The environment, resources, and population are the three major issues facing human society today. In particular, environmental issues have become increasingly deteriorating and are currently threatening the survival and development of human society. Manufacturing industry is a pillar industry that creates human wealth, but at the same time it consumes a great deal of human society's limited resources, and it is the main cause of current environmental pollution problems. Therefore, it is imperative for the manufacturing industry to implement a sustainable development strategy. Green manufacturing is a modern manufacturing model that comprehensively considers environmental impact and resource efficiency, and its goal is to make the product's impact on the environment (negative) during the entire product life cycle from design, manufacture, packaging, transportation, use to end-of-life disposal. It may be small, consume as little resources as possible, and coordinate and optimize the economic and social benefits of the company.
Machining is the main basic process of manufacturing technology. With the development of manufacturing technology, it has entered a new stage of development characterized by the development of high-speed cutting, the development of new cutting processes and processing methods, and the provision of complete sets of technologies. Such as high-speed cutting is the development direction of cutting processing has become the mainstream of cutting, its development and promotion and application (including the comprehensive development and improvement of machine tools and cutting tool technology) will promote the overall manufacturing level, efficiency, environmental friendliness and advancement and improvement . In the modern cutting process, the cutting tool is an important factor in ensuring the machining quality and improving the production efficiency in the cutting process, and also affects the energy consumption in the cutting process, the use of the cutting fluid, and the safety of production, that is, the influence of cutting. Greenness of processing. In recent years, the development of environment-friendly high-performance tool materials (including surface coating materials), tool manufacturing process technology, tool safety technology, and tool use technology have greatly improved the cutting performance, enabling high-speed cutting, dry cutting, and other green cutting processes. The technology has been realized, and the machining quality, cutting efficiency, processing cost, energy consumption, and use of cutting fluid in the traditional cutting process have also been improved, which is conducive to green manufacturing. In the process planning for green manufacturing, the reasonable choice of tool directly determines the impact of the tool on the green performance of the cutting process. From the five aspects of processing time, processing quality, cost, resource consumption and environmental impact, the impact of cutting tools on cutting machining was analyzed, a multi-objective evaluation system for tool selection was established, and a tool selection decision framework for green manufacturing was established. The model uses the combination of quantitative analysis of fuzzy clustering and qualitative analysis of expert systems to solve the model. 1 The establishment of an integrated tool selection model for green manufacturing The authors have conducted long-term research on resource issues and energy issues in manufacturing systems directly related to green manufacturing since the early 1980s, and proposed the T (time) of green manufacturing. Q (quality), C (cost), E (environmental impact), R (resources consumption) and other five decision-making target variables of the decision-making target system and the corresponding decision-making framework model. Any decision-making problem in green manufacturing is more or less related to some or all of the above five decision-making target variables. Combining with the specific problems of tool selection, a tool decision-making target system for green manufacturing is proposed, and on the basis of this, a comprehensive selection model of tool operability and a corresponding model solution method are established. The target system of the tool decision-making problem for green manufacturing The comprehensive selection of tools for green manufacturing is actually a multi-objective, qualitative and quantitative complex decision-making problem. In general, for tool selection, optimization of the cutting process is based on the highest productivity (or the shortest production time), the lowest unit cost, and the highest profitability. With the requirement of sustainable development of the manufacturing industry in the 21st century, the tool manufacturing industry will shift from a single objective model that seeks for economic benefits to a multi-objective model that pursues the coordinated development of economic, social, and environmental benefits. The factors that influence the development of the tool industry will be expanded from the functions, quality, cost, and service of the traditional tool industry to function, quality cost, time, environment, and resources. The tool selection decision target system for green manufacturing considers time (T), quality (Q), cost (C), resource (R), and environment (E) as important factors. The corresponding objective function is processing time T (X ), processing quality Q (X), production cost C (X), resource consumption R (X), environmental impact E (X). The results of the changes sought for these five target variables, that is, the smaller the better the processing time is, the better the processing quality is. The lower the better the production cost, the lower the better the resource consumption is, the better the environmental impact is. . There are close links between the above five decision-making goals, and they together constitute a tool-making target system for green manufacturing. The corresponding decision vector decomposition is shown in Figure 1:
