Energy efficiency evaluation for machining systems through virtual part

Accepted Manuscript Energy efficiency evaluation for machining systems through virtual part

Junbo Tuo, Fei Liu, Peiji Liu, Hua Zhang, Wei Cai PII:

S0360-5442(18)31161-7

DOI:

10.1016/j.energy.2018.06.096

Reference:

EGY 13138

To appear in:

Energy

Received Date:

22 December 2017

Accepted Date:

15 June 2018

Please cite this article as: Junbo Tuo, Fei Liu, Peiji Liu, Hua Zhang, Wei Cai, Energy efficiency evaluation for machining systems through virtual part, Energy (2018), doi: 10.1016/j.energy. 2018.06.096

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Energy efficiency evaluation for machining

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systems through virtual part

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Junbo Tuoa, Fei Liua,*, Peiji Liua, Hua Zhangb, Wei Caia

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aState

Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China

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bCollege

of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China

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Abstract: Energy prices, environmental concerns, carbon dioxide emissions, and economic matters are driving

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factors for research on reducing machining system energy consumption. Energy efficiency evaluation for

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machining systems is an effective management strategy for reducing the energy consumption and improving the

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energy efficiency of machining systems. This paper proposes a methodology called virtual part method to evaluate

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the energy efficiency of machining systems, and the proposed method overcomes the deficiencies or limitations of

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major existing methods (such as the entitative part method) through equivalence and virtualization of all possible

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machining system parts that may be manufactured in future. Based on an analysis of the energy compositions and

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characteristics of the proposed virtual part, an energy efficiency evaluation for machining systems through virtual

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part is conducted in three steps: 1) selection of evaluation indexes; 2) corresponding data collection for calculating

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indexes; and 3) development of energy efficiency evaluation system. Its application in a machine tool suggests that

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the proposed method is more accurate than the existing ones and contributes to energy-saving activities including

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the development of energy efficiency standards, design of energy-efficient machining systems, and reform of old

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machining systems.

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Keywords: Energy efficiency; Evaluation; Machining systems; Manufacturing industry.

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1 Introduction

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Energy is considered as the most fundamental resource for future economic growth and prosperity, and is

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believed to be a reference for determining future global competitiveness. Manufacturing accounts for 37% of

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primary energy use worldwide [1], and in Europe, for 40% in terms of only electricity consumption [2]. Machining

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systems are the major production and energy-consuming equipment widely used in the manufacturing industry [3],

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and currently require increasing energy owing to their large numbers and rapid growth [4]. For example, according

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to statistics obtained from the U.S. energy information administration, the electricity consumption of machining

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systems accounts for over 50% of the total manufacturing electricity consumption [5]. Moreover, energy efficiency

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during the machining processes is low, at generally less than 30% [6], which means that there is a significant

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potential for efficiency improvement.

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With the aim of reducing energy consumption and improving energy efficiency, research on the energy

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efficiency of machining systems has attracted considerable attention from the perspective of academic researchers

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and administrative organizations. For instance, Kara et al. [7] presented an empirical model to characterize the

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relationship between energy consumption and process variables for material removal processes. Herrmann et al.

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[8] introduced an energy-oriented simulation model for planning manufacturing systems, which considers the 1

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dynamic interactions between the different processes and auxiliary equipment (e.g., compressed air generation). To

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explore possible strategies for energy consumption reduction in machining processes, a systematic overview of

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energy consumption classification and prediction methods for different levels of energy consumption has been

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provided by Zhao et al. [9]. Kant et al. [10] have conducted an experimental study to investigate the capability of

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artificial neural network model for predicting the value of energy consumption, indicating that artificial neural

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network could be used for energy efficiency evaluation in machining systems. With regard to administrative

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organizations, the European Commission lists the critical products that need to follow eco-design measures, as

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defined by the directive 2009/125/EC [11] and the special market regulations in terms of environmental labels, as

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stated in directive 2010/30/EU [12] and standardized by DIN EN ISO 14020 [13]. Machine tools are on the list of

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critical products. The G7 Summit Declaration of June 2015 launched the “sustainable economic growth” initiative,

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with the reduction in energy consumption of machining systems as an action for the topic “sustainable growth in

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industrialized development”.

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Energy efficiency evaluation is usually considered as a precondition for reducing energy consumption and

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improving energy efficiency, because it can provide reference data for energy-saving programs such as energy

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efficiency standards, energy labels, and other incentive schemes [14]. Numerous methods for energy efficiency

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evaluation have been applied to the energy resources [15], chemical [16], building [17], environment [18], and

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coal [19], among others. Regarding manufacturing, the United States Department of Energy has set up Industrial

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Assessment Centers (IACs) to evaluate the energy efficiency of manufacturing processes and equipment in order

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to reduce energy consumption, minimize waste, and decrease carbon dioxide emissions. In this regard, more than

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18,241 assessments have been conducted and an amount of $136,777 has been saved yearly [20]. The Japanese

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Standards Association has developed the JIS TS B 0024 series, including a test method for electric power, to

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describe and evaluate the energy consumption of machine tools such as machining centres [21], numerically

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controlled turning machines and turning centres [22], cylindrical grinding machines [23]. The International

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Organization for Standardization (ISO), through its standardization body (ISO/TC 39 WG 12), currently works on

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the ISO 14955 series in order to enable the assessment of the energy-efficient design of machine tools [24]. Both

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JIS TS B 0024 series and ISO 14955 series focus on measure or analyze of energy consumption. However,

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evaluation and comparison of energy efficiency for machine tools are not detailed.

