Machining Science And Tool Design Book

Computer Aided Process Planning for Agile Manufacturing Environment

Neelesh K. Jain , Vijay K. Jain , in Agile Manufacturing: The 21st Century Competitive Strategy, 2001

5. CAPP FOR ADVANCED MACHINING PROCESSES

5.1. Objectives

Objective of developing a comprehensive, integrated, intelligent, interactive, and user-friendly CAPP system for non-traditional machining environment can be achieved by fulfilling the following specific sub-objectives:

•: Make a decision about the necessity of using Advanced Machining Processes (AMPs) and to identify those surfaces and manufacturing features, which require use of AMPs. This identification process should use CAD model of the product as input.
•: Rank suitable AMPs according to the different work material properties, shape and operational requirements of the application, and process economy.
•: Optimize the process parameters of the highest ranked AMP for each manufacturing feature.
•: Calculate the process performance parameters like MRR, Tool Wear Rate (TWR), machining time and machining cost.
•: Perform process dependent auxiliary tasks like tool design, machine tool selection, etc.
•: Generate Cutter Location Data (CLD) file and/or NC/CNC-code if required.

5.2. Solution methodology

A CAPP system, developed to fulfill the objectives mentioned above, should use suitable neutral file format of CAD model of the example part as input and generate CLD file or NC/CNC code as output (if applicable), thus integrating itself with CAD and CAM systems. Following methodology can be adopted for developing such a CAPP system:

•

CAD model of the part, created in a suitable CAD software like IDEAS or Pro/Engineer, should be used as input to process selection module.

•

Information required to identify those surfaces and features, which require use of advanced machining processes, can be extracted from CAD model of the part.

•

Information related to material characteristics, operational requirements, and shape requirements can be given interactively through pull down menus, if it is difficult or not possible to extract it from the CAD model data.

•

A comprehensive selection of advanced machining process can be performed at the following three levels using elimination strategy:

: Level 1: Elimination of some processes based on material characteristics like metallic or non-metallic, ductile or brittle, electrically conducting or not, optically reflective or not, etc.
: Level 2: Further elimination can be carried out based on operational requirements and shape requirements of the part. Operational requirements are quantitative in nature and are specified in approximate ranges. They should be considered along with their relative weightage. Shape capabilities of AMPs are qualitative in nature and need to be quantified and computerized. Fuzzy logic can be an appropriate tool for this level of process selection because (1) Key elements in human thinking process are NOT numbers but linguistic terms, (2) Human beings are comfortable making imprecise verbal statements and these can be evaluated using fuzzy theory, (3) Fuzzy logic controllers do not use chain-inference mechanism, i.e. consequent of a decision rule is not applied to the antecedent of another rule, (4) Process capabilities of AMPs are mostly expressed in ranges (quantitative) and linguistic terms (qualitative), (5) In most of applications there is flexibility in requirements.
: Level 3: Short-listed processes can be finally ranked according their economy. It requires economic analysis and data collection regarding initial investment cost, maintenance cost, tooling cost, operating cost, cost of auxiliaries, etc.

•

Non-linear multi-objective optimization of process parameters of the highest ranked AMP should be performed without any major approximations of the objective function(s) and/or constrain(s). Genetic Algorithms (GA) can be a suitable tool because (1) They work with population of points instead of a single point therefore a number of optimal or sub-optimal solutions can be obtained simultaneously, (2) Use coded strings instead of variables directly thus discretizing search space and a discontinuous or discrete function can be handled, (3) Do NOT require objective function to be unimodal, continuous and/or linear, (4) GA operators are probabilistic in nature while traditional optimization methods use deterministic algorithms, (5) Traditional methods cannot be parallelized as they use serial algorithm, (6) Constraints of any nature can be handled by GA, and (6) GA give a set of pareto-optimal solutions for multi-objective optimization.

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Development of an auxiliary module to perform the tasks of selection of machine tool, cutting tool, electrolyte in ECM, dielectric in EDM, etchant in CHM, or any such medium, horn design in USM, nozzle design in AJM, WJM, AWJM, tooling design in AFM, etc. This will involve collection and preparation of related databases,

•

Development of modules to generate CLD file and/or NC/CNC-code,

•

Experimental verification of optimized process variables and CNC/NC program.

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The use of active materials for machining processes: A review

Gyuhae Park , ... Charles R. Farrar , in International Journal of Machine Tools and Manufacture, 2007

New sensing techniques are also being implemented because of recent developments in machining technology and machine tool design. A critical element to achieving manufacturing automation with high productivity and improved precision and control is the ability to measure and estimate the process variables that impact the product's quality with reliable sensing techniques and associated signal processing and analysis technology. In addition, an innovate actuation technology is required in machining process control because of several constraints imposed in the process, including fast dynamic response, a relatively high force with fine resolution, high stiffness and frequency bandwidths, and space restriction.

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Virtual machine tools and virtual machining—A technological review

Aini Abdul Kadir , ... Enrico Hämmerle , in Robotics and Computer-Integrated Manufacturing, 2011

4.1 Web-based virtual machine tool systems

A number of such systems have been developed for different purposes and applications such as assisting machine tool design, performing machine tool simulation, monitoring machining performance and controlling machine tools remotely through a network.