Figure 1 Cutting tool decision-making for green manufacturing The environmental impact of multi-objective tools mainly includes the aspects of ecological environment impact, occupational health and safety management, and the impact of production safety. Variables for tooling decision-making problems for green manufacturing Description The optimal decision-making problem for tools is to use program optimization problems, that is, to choose the best or relatively optimal ones from several possible solutions. For the use plan optimization of the tool, the variables can be described by an n-dimensional vector, ie: X=[x1,x2,...,xn]T (1) where n is the number of possible machining plans; xi(i=1) , 2, ..., n) represent the first processing plan and have xi=
0 Do not use the ith scheme
1 The ith solution is adopted. Therefore, for the optimization of the use of the tool, all the variables involved can be represented by an n-dimensional vector. Based on the above analysis, the decision framework model can establish a mathematical model (I) for the green manufacturing tool decision framework. T(X) describes the time decision goal, Q(X) describes the quality decision goal, C(X) describes the cost decision goal, R(X) describes the resource consumption decision goal, and E(X) describes the environmental decision goal. The preferred decision for a tool is always: X=[x1,x2,...,xn]T Find: X*=[x*1,x*2,...,x*n]T
Satisfy: Gu(x)≤0(u=1,2,...,k)
Hv(x)=0(v=1,2,...,p X ∈ Rn,x1,x2,..., represents an alternative
X* is the optimal tool selection
Gu (X) inequality constraints hv (X) equality constraints In production practice, the values ​​of cutting speed and feed are not arbitrarily chosen, and they are subject to various limitations of production conditions. For example, the maximum feed rate is limited not only by the tool's durability, but also by the roughness of the machined surface, the rigidity of the work piece, the strength and stiffness of the tool, and the reliability of the clamping mechanism. The above selection model is a system under a given environmental conditions (ie, system constraints, such as limited resource control, quality objectives, cost objectives, environment objectives and raw material constraints, and meeting process requirements) and goals. The multi-objective planning model has two constraints, where gu(X) is the model inequality constraint and hv(X) is the model's equality constraint. Model Solving Method Due to the complexity of the preferred decision-making problems for green manufacturing tools, many of the above goals in the overall decision-making framework are difficult to quantify and analyze. Many problems can only be solved by a combination of qualitative analysis, quantitative analysis, and logical judgment. . The combination of the quantitative analysis of the fuzzy clustering comprehensive evaluation and the qualitative reasoning of the expert scoring method is one of the effective methods to solve this problem. Fuzzy clustering analysis and evaluation method is a method of using fuzzy set theory to comprehensively evaluate and make decisions on the system. The information about the priority of each candidate program can be obtained. Therefore, it is also one of the effective methods for the analysis of green manufacturing decisions. The main steps of applying the quantitative analysis of fuzzy clustering comprehensive evaluation and the qualitative reasoning of expert scoring method are as follows: To establish the evaluation index set UU={u1,u2,...,ui,...,um} where m is the number of evaluation aspects, In the i-th evaluation integration ui (i=1, 2, ..., n), N is the number of the first evaluation element, and can continue to be divided if necessary. For a certain index, fuzzy evaluation is performed on the next layer of elements dominated by the index, and an evaluation level set VV=(v1,v2,...,vj,...,vm) can be used to easily take the same number of evaluation levels. number. Get the original data matrix of n programs, ie
The calibration of the distance is to obtain a statistic rij that measures the degree of similarity between the classified objects, thereby determining the fuzzy similarity matrix R on the universe of the discourse. The statistics are normalized for analysis and comparison. Usually, the extreme value normalization formula can be used: 
Where: i∈(1,n),j∈(1,m). Obviously x'ij ∈ [0,1], then the matrix X'[x'ij] is obtained. The meaning of each x'ij indicates the degree of membership of the index affiliation with the set Xi. The degree of similarity rij between xi and xj is determined by the closeness t(Xi, Xj), ie, rij=t(Xi, Xj), and the closeness can be approximated by Haiming. 