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Machine tools are complex mechatronic systems that are built up by components such as fans, cooling units,

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spindle drives, etc., each contributing to the total electrical power level. Li et al. [25] proposed a component-based

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description of the power demand to compare different machine tools. Their study suggests that improvements of

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hydraulic, cooling, and lubrication systems can save up to 58% of fixed energy consumption. Schudeleit et al. [26]

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developed a metric to quantify the energy efficiency of machine tool design suitable for the standardized

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evaluation of machine tools by taking into account each machine tool components' efficiency and the need-

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oriented utilization in combination with the other components. Three key figures, efficiency, consistency, and

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sufficiency indexes, and a general metric was employed to complete the comparison and evaluation. Besides the

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component benchmark method, the measurement of a machine tool energy consumption for a predefined

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utilization profile (e.g., defined periods of time in off, standby, and ready states) has also been employed to

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describe and compare its energy efficiency. Kaufeld [27] introduced an energy efficiency indicator for comparison

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of machine tools based on a reference process. Schlosser et al. [28] chose a static approach by defining energy

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blocks for each operational state, which are summed in order to approximate the total energy consumption of a

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reference process. However, the existing study [29] has suggested that the energy consumption of a machining

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process is determined by both its intrinsic characteristics and machining tasks. It means that it is difficult to apply

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these component-benchmark or reference-process methods, which do not take into consideration the effects of the

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parts manufactured on energy consumption, to evaluate the energy efficiency of machine tools in a relatively 2

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precise way.

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Some approaches based on the machining part, including the specific energy consumption and reference

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entitative part methods, have also been used to assess the energy efficiency of machine tools. The specific energy

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consumption method is a mapping approach that aims at the empirical correlation between the machine tool power

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consumption and a physical output variable, e.g., material removal rate of a grinding machine. Gutowski et al. [6]

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suggested the assessment of the power consumption P as a function of the idle power, material removal rate

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(MRR), and a constant from the physics of the process. This function can be rearranged for computation of the

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specific electrical exergy, which is equivalent to the specific energy consumption (SEC) per removed material

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volume and is inversely proportional to MRR. Kara et al. [7] analyzed empirical data for SEC using ANOVA and

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regression analyses, and obtained a prediction model that is equal to the rearranged equation for the specific

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electrical exergy mentioned above. The reference entitative part method refers to the energy consumption needed

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by a machine tool in order to manufacture a predefined part. A recent example of a part used by the grinding

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machine manufacturer Fritz Studer AG is given in Gontarz et al. [30]. Bhinge et al. [31] have also presented a test

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part, which involves face milling, drilling pocketing, and slotting to generate energy data for simulating and

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evaluating the energy demands of machining systems. In their study, the definition of a test piece and the

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comparison regarding energy efficiency are limited to machine tools with similar specifications. In order to

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compare various machine tools, Behrendt et al. [32] presented a detailed description of different test procedures

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based on standardized parts, which consisted of 17 different features and three sizes, and Lv et al. [33] conducted

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an experimental study on the energy characteristics using different machining systems and parts in order to explore

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a perfect part. In fact, machining manufacture is a typical discrete manufacturing process. The machining tasks of

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most machine tools are various and uncertain. It is hard to be agreed on for standardization of predefined part, and

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this problem limited the application of existing part-based approaches.

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As previously mentioned, evaluation of energy efficiency plays a crucial role in reducing energy consumption

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and improving energy efficiency in machining manufacture. The existing major evaluation methods for machining

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systems are hindered by losing sight of the influence of parts on energy consumption (e.g., the component

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benchmark method) or the design methodology for the reference part (e.g., the reference entitative part method). It

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can be concluded that if a part could represent all possible parts that may be manufactured by machining systems

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from the viewpoint of energy consumption, this problem would be solved effectively. This paper proposes a new

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concept “virtual part” (VP) to represent the ideal part, through the equivalence and virtualization of all possible

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parts that may be manufactured by machining systems in future. A method based on the VP is also proposed to

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evaluate the energy efficiency of machining systems, and it has the advantage that both the intrinsic characteristics

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and machining tasks are taken into account, and the limitations existing in the reference entitative part, such as

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cutting parameters and geometrical shape, are overcome.

115 Nomenclature 𝛼

Additional load loss coefficient.

𝐸𝑎

Total Additional load loss.

𝐸𝑐

Total cutting energy.

𝐸𝑠𝑏

Total standby energy.

𝐸𝑠𝑡

Total starting energy.

𝐸𝑠𝑡(𝑛𝑖)

Starting energy at spindle revolving speed 𝑛𝑖.

𝐸𝑢

Total idling energy.

𝐿

𝑀

𝑃𝑙𝑑(𝑛𝑖),𝑃𝑙𝑑(𝑛𝑖), 𝐻

𝑃𝑙𝑑(𝑛𝑖)

Light load power, medium-sized load power, heavy load power respectively at spindle revolving speed 𝑛𝑖.

𝑃𝑛, 𝑃𝑛 + 1 𝑃𝑠𝑏 𝑃𝑢

3

Real-time average power in the n - t ℎ and n + 1 - tℎ interval time. Power demand in standby. Idling power of the machine tool.

ACCEPTED MANUSCRIPT 𝑓(𝑛𝑖)

Usage probability of spindle revolving speed 𝑛𝑖.

𝑠

𝑃𝑢 𝑠

𝑃𝑢(𝑛𝑖) 𝐹𝑐 𝑛𝑖 𝑓𝐶,𝐿,

𝑛𝑖 𝑓𝐶,𝐻

Occurring probabilities of the light load, Medium-sized load, and heavy load, respectively.

Idling power of the spindle at revolving speed 𝑛𝑖.

Cutting force. 𝑛𝑖 𝑓𝐶,𝑀,

Idling power of the spindle.

𝑇

𝑛𝑖

Rotation time at spindle revolving speed 𝑛𝑖.

𝑛𝑖 𝑛𝑖 𝑛𝑖 𝑇𝐶,𝐿,𝑇𝐶,𝑀,𝑇𝐶,𝐻

Cutting time of light load, medium-

𝑛1, 𝑛𝑘,𝑛𝑖,𝑛

Spindle revolving speed.

sized load, and high load

𝑃in

Input power

respectively.