Web-based virtual machine tools (WVMT) was developed [33,34] for the user to experience designing and modelling various kinds of machine tools through the Web. WVMT is composed of a class structure of machine tool configuration, geometric descriptions of mechanical components and the kinematic relationships between the mechanical components. Shinno and Yoshimi [35] classified the mechanical units of a machine tool into nine basic components and several combination patterns. A connectivity graph (Fig. 2) was used to reflect the combination of mechanical units which represents the kinematic chains of the mechanical units. This connectivity graph was applied together with a scene graph that was used to represent a display scheme in Virtual Reality Modelling Language (VRML) so that the display can be maintained between related components. The results are node groups in a connectivity graph as shown in Fig. 2.

A sub-system of WVMT called Web-based CAM (WCAM) was subsequently developed for graphic animation of machining processes in real-time by distributing the data load to a middleware translator, Common Object Request Broker Architecture (CORBA) [36]. Here, CORBA is used as a medium to receive tool-path information from a CAM user, translate the NC part program, and send it back to the user.

In simulating the material removal process using VRML, there are two possible primitives that can be used to define the dynamic movement of geometry. They are the IndexedFaceSet and ElevationGrid nodes (Fig. 3). The ElevationGrid is defined by a set of grids. The grid size can be defined by changing the xDimension and zDimension values. The grid has intersecting points with individual heights. A small surface is then formed through neighbouring intersecting points. In the IndexedFaceSet, the surface is defined by several facets that are interconnected by several vertices. The shape of the IndexedFaceSet changes as the vertices move. In the WCAM system, the IndexedFaceSet geometry node is used.

With a similar intention to the WVMT system, Qin et al. [37] provided a 3D simulation modelling platform for distributed manufacturing. In addition to machine tool modelling, this system also provided some control features via the Internet. It used a concept of drag-and-drop assembly methods and was implemented through a model-component based module system. The system comprised a hierarchical geometric modeller, a behavioural editor and two assemblers. During modelling, designers can combine basic modelling primitives with general extrusions and integrate CAD geometric models into simulation models. Each simulation component model can be visualised and animated in a VRML browser. A prototype was built using Hypertext Mark-up Language (HTML), VRML and TCP/IP as the backbone.

Contrary to the WCAM approach, the machining simulation used in a system developed by Qiu et al. [38] incorporated the ElevationGrid approach. In order to enhance the resemblance of a machining process, ElevationGrid is set with a large dimension and a small grid spacing value. However, updating a large ElevationGrid is expensive as it involves interactions among several layers, including a Java applet, the EAI, a VRML browser and a Web browser. Thus, a group of small ElevationGrid was used to model the workpiece.

The system developed by Hanwu and Yueming [39] incorporated the embedded HTML approach which integrates JavaScript, Java Applets and VRML plug-ins. VRML was used to create the virtual machining environment whereas JavaScript and Java Applets were used to provide a 2D display area for human–machine interactions. The operation interface of the prototype system includes the main operation interface, workpiece set-up or removal screen, reference tool addition or removal screen, CNC machining process for material removal visualisation, machining simulation result showing the finished workpiece and finally G-code editor Interface. In the G-code editor Interface, the user is able to enter the G-code manually and import/export the G-code. Like many other VRML-based systems, the ElevationGrid node was adapted by the material removal simulation algorithm for modelling the dynamics of the material removal process.

In addition to studying the machine tool's functionality, some systems also focus on machining parameter prediction and material removal simulation on the Internet. A prototype called HVMS-II [40] is such a system. It is a hybrid system in that it can carry out both the geometrical and physical simulations simultaneously, e.g. predictions of cutting forces, surface topography and cutter vibration. The Virtual Machining and Measuring Cell (VMMC) was first developed by Yao et al. [41] and later upgraded to have a Web-based environment [42]. This system is also capable of both geometrical and physical simulations. It can predict machining cycle time, cost and inspections such as dimensional errors, position errors and surface roughness.

A different approach based on the two-tier client/server architecture was implemented by Qiu et al. [38] with a platform that can provide dynamic update of workpiece geometry during machining operations. This is also performed under a VRML scene. The server side consists of a Web server and a Java application that relays the data between clients. A client is a Java applet that comprises a Graphical User Interface (GUI), External Client Interface (ECI), External Authoring Interface (EAI), shell, NC core, and the components that update the geometry of a workpiece. The applet can either be in a single-user mode or multi-user mode. In the multi-user mode, one user may function as the 'master' client while others as 'slaves'. The master client receives commands from the user through GUI and passes it to the slave ECI via a messenger. The commands from both master and slave clients are sent to the shell for interpretation. The NC interpreter then analyzes the G-code and sends it to the shape updating algorithm. Through this system, full collaboration can be performed in between clients.

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A systematic literature review on machine tool energy consumption

Nitesh Sihag , Kuldip Singh Sangwan , in Journal of Cleaner Production, 2020

7 Machining energy saving strategies

The need for improving energy and resource efficiencies has led to analyses of energy saving potentials and strategies for machine tools. The common energy losses occurring at machine tool levels have been studied in the literature, and measures to reduce these losses are briefly explained (Schmitt et al., 2011). Long operating time, inefficient loading of electric drives, inefficient components, and poor process design may lead to significant energy waste in machine tools. A large number of energy saving measures for machining operations have been proposed in the literature. Zein et al. (2011) presented a structured approach to categorize the energy saving measures based on energy reduction, reuse and recovery. The functional requirements and corresponding design parameters to fulfill the requirements were defined and mapped in a structured way to provide clarity towards selection of suitable sequence of improvement measures. Duflou et al. (2012) reviewed the energy saving measures at five levels: unit process, multi-machine system, factory, multi-factory, and supply chain levels.