Can also use the minimum and maximum closeness 
. Where: k∈(1,m). Therefore, the similarity matrix R=[rij] is used for cluster analysis. Generally, fuzzy equivalence relationship clustering method and maximum tree method are used. Since the fuzzy similarity relationship is not necessarily a fuzzy equivalence relationship, it is necessary to transform R into a fuzzy equivalence relationship R*, and then make a clustering graph and perform interception at the appropriate threshold value to perform the necessary classification. Establishing a weight set Based on the evaluation results obtained, the weights are determined using the expert's scoring method as follows: A=(a1,a2,...,aj,...,am), and satisfy 
Perform fuzzy transformations and normalize them. The fuzzy transformation formula is: B=A·R=(b1,b2,...,bk,...,b1), and the normalized formula is: 
Repeat the above steps to merge layers upwards to get the overall evaluation result of the solution. Finally, a total score can also be used to express the comprehensive evaluation result. Generally, it is desirable that the evaluation criteria membership degree set is u={u1,u2,ui...un...}, then a comprehensive evaluation result degree specific score can be calculated, according to which the score can be The objects to be evaluated are sorted. The method is: 
2 Case study The tool used in the NC rolling machining process of a machine tool plant is a conventional ordinary high-speed steel. During the machining process, the product surface machining quality, machining efficiency and tool life are lower, the tool wear amount is larger, and the machining process is in progress. The resulting oil mist, swarf, noise and other environmental pollution caused by large. The plant hopes to use a domestically developed TiN-coated high-speed steel cutting tool and imported TiN-coated high-speed steel cutting tools instead of conventional ordinary high-speed steel tools to improve the competitiveness of the product, and requires the authors to help make decisions. . In this paper, cutting tools for three different materials, including conventional high-speed steel, TiN-coated high-speed steel, and imported TiN-coated high-speed steel, are used on the same CNC machine tool YKX31320 (completely enclosed). Different cutting methods are adopted for the reverse milling and the down milling, respectively. With the same processing parameters, the 45 steel workpieces of the same workpiece were fired in the 0903201, and the flank wear test of the tool complied with the ISO 3685-1977(E) international standard. The system made a decision on this issue from the perspective of green manufacturing. Test items and test results are shown in Table 1 below. Table 1 Comparison Results of the 4 Projects The following items ABCD Tool parameters Material High-speed steel Up-cutting Coating High-speed steel Inverse milling Imported high-speed steel Inverse milling Imported high-speed steel Simulant milling modulus 4 4 3.941/4.00 3.941/4.00 Helical direction right-handed (2° 20') Right-handed (2°16') Right-handed (4°27'14") Righthanded (4°27'14") Specifications f40*f110*110 Accuracy Class AA Cutting specifications Number of cuts 5 Cutting speed m/min 43.2 69 67.2 Hob revolution r/min 125 200 Axial feed mm/r 1.4 Tool depth mm 8 5 Inspection item Processing time 19m6s 13m19s 6m56s 6m40s Surface roughness 2.125 1.75 1.25 0.825 Tool life (cutting length m) 1.5 2.25 3.75 4.0 Tool wear 0.105 0.071 0.0425 0.040 Energy consumption 2.6 kW (with spindle 1.64 kW) 3.9 kW (with spindle 2.7 kW) 3.6 kW (with spindle 2.6 kW) 3.45 kW (with spindle 2.5 kW) Cost per unit production tool ( (Yuan) 4.27 3.64 5.2 4.83 Cost (yuan) 9.56 9.33 9.0 8.55 Machine noise 78.5 78.0 77.3 76.6 Less liquid mist of cutting fluid General consumption of cutting fluid 1 0.70 0.37 Safety 0.5 0.6 0.8 Remarks Machining machine: YB3120 is a three-axis CNC Machine Tools; Workpieces: Steel 45°, Modulus 4, Teeth 49, Outer Diameter 209 Cutting width 40. Note: The cutting fluid consumption is based on standard high-speed steel as the reference value; noise is the same value that produces the maximum noise point. Variable description (plan description) X=(x1,x2,x3,x4) where xi(i=1,2,3,4)= 
0—Do not use the ith scheme
1—Using the ith scheme X= (1,0,0,0)=Solution A, ie, the high-speed steel tool milling method (x1=1, x2=0, x3=0, x4=0)