𝑃𝑎 𝑛𝑖 𝑃𝑎,𝐿,

Additional power loss. 𝑛𝑖 𝑃𝑎,𝑀,

𝑛𝑖 𝑃𝑎,𝐻

𝑃𝑐 𝑛𝑖 𝑃𝐶,𝐿,

Additional power loss

𝑛𝑖

representations for light load, medium-sized load, and heavy

𝑣

Cutting speed.

load respectively.

CEC

Comprehensive energy

Cutting power. 𝑛𝑖 𝑃𝐶,𝑀,

𝑠 𝑃𝑒

𝑛𝑖 𝑃𝐶,𝐻

speed 𝑛𝑖.

consumption.

Light, medium-sized, and heavy

CEU

Comprehensive energy utilization.

cutting power representations

ECD

Energy-correlated data.

respectively.

EUD

Energy-uncorrelated data.

SEC

Specific energy consumption.

SLSR

Step-less revolving speed

Rating power of the main drive motor.

𝑃𝑙𝑑(𝑛𝑖)

Load time at spindle revolving

𝑇𝑙𝑑

regulation.

Maximum load power at spindle

SSR

Step-revolving speed-regulation.

revolving speed 𝑛𝑖.

VP

Virtual part.

116 117

2 Concept and characteristics of virtual part

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The VP is proposed only from the perspective of energy consumption and related influential factors and is

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designed based on the concept of “energy consumption equivalent part.” If a part consumes the same amount of

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energy as other parts or its power demand curve is approximately that of other parts, the part can be taken as the

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energy consumption equivalent part of the others. For example, in Fig. 1, part 3 can be referred to as the energy

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consumption equivalent part of parts 1 and 2 as the power demand curve of part 3 approximates the combination of

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the power demand curve of part 1and part 2. It would be highly advantageous if an energy consumption equivalent

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part could be designed to represent all possible parts produced by the machining system in the future, because this

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equivalent part could reflect all of the energy consumption characteristics of the machining system. In this study,

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the VP presents to represent the desired equivalent part.

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Fig. 1. Schematic of power demand profile of parts 1, 2, and 3.

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Generally, the operating processes for manufacturing any part are divided into four stages: the standby,

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spindle start-up, idling, and cutting processes, e.g., the manufacturing process of part 2 in Fig.1. The

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manufacturing processes for the VP used to represent all possible parts that may be manufactured by machining

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systems intrinsically consist of the above-mentioned operating processes. The research suggests that the power

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demand is constant in standby process, while the energy consumption in the spindle start-up and idling processes is

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related to spindle revolving speeds, and that in the cutting process is related to the load power [34].

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Assume that there exists an equivalent part with a manufacturing process including (1) a standby process, (2)

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spindle start-up processes at the possibly occurring spindle revolving speeds, (3) idling processes at the possibly

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occurring spindle revolving speeds, and (4) cutting processes including all the load power. The equivalent part can

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obviously be taken as a VP for the machining system. Based on the assumption, the processes for machining a

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virtual part can be illustrated in the Fig. 2 and involve the following characteristics:

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1) Revolving speed characteristic. All spindle revolving speeds under the range of the rated revolution are

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involved in the virtual process, because the revolving speed is a key parameter in all operation states, with the

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exception of standby, and the energy demand in those states largely depends on the revolving speed [35].

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2) Spindle start-up and idling characteristics. All spindle start-up and idling processes under the range of the

144 145

rated revolution are also involved in the virtual process, owing to their frequent occurrence in the service period. 3) Load characteristic. The loads in the virtual process are divided into three sections: light, medium-sized,

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and heavy loads. Each load power comprises a cutting power representation (e.g., 𝑃𝐶,𝐻, 𝑃𝐶,𝑀, 𝑃𝐶,𝐿)and an

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additional power loss representation (e.g., 𝑃𝑎,𝐻, 𝑃𝑎,𝑀, 𝑃𝑎,𝐿), as illustrated in Fig. 2.

𝑛𝑖

𝑛𝑖

𝑛𝑖

𝑛𝑖

𝑛𝑖

𝑛𝑖

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4) Time characteristic. Two types of time parameters, namely the rotation time at different revolving speeds

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and the cutting time (also known as load time) at different type loads, are included in the virtual process. The

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rotation time is related to the usage probability of the corresponding revolving speed, while the cutting time

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depends on the occurring probability of the corresponding load.

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152 153

Fig. 2. Schematic for machining VP.

154

Obviously, the VP cannot be an entitative part; it is an imagined equivalent part integrating and simplifying

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the energy consumption of all possible parts manufactured in the future. “Simplifying” refers to the fact that the

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manufacturing process is simplified into many “subprocesses” consisting of the four processes and that the spindle

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start-up energy consumption and idling power for one spindle revolving speed are taken into account just once,

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because the energy consumption characteristic of the spindle start-up and idling processes at the same spindle

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revolving speed, is the same. The load power is also simplified into three categories.

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Generally, the revolving speed of most step-less revolving speed regulation (SLSR) machining systems is

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continuous; however, in the actual machining process, the revolving speed is also represented by a discrete

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description (the minimum interval is 1). As a result, the discrete symbol 𝑛𝑖 in Fig. 2 can also represent the

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revolving speed for the SLSR machining system.

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Compared to the entitative part, the VP includes nearly all energy consumption characteristics during the

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future manufacturing process; therefore, it can represent the future production of machining systems during the

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usage phase from an energy perspective. Furthermore, the VP energy consumption of a machining system is

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constant and unique. Therefore, the use of the VP will not result in doubts and disputations caused by test results

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derived from different reference parts.