Since the number of studies reporting energy saving measures for machine tools are large, a careful classification and simplified discussion is important for clear understanding. In the present study, the energy saving strategies are classified based on three different phases: design, macro process planning and micro process planning, as discussed below:

7.1 Design phase

It is well evident in literature that the energy efficiency of the machine tools can be improved by incorporating improvements in machine tool design such as design of light weight components, reduction of stand-by energy consumption, use of intelligent control loops, and improvement in structural aspects of machine tools. The energy saving strategies implemented in the design phase are presented in Table 10.

Table 10. Identification of energy saving strategies in design phase.

Energy saving measures	Reference articles
Light weight design of machine tool structure and components	(Fujishima et al., 2014; Kroll et al., 2011; Neugebauer et al., 2011)
Reduction of the weight of the axes	Edem and Mativenga (2016)
Improvement in the component efficiency	(Duflou et al., 2012; Li et al., 2011; Lv et al., 2016)
Improvement in spindle efficiency by using direct drives	Albertelli (2017)
Reduction of spindle energy by reducing the consumption of compressed air, hydraulics and stand-by power.	Abele et al. (2011)
Improved design of hydraulic units by using a variable displacement pump with a hydraulic booster and a variable speed control unit	(Brecher et al., 2017, 2013)
Improved design of feed drive system	Okwudire and Rodgers (2013)
Improved design of cooling system with tunable compressor, pressure controlled circulation pump, optimized chiller, and controlled EC-fan	Brecher et al. (2012)
Demand based control strategy for coolant pump.	Rahäuser et al. (2013)
Replacement of the coolant pump with dust and chip vacuum system	Fujishima et al. (2014)
Configuration of customized machine tools	Gontarz et al. (2015)
Avoiding oversizing of the machine tool components	Eisele et al. (2011)
Waste recovery within a machine tool (e. g. kinetic energy recovery system) and design of integrated or central peripheral components	Duflou et al. (2012)

7.2 Macro process planning phase

The energy efficient strategies at the macro process planning phase are presented in Table 11. It is observed that energy efficient scheduling, management and task scheduling are important energy saving measures for machining processes. The energy efficiency can be improved by integrating the energy efficiency measures at machine tool and production facility levels. It is to be noted here that the present study focuses on energy efficiency analysis at machine tool level, therefore scheduling and production management studies are not included in the present scope.

Table 11. Identification of energy saving strategies in macro process planning phase.

Energy saving measure	Reference article
Machine tool selection optimization	(Avram and Xirouchakis, 2011; Balogun et al., 2015; Bharambe et al., 2015; H. Wang et al., 2018)
Machine tools maintenance and retrofitting	Mert et al. (2015)
Energy efficient scheduling and process planning	Salonitis and Ball (2013).

7.3 Micro process planning phase

The energy efficiency strategies at the micro process planning phase are discussed in this section. A large number of studies considered optimization of cutting parameters as an important strategy to significantly improve the machining performance. It is evident that the optimum parameters should be carefully selected to improve the machining performance while satisfying the constraints related to tool life, machine tool capacity, vibrations, etc. For example, if the cutting speed is close to the natural frequency of the cutting tool, vibration in the machine tool increases resulting in higher cutting power consumption and poor surface finish (De Carvalho et al., 2015). Summary of the key optimization studies for milling and turning processes are provided in Table 12. The studies focusing on other energy saving strategies in the micro process planning phase are summarized in Table 13. Table 13 shows that more studies have been done on tool path, feature sequence and workpiece setting optimization and only few studies are available on the spindle acceleration energy reduction.

Table 12. Summary of cutting parameter optimization studies.

