Therblig-embedded value stream mapping method for lean energy machining

To improve energy efficiency, extensive studies have focused on the cutting parameters optimization in the machining process. Actually, non-cutting activities (NCA) occur frequently during machining and this is a promising way to save energy through optimizing NCA without changing the cutting parameters. However, it is difficult for the existing methods to accurately determine and reduce the energy wastes (EW) in NCA. To fill this gap, a novel Therblig-embedded Value Stream Mapping (TVSM) method is proposed to improve the energy transparency and clearly show and reduce the EW in NCA. The Future-State-Map (FSM) of TVSM can be built by minimizing non-cutting activities and Therbligs. By implementing the FSM, time and energy efficiencies can be improved without decreasing the machining quality, which is consistent with the goal of lean energy machining. The method is validated by a machining case study, the results show that the total energy is reduced by 7.65%, and the time efficiency of the value-added activities is improved by 8.12% , and the energy efficiency of value-added activities and Therbligs are raised by 4.95% and 1.58%, respectively. This approach can be applied to reduce the EW of NCA, to support designers to design high energy efficiency machining processes during process planning

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

Design of an instrumented smart cutting tool and its implementation and application perspectives

This paper presents an innovative design of a smart cutting tool, using two surface acoustic wave (SAW) strain sensors mounted onto the top and the side surface of the tool shank respectively, and its implementation and application perspectives. This surface acoustic wave-based smart cutting tool is capable of measuring the cutting force and the feed force in a real machining environment, after a calibration process under known cutting conditions. A hybrid dissimilar workpiece is then machined using the SAW-based smart cutting tool. The hybrid dissimilar material is made of two different materials, NiCu alloy (Monel) and steel, welded together to form a single bar; this can be used to simulate an abrupt change in material properties. The property transition zone is successfully detected by the tool; the sensor feedback can then be used to initiate a change in the machining parameters to compensate for the altered material properties.The UK Technology Strategy Board (TSB) for supporting this research (SEEM Project, contract No. BD266E

Multi-objective optimization model of cutting parameters for a sustainable multi-pass turning process

The turning process involves the linear removal of material from the work-piece and requires a relatively high amount of energy. The high energy consumption of the machining process increases carbon emissions, which affects the environment. Moreover, production costs will rise as the cost of energy rises. Energy savings during the machining process are crucial for achieving sustainable manufacturing. In order to determine and optimize the cutting parameters, this study creates a multi-pass turning processes optimi¬zation model. It considers cutting speeds, feed rates, and depth of cut. In this study, the model uses multi-objective optimization by incorporating three objective functions: processing time, energy consumption and product¬ion costs. OptQuest completed the proposed model in Oracle Crystal Ball software, then normalized and weighted the sum. Ordering preferences, the Multi-Objective Optimization based on Ratio Analysis (MOORA) approach is utilized. It ranks items based on their higher priority values. This paper provides a numerical example to demonstrate the application of an optimi¬zation model. Based on the preference order ranking results, the optimal values for three objective functions are as follows: total processing time of 4.953 min, the total energy consumption of 5.434 MJ, and total production cost of 395.21$

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

A LCA and LCC analysis of pure subtractive manufacturing, wire arc additive manufacturing, and selective laser melting approaches