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3 Energy efficiency evaluation of machining system through virtual part

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3.1 Selection Indexes for energy efficiency evaluation of machining system

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Traditionally, energy efficiency has been defined as the simple ratio of the useful energy output of a process

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to its energy input, where the useful output is generally represented by the thermodynamic, physical, and economic

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outputs [36]. For machining systems, the useful output often refers to the cutting energy, material removal volume,

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or number of produced parts. Based on this, a series of evaluation indexes, such as the cutting power to input

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power ratio [37], energy intensity, and SEC, have been presented to evaluate the energy efficiency of machining

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systems. In this study, two modified indexes, namely the comprehensive energy consumption (CEC) in Eq. (1) and 6

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the comprehensive energy utilization (CEU) in Eq. (2), based on energy utilization and SEC, are selected as the

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indexes for evaluating the energy efficiency of machining systems.

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The CEC refers to the total energy demand for machining the VP and is a key parameter for predicting the

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SEC of a machining system in the usage phase. Given that the service time from the machining system birth to

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end-of-life is acquired by specific means, the total energy required by the machining system in the usage phase can

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be estimated though the CEC, multiplied by the ratio of service time to virtual process time. In general, the CEC is

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calculated as in Eq. (1).

184

𝐶𝐸𝐶 = 𝐸𝑠𝑏 + 𝐸𝑠𝑡 + 𝐸𝑢 + 𝐸𝑐 + 𝐸𝑎,

(1)

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where 𝐸𝑠𝑏 denotes the total standby energy, 𝐸𝑠𝑡 is the total starting energy, 𝐸𝑢 is the total idling energy, 𝐸𝑐 is the

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total cutting energy, and 𝐸𝑎 is the additional load loss.

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The CEU is the ratio of cutting energy to total energy in a virtual process for VP machining, as illustrated in

188

Eq. (2). It reflects the average energy utilization of the machining system, which is a primary figure for designers

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or decision makers to explore energy-efficient machining systems or processes.

190 191

𝐶𝐸𝑈 = 𝐸𝐶/𝐶𝐸𝐶

(2)

3.2 Corresponding data collection for calculating indexes

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According to the energy compositions of the virtual process for the VP machining, as provided in Fig. 2, the

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data for calculating the indexes can be divided into energy-correlated data (ECD) and energy-uncorrelated data

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(EUD), as listed in Fig. 3. The ECD are directly related to the energy consumption for the VP in the machining

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process, such as standby power and starting energy consumption. The EUD are indirect energy consumption data,

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including rotation time at different revolving speeds and cutting time (also known as load time) at different type

197

loads.

198 199 200 201 202 203 204

Fig. 3. Data components for calculating indexes and corresponding acquisition.

3.2.1 ECD These data include standby power, starting energy consumption, idling power, cutting power representation, and additional load loss power representation. The method for acquiring ECD is as follows. Standby power refers to the average power demand of the procedure, from switching on of the main power switch to starting the spindle or main motor, and it can be measured directly through a power sensor.

205

The starting energy consumption is the energy consumption of the procedure from zero rpm to a definite

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spindle rpm. Our research team has proposed a measuring method for acquiring the starting energy consumption, 7

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which is provided in the paper “A method for determining the energy consumption of machine tools in the spindle

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start-up process before machining” [38].

209 210 211

Idling power is the power required by the machining system when the spindle is rotating, but without cutting, and it can also be measured directly by a power sensor. The cutting power representation is related to the load type, as illustrated in Fig. 2. The light, medium-sized,

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and heavy loads in this study are defined as loads with power demands belonging to the power intervals,

213

respectively as follows: 1

214

𝑙𝑑

1

215

2

(4)

𝑃 (𝑛𝑖)~3𝑃 (𝑛𝑖)

3 𝑙𝑑

𝑙𝑑

2

216 217

(3)

0~3𝑃 (𝑛𝑖)

(5)

𝑃 (𝑛𝑖)~𝑃𝑙𝑑(𝑛𝑖)

3 𝑙𝑑

𝑠

The maximum load power 𝑃𝑙𝑑(𝑛𝑖) is usually estimated by subtracting the idling power 𝑃𝑢(𝑛𝑖) from the rating 𝑠 𝑃𝑒

218

power

of the main drive motor, because the energy consumption caused by the load is mainly determined by the

219

main drive system [34]. Given that the load in each power interval has a uniform distribution, the middle power in

220

each load interval can be chosen as the corresponding load power representation; that is, the heavy load power

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representation is 6𝑃 (𝑛𝑖) (the arithmetic mean of 3𝑃 (𝑛𝑖) and 𝑃𝑙𝑑(𝑛𝑖)). Based on this assumption, the relationship

222

between load power representation and cutting power expressed in Eq. (6), and the relationship

223

power and additional power loss shown in Eq. (7)[39], the corresponding cutting power representations can be

224

estimated by the system of equations (8).

5

2

𝑙𝑑

𝑙𝑑

226

229

{

𝑛𝑖

5

𝐻

𝑃𝐶,𝐻 = 𝑃𝑙𝑑(𝑛𝑖)(1 ‒ 𝛼) = 6𝑃 𝑛𝑖

5

1

𝑀

𝑛𝑖 𝐿 𝑃𝐶,𝐿 = 𝑃𝑙𝑑

(𝑛𝑖)(1 ‒ 𝛼) = 6(𝑃𝑒𝑠 ‒ 𝑃𝑢𝑠 (𝑛𝑖))(1 ‒ 𝛼)

𝑙𝑑

𝑃𝐶,𝑀 = 𝑃𝑙𝑑(𝑛𝑖)(1 ‒ 𝛼) = 2𝑃

1

(𝑛𝑖)(1 ‒ 𝛼) = 2(𝑃𝑒𝑠 ‒ 𝑃𝑢𝑠 (𝑛𝑖))(1 ‒ 𝛼)

𝑙𝑑

1

(8)

1

𝑠 𝑠 𝑛𝑖 (1 ‒ 𝛼) = 6𝑃 𝑛𝑖 (1 ‒ 𝛼) = 6(𝑃𝑒 ‒ 𝑃𝑢(𝑛𝑖))(1 ‒ 𝛼) 𝑙𝑑

( )