(0,1,0,0)=Solution B, namely the up-cut milling method using homemade coated high speed steel tools (x1=0, x2=1, x3=0, x4=0)
(0,0,1,0)=Option C, that is, an up-cut milling method using an imported coated HSS tool (x1=0, x2=0, x3=1, x4=0)
(0,0,0,1)=Option D, that is, using the imported coated high-speed steel tool milling (x1=0, x2=0, x3=0, x4=1) objective function and preliminary analysis time function T ( X), the quality function Q(X), the cost function C(X), the resource consumption function R(X), and the environmental impact function E(X) include the contents shown in Fig. 1, based on the tool-making target for green manufacturing. The decomposition content of the system establishes four comprehensive evaluation systems. As shown in Table 1, in this decision process, the five objective functions are comprehensively evaluated using the solution method given in the decision model to obtain quantifiable results. Reflects the final optimization results of the 4 scenarios. The results of the comprehensive evaluation of the four schemes are based on the solution method of the above model, and the results of the comprehensive evaluation matrix B and comprehensive evaluation scores of the four schemes are shown in Table 2: Table 2 Comparison results of the four schemes Comprehensive evaluation matrix Comprehensive evaluation score A (0.486 ,0.302,0.205) 67.47 B (0.567,0.302,0.122) 72.79 C (0.757,0.179,0.07) 80.67 D (0.764,0.168,0.068) 81.12 The final comprehensive evaluation results show that: The method for the milling of imported TiN coated high-speed steel > Plan for Inverted Milling of Imported TiN Coated High Speed ​​Steel> Plan for Upward Milling of TiN-Coated High Speed ​​Steel Tools Developed in China> Plan for Upward Milling of Ordinary High Speed ​​Steel. In particular, the experimental data describe the energy consumption and environmental impact factors of the target system, such as energy consumption, noise, machining accuracy, surface roughness, and tool life and tool wear. The imported TiN-coated high-speed steel tools are also superior to the TiN coating in sequence. The results of high-speed steel cutters and ordinary high-speed steel cutters are shown in Table 1. If the wear standard is 0.105mm, the domestic TiN-coated high-speed steel cutters and imported TiN coated high-speed steel cutters are durable. The degrees are about 2 and 5 times higher than the coated HSS tools, respectively. The surface roughness of the workpiece processed by the coated tool is reduced by about 50% compared with the surface roughness of the workpiece processed by the coated tool. From Table 1, it can be seen that although the cost of the single-piece production of imported TiN coated high-speed steel is high, due to the improvement of efficiency, the cost and labor costs of the machine tool are greatly reduced, and the overall cost is reduced. This is also an industrial development. The national manufacturing industry adopts a consistent business strategy. Therefore, under the consideration of the comprehensive cost under large-scale mass production, the plant still adopts the imported TiN-coated high-speed steel cutting tool program, and as a result, it has achieved significant comprehensive benefits (economic benefits and social benefits). Practical production shows that it has achieved good results. 4 Conclusions In the modern cutting process, the cutting tool is an important factor in ensuring the machining quality, improving the production efficiency and reducing the environmental pollution in the cutting process. The reasonable choice of tool is one of the important ways to improve the green performance of the cutting process. A multi-objective decision-making multi-objective system for the green manufacturing tool is established, including five major decision targets such as time T, quality Q, cost C, resource consumption R, and environmental impact E, and the decision vector in the target system is decomposed. On this basis, a comprehensive tool selection model for green manufacturing is established, and it is suggested that a method for solving the model is adopted by combining the quantitative analysis of the fuzzy clustering comprehensive evaluation with the qualitative reasoning of the expert system. The above model and method are verified by examples, which shows that the application of the model and the solution method is feasible.