Article	Process	Material	Coolant conditions	Method	Process variables					Process responses
Article			Coolant conditions	Method	v_c	a_p	f	a_e	Other	Cutting energy	SCE	Total energy	Production time	R_a	Tool wear	SEC	CE	EE	Other
Paul et al. (2018)	Turning	AISI 1060 steel	Dry			x	x		Nose radius, Rake angle, Inclination angle		x								Back force
Luan et al. (2018b)	Face Milling	cast iron alloy HTCuCrSn-250	Dry						Tool path		x			x					Cutting time
Cui and Guo (2018)	Turning	AISI 1045 steel	Dry	FEM, Contour plots	x		x				x			x	x
Warsi et al. (2018a)	Turning	Al 6061-T6	Dry	Energy mapping	x	x					x
Warsi et al. (2018b)	Turning	Al 6061-T6	Dry	Energy mapping	x		x				x
Chen et al. (2018)	Milling)	S45C carbon steel	wet	PSO	x	x	x									x			Cost, SPT
Zhang et al. (2018)	Turning			NSGA-II	x	x	x									x			Noise, cost
Bagaber and Yusoff (2018a)	Turning	AISI 316 steel	Dry	Desirability approach	x	x	x			x				x
(Y. C. Wang et al., 2018)	Face milling	Medium carbon (150NHB)	Dry	Evolutionary strategy	x	x	x									x			Cost
Luan et al. (2018c)	Face milling	alloy cast iron- HTCuCrSn-250	Dry	GRA & 3-D surface plots	x		x			x				x	x
(L. Li et al., 2018)	Free form surface milling	Al-6061	Dry	linear weighted summation and adaptive dynamic GA													x		Cutting + air cut energy
Xie et al. (2018)	Turning	C45E4	Dry	NSGAIII	x	x	x		Tool wear		x			x				x
Zhou et al. (2018)	Milling	AISI1045	Dry	GA	x	x	x	x					x			x
Zhao et al. (2018)	Milling	45#	Dry	GRA	x	x	x	x						x		x
Zhang et al. (2017b)	Milling	Steel 16 Mn	Dry	GA	x	x	x	x					x						CSEC
Kumar et al. (2017)	Turning	EN 353 alloy steel	Wet	3 wt criteria, Taguchi-TOPSIS	x	x	x		Nose radius					x				x	AECM, APCM, MRR, PF
Shin et al. (2017)	Milling	Cold finish mild steel 1018	Wet	Online optimization	x	x	x	x				x
Lee et al. (2017)	Milling	SUS stainless steel	Dry	GA	x		x					x	x
Zhang et al. (2017a)	Milling	C45 medium carbon steel	Dry	Weighted coefficients, GA	x	x	x	x				x	x				x
He et al. (2017)	Milling and turning	C45 medium carbon steel	Dry	Weighted coefficients, GA, pareto fonts	x	x	x	x				x	x						Back force
Arriaza et al. (2017)	Milling	Aluminum 7075	Dry	RSM, DA	x	x	x	x		x							x		Cutting time
Wang et al. (2017)	Milling	Ti–6Al–4V alloy		Pareto plot	x	x	x	x		x			x						Tool life
Sangwan and Kant (2017)	Turning	AISI 1045 steel	Dry	RSM, GA		x	x	x											Cutting power
(D. Liu et al., 2017)	Milling	Al6061-T6	Dry	Response surface			x	x		x									Machining accuracy
(C. Li et al., 2017)	MP milling	S45C carbon steel	Dry	AMOPSO	x	x	x		No of passes							x			Cost
Zhong et al. (2016b)	Turning	Carbon steel, ductile iron	Dry		x	x	x									x
Park et al. (2016)	Milling	hardened AISI 4140 steel	Dry	NSGA-II	x		x		Nose radius,Edge radius, Rake angle, Relief angle		x							x
Lu et al. (2016)	MP turning	C-45 carbon steel	Wet	MOBSA	x	x	x		No of passes			x							Machining precision
Bilga et al. (2016)	Turning	EN 353 alloy steel		Taguchi, ANOVA	x	x	x		Nose radius									x	AECM, PF
Albertelli et al. (2016)	Milling	High alloy steel	Wet	Exhaustive enumeration method	x	x		x				x	x
Li et al. (2016b)	Milling	AISI 1045 steel	Dry	Taguchi, RSM, MOPSO	x	x	x	x			x		x
Altıntaş et al. (2016)	Milling	AISI 304 SS	Wet	RSM	x	x	x					x
Li et al. (2016a)	Milling	45#steel	Dry	Tabu search	x	x	x	x					x			x
Camposeco-Negrete et al. (2016) Camposeco-Negrete et al. (2013)	Turning	AISI 1018 steel	Dry and wet	Robust design, MEP	x	x	x					x
Jang et al. (2016)	Milling	SM45C structural steel	Dry, wet, MQL	PSO	x	x	x									x
Tapoglou et al. (2016)	Milling			GA	x	x	x												Cutting power, cutting time
Iqbal et al. (2015)	Grooving	AISI 4340	Dry	Fuzzy methodology	x	x	x		Workpiece hardness	x					x				MRR
Camposeco-Negrete (2015)	Turning	AISI 6061 T6 aluminum	Wet	RSM, DA	x	x	x							x		x
Warsi et al. (2015)	Turning	AISI 6061 T6 aluminum pipe	Dry	Contour plots	x		x				x
Garg et al. (2015)	Milling	Cast ZG35	Dry	Com-MGGP	x	x	x					x
Velchev et al. (2014)	Turning	steel	Dry	Differentiation	x	x	x					x
Wang et al. (2014a)	Turning	Carbon steel #45	Wet	NSGA-II	x	x	x					x		x					Cost
Li et al. (2014)	Rough milling	Aluminum alloy	Dry	GA	x		x						x			x
Li et al. (2014)	Finish milling				x		x							x		x
Arif et al. (2013)	MP Turning	Alloy steel	Dry	NLP	x	x	x		No of passes			x
Camposeco-Negrete (2013)	Turning	AISI 6061 T6 aluminum	Dry	Taguchi S/N	x	x	x					x		x
Yan and Li (2013)	Milling	Medium carbon steel C45	Dry	SQP	x	x	x	x		x				x					MRR
Calvanese et al. (2013)	Milling	Aluminum alloy	Wet	Surface plot	x		x					x							PT
Newman et al. (2012)	Milling	Aluminum alloy 6042	Dry			x	x												Power, MRR
Guo et al. (2012)	Turning Sequential opt	steel	dry		x	x	x							x		x
Mativenga and Rajemi (2011)	Turning	Medium carbon steel	Dry		x	x	x									x
Kant and Sangwan (2014)	Turning	AISI 1045 Steel	Dry	GRA	x	x	x							x					Cutting power
Bagaber and Yusoff (2017)	Turning	Stainless steel 316	Dry	DA	x	x	x			x				x	x
Campatelli et al. (2014)	Milling	AISI 1050 carbon steel	Dry	RSM	x	x	x	x			x					x
Bhushan (2013)	Turning	Al–SiC composite	Dry	RSM, DA	x	x	x		Nose radius										Power, tool life
Hanafi et al. (2012)	Turning	PEEK-CF30	Dry	GRA, MEP	x	x	x							x					Cutting power
Bagaber and Yusoff (2018b)	Turning	AISI 316 steel	Dry	NSGA II	x	x	x					x							Machining cost