( )

The additional load loss power always exists together with the cutting power, and their relationship is shown in Eq. (7). The additional load loss power representation is calculated by the following equations. 𝑛𝑖

𝑛𝑖

𝑃𝑎,𝐻 = 𝛼𝑃𝐶,𝐻 𝑛𝑖

231

234

(6) (7)

230 232 233

𝑛𝑖

𝑃𝑎 = 𝛼𝑃𝑐

227

228

𝑛𝑖

𝐻

𝑃𝑙𝑑(𝑛𝑖) = 𝑃𝑐,𝐻 + 𝑃𝑎,𝐻

225

between cutting

𝑛𝑖

𝑃𝑎,𝑀 = 𝛼𝑃𝐶,𝑀 𝑛𝑖

𝑛𝑖

(9) (10) (11)

𝑃𝑎,𝐻 = 𝛼𝑃𝐶,𝑀 3.2.2 EUD

These data include the rotation time at different revolving speeds and cutting time at different type loads. The

235

corresponding calculation methods are as follows.

236 237

In this study, the rotation time 𝑇 at different revolving speeds is proportional to the corresponding usage probability 𝑓(𝑛𝑖). The usage probability calculation is related to the machining system type. For step revolving

238

speed regulation (SSR) machine systems, the usage probability is acquired directly via surveys. For certain SLSR

239

machining systems, the former method may not be suitable because too many cases of revolving speed exist, and it

240

is not practical to select all revolving speeds as test cases for industrial applications. Generally, for SLSR machine

241

systems, the revolving speed is firstly divided into several intervals; then, the revolving speed running time in each

𝑛𝑖

8

ACCEPTED MANUSCRIPT 242

interval and the corresponding usage probability are assessed via surveys. Finally, one representative revolving

243

speed is selected for each interval and the total usage probability for each interval is taken as the representative

244

revolving speed probability.

245

Similarly, the cutting time at different type loads is related to the corresponding probability of occurrence.

246

The occurrence probability of different machining system type loads can be determined by the following four

247

steps. (1) Collect the historical process files or cards for the objective machining system. (2) Calculate the cutting

248

power in the

249

historical process files or cards though empirical cut power models, i.e., Eq. (12) and Eq. (13) [40], and the

250

𝑃𝑐 = 𝐹𝑐𝑣 𝑥 𝑦 𝑧 𝐹𝑐 = 𝐶𝑎𝑝𝑓 𝑣 𝐾

251

(12) (13)

252

corresponding cutting time through the distance and feedrate in the files or card. (3) Recognize to which load type

253

the cutting power belongs and sum up the time for each type load, respectively. (4) Calculate the ratio of the

254

corresponding type load time to total time, and thus, obtain the occurrence probability of different type loads. After

255

acquiring the occurring probability, the cutting time at different type loads in this study is defined by the following

256

equations: 𝑛𝑖

257

𝑛𝑖

(14)

𝑛𝑖 𝑛𝑖 𝑛𝑖 𝑇𝐶,𝑀 = 𝑇𝑙𝑑𝑓𝐶,𝑀 𝑛𝑖 𝑛𝑖 𝑛𝑖 𝑇𝐶,𝐻 = 𝑇𝑙𝑑𝑓𝐶,𝐻

258 259 260

𝑛𝑖

𝑇𝐶,𝐿 = 𝑇𝑙𝑑𝑓𝐶,𝐿

where

𝑛𝑖 𝑇𝐶,𝐿,

𝑛𝑖 𝑇𝐶,𝑀,

𝑛𝑖 𝑇𝐶,𝐻

(15) (16)

indicate, respectively, the cutting time of the light, medium, and heavy loads at

𝑛𝑖

𝑛𝑖

𝑛𝑖

𝑛𝑖

261

revolving speed 𝑛𝑖; 𝑇𝑙𝑑 is the load time; and 𝑓𝐶,𝐿, 𝑓𝐶,𝑀, 𝑓𝐶,𝐻 are the occurring probabilities of the light, medium,

262 263

and heavy loads, respectively. For simplification, the load time 𝑇𝑙𝑑 can be replaced by the rotation time 𝑇 , as the feed time from the idling start to cutting start is usually very short compared to the total cutting time.

264

3.3 Development of energy efficiency evaluation system

𝑛𝑖

𝑛𝑖

265

In order to integrate the above data systematically and then calculate the CEC and CEU, an energy efficiency

266

evaluation system, mainly consisting of two power sensors and software, is developed. The construction

267

specifications of the evaluation system are listed in Table 1. The USB-RS485 converter in table is used to correct

268

the power sensor and the software. The power sensor installed at the machining system power input point

269

(measuring point 1 in Fig. 4) is used to measure the standby power, starting energy consumption, and idling power

270

in Fig. 3. The power sensor installed at the main drive motor power input point (measuring point 2 in Fig. 4) is

271

used to measure the main drive motor idling power, which is applied to support the cutting power representation

272

calculation in Eq. (8). Additional information about the power sensor is presented in Table 2. Because of the

273

restriction of sensor’s measuring range, the spindle motor measured should be an AC motor and their operating

274

power cannot extend the measuring range. If needed, you can change the power sensor 2 in Table 1 for other

275

spindle motor measurement.

276

Table 1.

277

Construction specifications of energy-efficiency evaluation system. Device

Function or measured parameters

Measuring range

Accuracy

Sampling time

Power sensor 1

Main power (Point 1 in Fig. 4)

0-13 KW

0.5% FS

50ms

Power sensor 2

Spindle power (Point 2 in Fig. 4)

0-8 KW

0.5% FS

50ms

USB-RS485 Converter

Translate data

Transmission speed: 300–921.6 kbps

----

Computer

Software operation

----

----

278 279

Table 2. 9

----

ACCEPTED MANUSCRIPT 280

Technical features of power sensor. Product Model

HC-33C3

Measuring range

0–13 KW, 0–8 KW.(Optional)

Overload capacity

Continuous: 1.2 * max range. Instantaneous: 5 * max range.