Funding Information: João Pedro Oliveira 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. 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 Framework Programme . Publisher Copyright: © 2023 The AuthorsThe development of sustainable manufacturing solutions is gaining attention in the manufacturing sector due to increased awareness about climate change and the formulation of stricter environmental legislation. Sustainable manufacturing involves the development of solutions that are environmentally friendly and cost-effective at the same time. Considering the opportunities and limitations of metal subtractive and additive manufacturing approaches from a sustainability perspective, this study aims to compare the environmental impact and production costs associated with the manufacture of a marine propeller using pure subtractive CNC milling along with additive Wire arc additive manufacturing (WAAM) and Selective Laser Melting (SLM) approaches. Life Cycle Assessment (LCA) and Life Cycle Costing (LCC) are used to quantify the environmental and economic impacts, respectively for each manufacturing approach. Based on the LCA and LCC models formulated, and the input data collected, the WAAM approach is observed to be the most environmentally and cost-efficient approach for the marine propeller analyzed. WAAM shows an environmental impact about 2.5 times and 3.4 times lower than pure CNC milling and SLM approaches, respectively mainly due to its better material and energy efficiencies. The effect of key variables on the environmental impact and production cost such as raw material, electricity, and post-processing parameters like a material allowance for finish machining and cutting velocity is also studied to suggest the parameters ensuring sustainable performance for a particular approach. WAAM is seen to be the most economical and ecological option for a post-processing material allowance under 4 mm and the finish machining velocities below 96 m/min. Additionally, an uncertainty assessment using the Monte Carlo analysis method is also performed to give a probabilistic range of environmental impacts and production costs considering the input data uncertainties for each approach. The methodology used in this study can be applied to other additive manufacturing processes. This study can be of potential help to AM practitioners in decision-making on selecting the most sustainable approach for manufacturing their products.publishersversionpublishe

Characterization of beech wood pulp towards sustainable rapid prototyping

Wood has several advantages that are transferable to various derivates allowing the introduction of a sustainable material into the product lifecycle. The objective of this paper is to apply a design for manufacturing approach based on wood flour rapid prototyping, while associating the requirements of the ‘mass customisation’ in the implementation of a customised product. New collaborative software allows consumers to be involved in the design process. Prototyping processes allow direct manufacturing of products

Sustainable manufacturing and parametric analysis of mild steel grade 60 by deploying CNC milling machine and Taguchi method

Design and manufacturing are the key steps in the sustainable manufacturing of any product to be produced. Within the perspective of injection molds production, increased competitiveness and repeated changes in the design require a complete optimized manufacturing process. Local and minor improvements in the milling process do not generally lead to an optimized manufacturing process. The goal of the new geometry and parametric analysis of the mould is to reduce the quality issues in mild steel grade 60. In this explicit research, the surface roughness (smoothness) of indigenously produced injection moulds in the local market in Pakistan is investigated. The CNC milling machine (five-axis) is used for the manufacturing of an injection mould, and the Taguchi method of the design of the experiment is applied for parameters optimization. Hence, the overall process is assisted in balancing the milling machine parameters to trim down the surface roughness issue in mild steel moulds and increase their sustainability. The spindle speed (rpm), the depth of cut (mm), and the feed rate (mm/rev) are considered as input variables for process optimization, and the experiments are performed on mild steel grade 60. It is deduced that the combination of a spindle speed of 800 rpm, feed rate of 10 mm/rev and depth of cut of 0.5 mm is the best case in case of minimum surface roughness, which leads to sustainable products. It is also deduced from ANOVA, that the spindle speed is a factor that affects the surface roughness of mild steel products, while the feed rate turns out to be insignificant

Drilling of Glass Fiber Reinforced Polymer (GFRP) Composites: Multi Response Optimization Using Grey Relation Analysis with Taguchi’s Method

Nowadays, GFRP (Glass Fiber Reinforced Polymer) composites are widely used in manufacturing industries specially aircraft, aerospace, and automobile industries due to their excellent mechanical and thermal properties such as more specific strength, better specific modulus of elasticity, high damping factor or damping capacity, better resistance to corrosion, effective fatigue resistance, low thermal expansion coefficient. Hence, it is necessary to understand the machinability behavior of these composites. Drilling is widely used to assemble the components in aforementioned industries. But machining of these composites is dissimilar to conventional metals due to their isotropic nature and in-homogeneity. Major drawbacks of these composites in machining are fiber pull out, delaminating and burring of fibers. So, appropriate selection of process parameters is an important concern in machining of GFRP composites. This work mainly focuses on assessing the effects of process parameters i.e. spindle speed, feed and drill diameter on thrust, torque, delamination factor (both at entry and exit) in drilling of GFRP composites using TiAlN coated drill bit. The study also utilizes the Grey methodology coupled with Taguchi L16 OA to determine the optimal parametric combination