Table 13. Identification of energy saving strategies in micro process planning phase.

Energy saving measure	Reference article
Stand-by energy optimization
Better process planning and switching off the machine during long idle times	(Lanz et al., 2010) (Li et al., 2011) (Hu et al., 2012) (Lenz et al., 2017) (Fujishima et al., 2014) (Camposeco-Negrete, 2013) (Lv et al., 2016) (Peng and Xu, 2013)
Component start-stop, setting some components to sleep mode in stand-by state, and retrofitting with a regulatory control	Lenz et al. (2017)
Optimization of operating status of machine tool components and activation of energy saving model	(Lv et al., 2017) (Eberspächer and Verl, 2013) (Eberspächer et al., 2016) (Schlechtendahl et al., 2016)
Reduce spindle acceleration energy
Avoiding unnecessary spindle start-stop, reducing the acceleration time and incorporating lightweight design	Lv et al. (2017)
Synchronizing the spindle acceleration/deceleration with rapid transverse	Mori et al. (2011)
Improvement in coolant conditions
Optimization of coolant flow rate	Denkena et al. (2015)
Minimum quantity lubrication (MQL)/cryogenic cutting conditions	(Lv et al., 2016) (Zhang et al., 2015) (Shokrani et al., 2018)
Reactive power compensation and braking energy storage	Götze et al. (2012)
Feature sequence optimization	(Hu et al., 2017b) (Hu et al., 2018a) (Hu et al., 2017a) (Hu et al., 2018b) (Li et al., 2018) (Wu et al., 2017)
Workpiece setting optimization	(Campatelli et al., 2015) (Edem and Mativenga, 2017a) (Sato et al., 2017) (Xu and Tang, 2016)
Tool path optimization	(Aramcharoen and Mativenga, 2014) (Guo et al., 2015) (Pavanaskar and Mcmains, 2015) (Xu et al., 2016) (Edem and Mativenga, 2017a) (Edem et al., 2017) (Luan et al., 2018b) (L. Li et al., 2018)

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On the sustainability of machining processes. Proposal for a unified framework through the triple bottom-line from an understanding review

M. Estela Peralta Álvarez , ... Francisco Aguayo González , in Journal of Cleaner Production, 2017

5 Proposal for future lines of research and key indicators for sustainable machining

Analysing the review's results, the publications that take into account the three dimensions of sustainability fulfil the following characteristics: (1) they are developed theoretically and are very general, trying to lay down the groundwork to integrate comprehensive sustainable manufacturing, proposing strategies, principles or relevant axioms, and (2) they develop one field in depth with one or two perspectives of the sustainability of the 3E frame, only including a small set of parameters.

This situation is sustained by the main drawback, which implicates taking on an objective of sustainability from the 3E perspective: the growing complexity. In many occasions, due to the stage of technology development, resource availability or insufficient knowledge, the processes are technically impossible. In other cases, the set of requirements or parameters that need to be taken into account to achieve the 3E sustainability are not known. If on the contrary, they are sufficiently defined, to control them in the sustainable machining process, is not feasible for two reasons: (1) the existence of application models developed as qualitative strategies, or (2) because the experts in manufacturing do not have the sufficient experience to implement them. Until now the methodologies focussed on small individual goals and few covered sustainability as a whole. With the current objective set by many authors to develop a unified global index for the sustainable design of manufacturing engineering, taking into account the 3E in a concrete and systematic manner (Gunasekaran and Spalanzani, 2012; Jovane et al., 2009; Pusavec et al., 2010).

As a final contribution and from the review's results, along with the information summarised in Fig. 10, a proposal is offered for the relevant lines of work, to lay the foundations for sustainable research on machining.

In Fig. 11, the knowledge areas, new lines of research and metrics are included. Here the sustainability of the machining process at micro level is approached from four key areas which will allow to set up a sustainable machining process:

(1): Machine tool: it refers to the machine tool's design and structural and functional architecture. The aim is to improve the performance of the specific machining process and adapt the equipment to the characteristics of the operation, the worker and the environment, taking into consideration the results, the human factor and the control parameters.
(2): Worker: where the characteristics, skills, knowledge and information are included, so to perform the task properly and in a context of occupational well-being.
(3): Task: it includes all the activities that should be carried out over a period of time and with a set of requirements to achieve the objective of the process.
(4): Process: it covers all those parameters and indicators that may affect the performance and sustainable efficiency, classified as inputs and outputs.