Main frequency(three-phase AC)

50 Hz

Accuracy

0.5% FS

Communication interface

RS-485 interface

Communication protocol

MODBUS-RTU, DLT546

Power supply

DC +10–30 V, AC 85–265 V.(Optional)

Environment temperature

-20–+60 ℃

Size

118*107*59 (mm)

281

282 283

Fig.4. Structure of energy efficiency evaluation system and its measuring position.

284

The evaluation software is employed to deal with the data and calculate indexes, as illustrated in Fig. 5. It can

285

be concluded that five parameters, 𝐸𝑠𝑏,𝐸𝑠𝑡, 𝐸𝑢,𝐸𝑐,𝐸𝑎, are the necessary data for calculating the index from Eqs. (1)

286

and (2). In the system, the total standby energy can be calculated directly by integrating the standby power and

287

time after the real-time power data collected from the power sensor is obtained, and the operating states are

288

recognized. The total starting energy and total idling energy are obtained, respectively, through the summation

289

formulas F and G in Fig. 5, after the corresponding energy at each spindle revolving speed is calculated by

290

integrating the corresponding power and time. The total cutting energy and additional load loss are obtained,

291

respectively, though summation (D, E, H and I in Fig. 5) after each cutting power representation, and additional

292

load loss at each spindle are calculated by formulas A, B, and C in Fig. 5. The  is the data acquired by the system

293

and the  is the data input from the human-computer interface shown in Fig. 7. At last, the indexes are calculated

294

by Eqs. (1) and (2), that is, the formula J in Fig. 5.

10

ACCEPTED MANUSCRIPT

295 296

Fig. 5. Data flow in evaluation software

297

One innovation in the evaluation software is that the operating states without load are evaluated online only

298

by their power and variations in power, avoiding the cost of the traditional method that purchases communication

299

protocols and then accesses the states from the CNC system. The state boundary recognition grounded on the

300

power and/or power-variation are presented in the following sections.

301



Start of standby. Two cases are involved in the start of the standby state: one is from stop to standby, and the

302

other from the idling state to standby, as illustrated in Fig. 6. The start time for the former case is considered

303

as the time at which the real-time obtained power increases from zero to nonzero, while for the latter case, it

304

is considered as the time at which the real-time power decreases sharply and then remains near the standby

305

power.

306



Beginning of the spindle start-up process. The determinant for the beginning boundary of the spindle start-up

307

process is demonstrated in Eq. (17). If the absolute value ∆ of the real-time average power in the n - tℎ

308

interval time 𝑃𝑛 and n + 1 - tℎ interval time 𝑃𝑛 + 1 is greater than the constant α at a moment, that moment

309

will be taken as the beginning of the spindle start-up process. The constant γ in Eq. (17) is determined by the

310

power waves in the standby state.

311 312

∆ = |𝑃𝑛 ‒ 𝑃𝑛 + 1| ≥ γ 

(17)

Start of idling state. The start can be determined indirectly by the power-variation characteristics at the end

313

of the spindle start-up process, because the idling state start is also the spindle start-up process end time. In

314

fact, the power in the idling state is stable, while that during the spindle start-up process varies; thus, during

315

the spindle start-up process, if the average power 𝑃𝑛 in the n - tℎ interval time and average power 𝑃𝑛 + 1 in

316

the n + 1 - tℎ interval time begin to satisfy Eq. (18) constantly from one moment 𝑡𝑛, moment 𝑡𝑛 will be

317

considered the idling state start.

318

∆ = |𝑃𝑛 ‒ 𝑃𝑛 + 1| ≤ β

319 320 321

(18)

The constant β taken as a criterion is determined by the power wave in the idling state. 

Time at power off. The time at power off is assessed as the moment at which the real-time power value decreases suddenly from nonzero to zero.

11

ACCEPTED MANUSCRIPT

322 323

Fig. 6. Schematic of power profile under different operation states (take YS 3118CNC5 as an example).

324

4 Case study

325

4.1 Objectives and experimental setup

326

The objectives of the case study are to validate the proposed method and then explore the possible energy

327

saving of the usage phase oriented for machining systems. The energy efficiency of one lathe C2-6150 in

328

Chongqing Shengong Mechanical Manufacture Co. Ltd., China is assessed by means of the developed energy

329

efficiency evaluation system. The evaluation system consists of a two-channel power sensor that can measure real-

330

time power at two different sites and the evaluation software developed by our team. The power sensor is installed

331

in the electric cabinet and it measures the main power input and spindle motor power input at the same time, as

332

shown in Fig. 7(a). The evaluation software is installed in the computer fixed at the machine tools shown in Fig. 7

333

(b). The partial parameters input in Fig. 7(c) include not only the necessary data ( in Fig. 5) for calculating

334

indexes, such as spindle rated power, additional load loss coefficient, and occurrence probabilities of the light,

335

medium-sized, and heavy loads, but also some basic information like spindle type and speed range.

336 337

（a）

(b)

(c)

338

Fig. 7. (a) Power sensor, (b) measuring object (Lathe C2-6150), and (c) evaluation software interface in the measuring process

339

Furthermore, in order to compare the characteristics between the reference entitative part method and the

340

proposed method in this study, three frequently produced parts by this machine tool, shown in Fig. 8, are selected

341

as the representations for the reference entitative part method to evaluate the energy efficiency of the machine tool.

342

Part A is a small idler wheel with only one spindle revolving speed, part C is a small axle with two spindle

343

revolving speeds and part B is a bigger idler wheel with two spindle revolving speeds. 12

ACCEPTED MANUSCRIPT

344 345

(a)

346 347

(b)

(c)

Fig. 8. Detailed drawing of three frequently produced parts: (a) part A, (b) part B, and (c) part C.

