbon emission prediction of machining is a priori technology for its reduction, and has been established as one of the most crucial research targets. The purpose of this study is to design a carbon emission prediction model of machining through a data-driven approach. First of all, the multiple sources and impact factors of carbon emissions in machining are studied, and the relationship between these factors is also studied to describe the carbon emissions. Then, a data-driven approach is designed to predict the carbon emission of machining, which consists of data collection and preprocessing, feature extraction, prediction model establishment and model validation. The ridge regression, BP neural network based on Genetic Algorithm (GA-BP), root means square error (RMSE) and mean relative percentage error (MPAE) are respectively employed to fulfill the above tasks in the design approach. Finally, an experimental study of a real turning machining is proposed to verify the feasibility and merits of the designed approach