The proposal includes a set of guidelines and future lines of investigation that covers areas of knowledge dealt with in previous papers, and also covers uncommon areas and unexplored topics within manufacturing, being significant for sustainable machining so to create environmental and social value. In the same figure, it shows how to integrate the identified aspects of sustainability into the 3E work plan.

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A review of fly cutting applied to surface generation in ultra-precision machining

S.J. Zhang , ... G.Q. Zhang , in International Journal of Machine Tools and Manufacture, 2016

3.2 Machine tool

An UPFC machine is a high stiffness and damping system. Its kinematic and dynamic characteristics are the important factors affecting such a high quality surface with nanometric surface roughness and sub-micrometric form error. Montesanti et al. [22,50] adopted high-loop-stiffness and high-resolution measurement and control to design a vertical-axis diamond fly cutting machine for producing flat half-meter scale optics. It reached up to a flatness of 0.1 μm and a roughness of 10 nm. Liang et al. [43] analyzed the kinematic chain and configuration of machine tools to optimize an UPFC machine design under much higher requirements for surface roughness and flatness.

In addition, Liang et al. and Chen et al. [51,52] designed and optimized an UPFC machine for machining a large KDP crystal surface by analyzing its kinematic and dynamic performances to improve its strength and stiffness using finite element modeling and modal testing. For surface waviness induced by machine tool dynamic characteristics, a control method was proposed. Fig. 9 illustrates the design approach of machine tool based on the functional requirement of workpiece. Moreover, Liang et al. [23] proposed a mechanical structure-based design method for UPFC. The method took full account of the effect of dynamic performance of the mechanical control system on surface generation to optimize the hydrostatic slide and the air-spindle structure. Fig. 10 shows the mechanical design method to analyze the effect of kinematics and dynamics on surface generation using analytical modeling and finite element modeling.

Chen et al. [44] used a two-round design method for machine tool design termed "design-simulation-experiment-simulation-redesign-experiment" strategy. It comprises a machine tool structure design, machine tool optimization based on machined surfaces, and simulation models (finite element model, dynamic model and mathematical model) to improve machine tool performances. The main factors for all specifications were determined by the method. In addition, Sun et al. [45] proposed an error budget method for designing and characterizing machine tools taking into account the kinematic and dynamic errors of machine tools and its effect on surface generation. The errors cover spindle motion error, slide motion error, machine-tool-structure induced error, and environment-vibration induced error.

Chen et al. [46] increased dynamic stiffness to reduce its effects on the root mean square gradient, considering that the root mean square gradient was a key parameter for KH₂PO₄ crystal of low frequency wave-front influencing the focusing performance in the inertial confinement, which results from the dynamic performance of the machine tool. Fig. 11 shows the dynamic performance of a machine tool analyzed by finite element modeling and its effects on the measured surfaces and their corresponding root mean square gradient after and before optimization. Chen et al. [47] also proposed a new machine tool design method based on surface generation simulation. The model quantitatively analyzes the effect of straightness, dynamic stiffness, and frequency of machine tool on surface topography. Overall, the system-loop stiffness, dynamics, and control are fully considered to improve the machine tool performances to guarantee the machined surface quality by increasing the machine-tool strength and stiffness and by optimizing the machine-tool design structure.

Kong and Cheung [35] built an integrated kinematics error model, considering the machine kinematic motion errors in UPRM. The model was used to predict the effects of the machine kinematic motion errors on freeform surface errors. It shows that form error majorly originated from the machine kinematic motion errors, i.e. slide errors [21,28]. Chen et al. [53] found that in flat fly cutting of a KDP crystal the spatial position during each cutting path caused a convex surface, which was reduced by a novel adjusting mechanism. It was designed to adjust the axis of the spindle and the workpiece slightly forward to the slide, rather than being completely vertical. Zhang et al. [54] discussed the spindle inclination error in UPRM and developed a novel spindle inclination error identification and compensation method to establish the relationship between the spindle-tilting angle and the tool mark direction. This method is efficient for calibrating the spindle-tilting angle and for reducing the machining error.

Although machine tool kinematic motion errors in UPFC have been fully studied, machine dynamic motion errors have not fully been discussed in depth and little relevant research has been reported. Moreover, assembling errors for machine tools have not been studied sufficiently with regard to their effects on surface generation in UPFC, especially multi-axis UPRM. Much research work should be carried out on the effects of dynamic motion errors and assembling errors on surface generation not only in UPFC but also in all UPM.

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Towards greener machine tools – A review on energy saving strategies and technologies

Hae-Sung Yoon , ... Sung-Hoon Ahn , in Renewable and Sustainable Energy Reviews, 2015

4 Hierarchical approach to the energy saving strategy in machine tools

Though technologies have not been classified on a single device level hierarchy, several studies show technological classifications at the device/unit process level. Table 2 shows energy-efficient and environmentally benign strategies at the unit process level, as arranged by Duflou et al. [35] . They showed the trends of energy saving technologies in 3 categories. The first group was optimization of process control, which involved parameter control, and selective operation of machine tool components at the micro process planning level. The second group was the selection of optimum machine tools and processes, which could be involved in the macro process planning level. The last group was technologies regarding machine tool design, and considered lightweight or efficient components, waste recovery within a machine tool, and integrated peripheral devices.

Table 2. Energy-efficient and environmentally benign strategies at the unit process level (reconstructed from Duflou et al. [35], with kind permission from Elsevier).