4.2 Results

348

In this case, occurrence probabilities of different load and usage probabilities of different revolving speeds,

349

are estimated from two manufacturing enterprises, namely Chongqing Machine Tool (Group) Co. Ltd., China, and

350

Chongqing Shengong Mechanical Manufacture Co. Ltd., China, by means of historical statistics. The former is

351

entered into the evaluation system through human-computer interface in Fig. 7(c) for calculating the cutting time.

352

The latter is used to acquire the rotation time for each spindle revolving speed in this proposed method and the

353

result is provided in Table 3. The data in Table 3 shows that although some parts, such as parts A, B, C, are

354

produced frequently, the corresponding spindle revolving speed, e.g., 500 and 1000 rpm, may not be the ones used

355

most frequently because many different small batch productions may employ the same spindle revolving speed.

356

Then, the machine tool is allowed to run according to the defined process listed in Table 3. The measurement

357

results will be obtained by the evaluation system, as provided in Fig. 9, including the two indexes and the energy

358

consumption composition. The related parameters of concern to user are also been presented on the left of Fig. 9.

359

With regard to reference entitative part method, the graph and the machining process of the selected parts are

360

presented in Fig. 8 and Table 4. The differences among the three parts are the metal removal volume and spindle

361

revolving speed, which usually have a bigger effect on energy and power demand. The cutting process for parts B

362

and C employed multiple spindle revolving speeds while that for part A is a single spindle revolving speed, as

363

illustrated in Table 4. The volume of metal removed for part B is bigger than that for parts A and C. The energy

364

consumption and efficiency of the machine tool using different parts are shown in Fig. 10. The energy

365

consumption composition is also described in Fig. 10 to analysis and explore the possible energy saving methods.

366

Furthermore, an economic cost analysis for implementing the two methods, outlined in Table 5, is carried out

367

for those economic factors playing a crucial role in the industry and based on estimations provided from the

368

literature [41] and the following assumptions: 1) the electric power cost is taken as 0.76 RMB/ kWh; 2) the cutting

369

fluid cost is considered as 0.8 RMB/h; 3) the material cost is taken as 7 RMB/kg; 4) the tool cost is assumed as

370

0.27 RMB/min based on literature [41] and the market; and 5) other costs are neglected.

371

Table 3.

372

Running times in different operation states. Standby time State Time （s）

between idling states

10

Idling running time 250 rpm

7

350 rpm

450 rpm

20

56

13

600 rpm

138

800 rpm

184

1000 rpm

1400 rpm

99

9

ACCEPTED MANUSCRIPT

373 374

Fig. 9. Results of the measurement through developed energy efficiency evaluation system.

375

Table 4

376

Cutting parameters of parts A, B, and C. Parts

Content

Spindle

revolving

speed

Feed (mm/min)

Number of feeds

(rpm) Part A

Part B

Part C

Turning (ф18 mm)

1000

200

1

Drilling（ф10 mm）

1000

50

1

Chamfer（1 mm）

1000

100

2

Cutting

1000

50

1

Cutting

500

120

1

Turning (ф85 mm)

500

120

2

Drilling (ф22 mm)

500

50

1

Hole turning (ф70 mm)

500

80

4

Cutting

1100

120

1

Turning (ф60)

1100

200

9

Turning (ф50)

1100

200

4

Hole turning (ф35 mm)

1100

100

5

Turning (ф12 mm)

1000

200

4

Slot (1 mm）

1000

50

1

Threading (M5)

600

100

4

*The cutting depth is determined by the CNC system automatically or by handlers

377 378

(a)

379

(b)

(c)

Fig. 10. Energy consumption composition for machining: (a) part A, (b) part B, and (c) part C.

380

Table 5.

381

Economic costs for measuring energy efficiency of the lathe in the case study. 14

ACCEPTED MANUSCRIPT Part A

Part B

Part C

VP

0.35

8.69

0.64

--

Tool cost

0.34

1.18

0.29

--

Electricity cost

0.042

0.229

0.037

0.326

Cutting fluid cost

--

0.06

--

--

Total cost

0.73

10.16

0.97

0.33

Material

cost

（RMB）

382

5 Discussion

383

5.1 Analysis and comparison of evaluation methods

384

There are four existing methods for evaluating the energy efficiency of machining systems: reference

385

entitative part, reference process, specific energy consumption, and component benchmark. Compared with the

386

reference process and component benchmark methods, which rarely consider the effects of the manufactured parts

387

on energy consumption, the VP method is more accurate for describing and evaluating the energy efficiency of the

388

machining system. This is because both the machine tool and part-related factors have certain effects on the energy

389

consumption of machine tools. The specific energy consumption, reference entitative part and the VP methods take

390

into account both factors. However, the specific energy consumption method ignores the energy consumption

391

during the standby, spindle start-up, and idling processes, which may cause disputes. Furthermore, the differences

392

between the reference entitative part and the VP methods are discussed below.

393

The energy demand and energy efficiency for each part are presented in Fig. 10. It was found that the energy

394

consumption and utilization were varied, with 200.53 kJ and 16.6% for part A, 1083.74 kJ and 47.4% for part B,

395

and 174.77 kJ and 14% for part C. Thus, the reference entitative part method has difficulties in selecting a

396

reasonable part and providing results that represent energy efficiency of the machining system, which makes it

397

challenging for an energy management organization to develop and implement the related standards. In contrast,

398

the CEU is nearly constant when the standby and starting energy consumption, accounting for less than 5% in Fig.

399

9, are ignored, for

400 401

CEU ≈ (

𝐸𝑐

(𝐸𝑢 + 𝐸𝑐 + 𝐸𝑎) =

𝑃𝑐

((1 + 𝛼)𝑃𝑐 + 𝑃𝑢)

(18)

where the cutting and idling power are the mean power, not a single value.