Classifications	Detail strategies
Effects of optimized process control	Selective actuation of non-continuously required devices
	Reducing idle production times
	Optimized process parameters
	Energy and resource efficient process modeling and planning
Effects of process/machine tool selection	Process selection
	Optimal machine tool capacity
	Optimal resource consumption
Effects of optimized machine tool design	More efficient machine tool components
	Technological changes
	Waste recovery within a machine tool
	Integrated or central peripherals

Other classifications were suggested in the research by Zein et al. [38]. In this study, energy-efficient machine tool strategies were classified into different 3 categories. The first group was minimization of energy input, which involved minimization of power demand of each component and operation time, and the second group was reuse of energy using energy feedback systems. The last one was recovering energy losses, which covered maximizing energy efficiency, and recovering thermal energy in machine tools. From a wider perspective, Dornfeld showed a strategy for sustainable manufacturing. The strategy consisted of three phases: improving the process, improving the system, and leveraging the process [39].

These classifications could re-arrange energy saving technologies on machine tools in a good manner. However, in this review, we aim to provide hierarchical strategies to single device managers or manufacturers. Hence, on the unit process level, we need to consider a wider range of research on assessments of energy consumption in machine tool design. Moreover, strategies between energy consumption assessment and machine tool design need to be ordered with respect to the level of modification activities.

Six hierarchies are suggested in this study. Fig. 9 shows the suggested hierarchical approach to energy saving strategies for machine tools. The six hierarchies are assessment and modeling, software-based optimization, control technology, cutting improvement, hardware-based optimization, and design for the environment. They are ordered with respect to the level of modification activity or the difficulty of improvement, and also arranged in accordance with the decision-making level. Strategies from assessment and modeling to control technology could be applicable at the micro process planning level, strategies from software-based optimization to cutting improvement could be applicable at the macro process planning level, and strategies from cutting improvement and design for the environment are involved at the machine design level.

The first approach in investigating energy consumption in the machine tool and manufacturing sectors is to measure and analyze the energy consumption of facilities. Specific and quantitative measurement of energy consumption is necessary for applying energy saving technologies, and it enables the most effective application of technologies. Vijayaraghavan and Dornfeld [34] discussed an automated energy-monitoring framework at different levels, as mentioned in Fig. 7. The energy consumption of machine tools does not include direct information or the usual signals in machine tool condition monitoring, such as the cutting force; however, it reflects little information on cutting conditions and machining states [40]. Monitoring and modeling, characterization of energy consumption, and the decomposition of each component are involved at this level, so managers could obtain useful information on the energy consumption of machine tools.

The next approach is software-based optimization, which means process optimization at the software level. Optimization of process parameters, including tool paths, minimization of standby time of machine tools, and scheduling of operations, are included in this level. Using the constructed model or data obtained from the previous level, the energy consumption of machine tools could be easily reduced, without significant hardware changes to the process. The next step is control improvement. This level involves the control of auxiliary devices, and prognosis of energy consumption. Since machine tools have many peripheral devices for cutting fluids and coolants, it also could easily reduce energy consumption at this level.

The fourth approach is the application of cutting technologies. This level involves technologies in assisted machining. Minimum Quantity Lubrication (MQL) is one of the most effective methods, because fluid pumps contribute a lot to the energy consumption of machine tools. Particularly for difficult to cut materials, Laser-Assisted Machining (LAM) helps to save machining time and to increase tool life, thus enabling increase of the potential energy efficiency, though attached lasers consume additional energy resources.

The fifth approach is hardware-based optimization, or optimization at the hardware level. This level includes increasing hardware efficiency and minimizing the leakages, and even replacement of components. The last approach is machine tool design. At this level, strategies of the optimal design of machine tools are included. Lightweight and stiffness design of machine tools could improve the energy efficiency of machine tools both directly and indirectly.

Each approach could be applicable both solely, and combined with others, and has different effects on energy consumption. However, it is very difficult to directly compare the effects, because each approach affects not only different energy elements, but also different levels of energy consumption. Moreover, both direct and indirect improvement of energy efficiency could be achieved. So, in the next section, the state of the art at each individual level is reviewed in more detail.

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The development of modelling tools to improve energy efficiency in manufacturing processes and systems

Victoria Jayne Mawson , Ben Richard Hughes , in Journal of Manufacturing Systems, 2019

5.3 Virtual and augmented reality

Virtual reality (VR) is an environment in which the user is fully immersed in a simulated virtual world, which may or may not hold any resemblance to the real world [54]. Augmented reality (AR) is VR placed over the real world, but with the provision of additional information. An enriched real world is represented rather than one that replaces it [55]. In this way, both technologies are considered linked (Fig. 6).

Use of VR in manufacturing has the potential to reduce time and costs, and lead to increased quality, reduction of design errors and improved efficiency and well-being of operators [56]. It's been used in industry for training, assembly and disassembly of products, manufacturing, design and virtual prototyping [56], as well as improvements in ergonomics of the workplace [57].

Jimeno et al. [55] provided an insight into the use of VR in design and manufacturing processes, and suggested the technology is highly advantageous in computer aided design (CAD) processes due to interactive examination and ability for direct manipulation of models. Designs can be realised before expensive prototyping processes commence, through the use of Virtual Prototyping. Virtual manufacturing involves simulation of a product and manufacturing processes. Shape, residual stresses and durability have been highlighted as common factors for analysis in order to reduce cost of production and minimise waste. However issues were highlighted such as need for extremely high accuracy to ensure that the model is an accurate representation of the physical object, as well as need for reflection of changes to the system in real time.