402

In many cases, the measuring process through the entitative part is more tedious, as it includes parameter

403

decision-making, tool change and cutting operations, and those usually required educated or experienced

404

implementers. For the VP method, the staff requirement is relatively low, involving the ability to turn on/off and

405

idling. In terms of the economic analysis, the average cost for the entitative parts is 3.95 in the present case, as

406

indicated in Table 5. Compared to the reference entitative part method, the proposed method is inexpensive, only

407

including the electricity cost. Assuming that each machine system requires an entitative part, the total cost for

408

evaluating the machining-system energy efficiency by means of the reference entitative part method would be

409

enormous. Meanwhile, the material requirement and solid pollution by scrap cannot be neglected, considering

410

resource challenges and environmental conservation challenges.

411

5.2 Energy saving and energy efficiency improvements

412

The objective of the energy efficiency research is to explore possible energy saving and energy efficiency

413

improvements. The results of the case study suggest that the usage-phase orientated CEU of the machining system

414

is low, of less than 50% as shown in Fig. 9, and the energy saving potential for the machining systems is

415

enormous. From the view of energy consumption constitution, reducing idling time and power is an effective 15

ACCEPTED MANUSCRIPT 416

means of reducing the machining system energy consumption. Furthermore, reducing the additional load loss via

417

transmission system optimization design is an effective option, because the additional load loss may reach 5%, as

418

shown in Fig. 9.

419

In addition to the above-stated recommendations, energy efficiency evaluation of machining systems through

420

the VP can contribute to the following energy-saving activities, which involve energy efficiency standards and

421

energy labels, design of energy-efficient machining systems, and reform of old machining systems.

422

(1) Energy efficiency standards and energy labels. Energy efficiency standards and labels constitute a

423

broadlyr strategy for saving energy and educating consumers to use energy wisely. The modified indexes used in

424

this study, namely, CEC and CEU, are helpful for establishing standards to evaluate the energy efficiency of

425

machining systems during the usage phase. The related measurement results also provide energy information for

426

developing energy labels for machining systems, such as information-only labels.

427

(2) Design of energy-efficient machining systems. At present, most of the research on design methodologies

428

for energy-efficient machining systems involves energy efficiency evaluation of the systems and their components

429

during their future usage phase [24]. The method proposed in this study for evaluating the energy efficiency of

430

machining systems provides guidance for their design, as it involves the acquisition and comprehensive analysis of

431

the energy efficiency of the system during the usage-phase. For example, the maximum energy consumption for a

432

machining system is the idling energy consumption, as shown in Fig. 9. As a result, an effective means of

433

designing an energy-efficient machining system is to reduce the idling power by selecting appropriate components

434

or optimizing the mechanical structures of the machining system.

435

(3) Reform of old machining systems. An abundance of old machining systems exists worldwide, particularly

436

in manufacturing countries. For example, in China, the number of machine tools that have been in use for more

437

than 10 years exceeds two million [42]. Therefore, the reform of old machining systems is also very important for

438

achieving energy-saving goals in machining manufacture. Usually, the reform of old machine tools requires an

439

analysis of their energy consumption during the usage phase, and the method proposed in this study is helpful in

440

this regard.

441

6 Conclusion

442

The wide distribution of low-efficiency machining systems and their large amount of energy consumption

443

offers a considerable energy-saving potential. In this study, to overcome the deficiencies of previous energy-

444

efficiency evaluation methods in the application process, the new concept of VP and a method based on it are

445

proposed for evaluating the energy efficiency of machining systems, which contribute to improving the energy

446

management and increasing the energy efficiency. The results of the study are summarized as follows.

447

First, previous studies on energy efficiency, related to machining or production, were analyzed. In view of the

448

following problems, the concept of the VP was proposed. The VP in this research is an imagined equivalent part,

449

which integrates and simplifies the energy consumption of all possible parts and overcomes the limitations existing

450

in the reference part, such as cutting parameters, geometrical shape, part material, and machining features.

451

Moreover, without specific material and machining process, the VP also avoids material wastage and is cost

452

effective.

453

There is agreement in that the energy consumption of machining processes is determined by both their

454

intrinsic characteristics and the machining tasks. For most machine tools, the machining tasks are various and

455

uncertain. The existent major evaluation methods for machining systems are hindered either by losing sight of the

456

effects of parts on energy consumption (e.g., the component benchmark and reference process methods) or by the

457

design methodology for the reference part (e.g., the reference entitative part method).

458

Second, a method for evaluating the energy efficiency of machining system through virtual part was 16

ACCEPTED MANUSCRIPT 459

proposed. The proposed method comprised three steps: 1) selection of evaluation indexes, 2) data collection for

460

calculating these indexes, 3) development of the energy efficiency evaluation system. Using this evaluation

461

system, the energy efficiency of machining system can be evaluated on simply by allowing the machining system

462

to run with no load for a defined process.

463

The energy efficiency evaluation through VP was applied to one lathe in Chongqing Shengong Mechanical

464

Manufacture Co. Ltd., China, demonstrating that the proposed method and developed system are feasible for

465

energy efficiency evaluation of machining systems. The test results may could a crucial role in exploring usage-

466

phase oriented energy saving in machining manufacture, including the design of energy-efficient machining

467

systems and reform of old machining systems.

468

Furthermore, the two usage data sets involved in the case study, obtained by consulting two companies, may

469

not be completely accurate. Therefore, in future studies, we will attempt to use feedback data through computer

470

science concepts, such as the internet of things and artificial intelligence.

471

Acknowledgements

472

The author acknowledges the support provided by the National Natural Science Foundation of China (Grant

473

number 51775392), the two manufacturing enterprises in the case study, and the scholars who contributed with

474

their valuable comments on this paper.

475

References

476

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ACCEPTED MANUSCRIPT Highlights    

A new energy-efficiency evaluation method for machining systems is proposed. A virtual part to overcome limitations in reference entitative part is designed. A software system for evaluating energy efficiency is developed. The energy saving based on energy efficiency evaluation is analyzed.