Pelliccia et al. [58 ] highlighted the need for including energy consumption in the optimisation of machine tool design and manufacturing. The authors present 2 methodologies of energy visualisation of a milling and turning centre using VR, of which all use a discrete colour mapping technique (green for low consumption, yellow for mid consumption and red for high) for different parts of the machine tool. Energy values were determined experimentally from measurements on the machine. The 3D Sankey diagram method mentioned uses VR to add further detail to the common simple 2D Sankey diagram. However power was assumed to be a constant, with energy losses between components neglected. Furthermore, multiple energy flows and a large number of components made the diagram unclear. Similarly, a particle system technique was mentioned along with the use of VR, which allowed for visualisation of dynamic changes of energy consumption over time, as well as highlighting the direction of energy flows ( Fig. 7).

A CAD model of the machine was developed, however CAD geometries are generated without concerning energy consumption, and the methodology to which it was created is unknown, and therefore accuracy of the model could not be determined. The methodologies discussed are based on a fixed framework and are related to a single configuration of machine axes. Development of a dynamic model with multiple modes of operation, along with real time capabilities, as well as multi-machine considerations would allow for a more accurate analysis of the manufacturing process.

Niu et al. [59] used virtual reality technology along with a design for intent method to collect occupancy information in building energy design with the aim of determining the performance gap with respect to energy demand in order to identify the most energy efficient design patterns. Plug loads, backup boilers, ICT infrastructure and lighting were all highlighted as energy related activities requiring set target behaviours. Different design patterns were modelled in Building Information Models (BIM) depending upon setting developed in VR to model different scenarios. Implementation of the methodology into a case study highlighted unexpected problems such as highly unpredictable and erratic behaviour of occupants, with a large number of factor considerations. However it was concluded that the tool allowed designers to determine design patterns which allowed for target occupant behaviour, however multi-criteria decision making methods are required to address conflicting design criteria. Furthermore, quantification of energy demand and energy saved would be beneficial.

For the use of AR in manufacturing, operators can carry out machine operations and also be provided with real time data and process information simultaneously without leaving the work piece [60]. Environmental impacts of the operation can also be viewed in this way, with users able to take immediate action dependent upon feedback. Its use in collision detection on sorting lines [61] and use in robot control [62] allows for visualisation of scenarios and process risk assessment. AR has also be used for factory layout planning and maintenance and is considered a valuable teaching tool, especially in product assembly [62,63]. Nee and Ong [63] discuss the use of AR in aiding operators in CNC machining, with reflection of dynamic tool movements providing the user with real time information on cutting parameters and CNC programs, as well as alarms and errors in machining.

In the context of energy analysis, Herrmann et al. [60] used virtual and augmented reality to determine and illustrate energy flows and environmental impact of manufacturing processes in order to determine energy hotspots and establish improvements. Implemented sensors at the machine tool allowed for consideration of electrical energy, compressed air, coolants and raw materials as inputs along with temperature changes, heat losses and emissions. Data processing using time studies to perform statistical analysis of the material and energy demands along with life cycle assessment was required prior to impact visualisation. Both virtual and augmented reality were used via a touchscreen wall and a smart phone camera display (Fig. 8).

The concept was tested on a grinding machine, implemented with a power meter, compressed air, coolant, pressure and temperature sensor. The full factory floor was presented, with in and outflowing material and energy data displayed along with measured energy data, in terms of energy usage or C0₂ impact. The tool was able to determine the greatest contributor to energy expenditure, but further steps are required in order to test and simulate further possible improvements, along with abilities to alter process parameters and investigation of various scenarios. Furthermore, the ability to monitor multiple machines along an integrated process chain would allow for a more in-depth analysis of energy expenditure of a manufacturing facility.

Current applications of VR and AR in the manufacturing industry is highly swayed towards product design and development [64]. A similar trend was identified in academia, with many studies highlighting the potential of the technology along with applications to production and facility planning [59] and ability for extensive visualization techniques for machine operators [55]. Studies applying VR and AR technology to energy analysis is limited. Pelliccia [58] discuss the use of VR to the visualisation of energy paths of a milling centre, however analysis was limited to electrical energy for one tool, with no consideration of other flows such as compressed air or heat, nor enabled multi-level analysis across multiple machines in a production chain or the manufacturing facility. Herrmann discuss the use of AR for simulating multiple inputs and energy flows of a grinding machine, along with consideration of the full factory floor, however again, analysis was limited to one machine tool, neglecting interdependabilties between multiple machines and the manufacturing process chain, along with the surrounding building environment.

No studies have been found linking concepts of Industry 4.0, such as digital twinning, virtual factories and automated model creation, with analysis of energy or material flows with a multi-level holistic approach. These novel concepts have been focused towards applicability and potential within Industry 4.0, as well as introducing multiple concepts of increased automation and flexibility within smart factories. With these technological advancements, increased need for high cyber security and increased automation results in an increased energy demand, at a cost to industry. Therefore, highlighting energy use of manufacturing processes in the shift towards Industry 4.0 is of upmost importance.

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