Machining is the main basic process of manufacturing technology. With the development of manufacturing technology, it has entered a new stage of development characterized by the development of high-speed cutting, the development of new cutting processes and processing methods, and the provision of complete sets of technologies. Such as high-speed cutting is the development direction of cutting processing has become the mainstream of cutting, its development and promotion and application (including the comprehensive development and improvement of machine tools and cutting tool technology) will promote the overall manufacturing level, efficiency, environmental friendliness and advancement and improvement . In the modern cutting process, the cutting tool is an important factor in ensuring the machining quality and improving the production efficiency in the cutting process, and also affects the energy consumption in the cutting process, the use of the cutting fluid, and the safety of production, that is, the influence of cutting. Greenness of processing. In recent years, the development of environment-friendly high-performance tool materials (including surface coating materials), tool manufacturing process technology, tool safety technology, and tool use technology have greatly improved the cutting performance, enabling high-speed cutting, dry cutting, and other green cutting processes. The technology has been realized, and the machining quality, cutting efficiency, processing cost, energy consumption, and use of cutting fluid in the traditional cutting process have also been improved, which is conducive to green manufacturing. In the process planning for green manufacturing, the reasonable choice of tool directly determines the impact of the tool on the green performance of the cutting process. From the five aspects of processing time, processing quality, cost, resource consumption and environmental impact, the impact of cutting tools on cutting machining was analyzed, a multi-objective evaluation system for tool selection was established, and a tool selection decision framework for green manufacturing was established. The model uses the combination of quantitative analysis of fuzzy clustering and qualitative analysis of expert systems to solve the model. 1 The establishment of an integrated tool selection model for green manufacturing The authors have conducted long-term research on resource issues and energy issues in manufacturing systems directly related to green manufacturing since the early 1980s, and proposed the T (time) of green manufacturing. Q (quality), C (cost), E (environmental impact), R (resources consumption) and other five decision-making target variables of the decision-making target system and the corresponding decision-making framework model. Any decision-making problem in green manufacturing is more or less related to some or all of the above five decision-making target variables. Combining with the specific problems of tool selection, a tool decision-making target system for green manufacturing is proposed, and on the basis of this, a comprehensive selection model of tool operability and a corresponding model solution method are established. The target system of the tool decision-making problem for green manufacturing The comprehensive selection of tools for green manufacturing is actually a multi-objective, qualitative and quantitative complex decision-making problem. In general, for tool selection, optimization of the cutting process is based on the highest productivity (or the shortest production time), the lowest unit cost, and the highest profitability. With the requirement of sustainable development of the manufacturing industry in the 21st century, the tool manufacturing industry will shift from a single objective model that seeks for economic benefits to a multi-objective model that pursues the coordinated development of economic, social, and environmental benefits. The factors that influence the development of the tool industry will be expanded from the functions, quality, cost, and service of the traditional tool industry to function, quality cost, time, environment, and resources. The tool selection decision target system for green manufacturing considers time (T), quality (Q), cost (C), resource (R), and environment (E) as important factors. The corresponding objective function is processing time T (X ), processing quality Q (X), production cost C (X), resource consumption R (X), environmental impact E (X). The results of the changes sought for these five target variables, that is, the smaller the better the processing time is, the better the processing quality is. The lower the better the production cost, the lower the better the resource consumption is, the better the environmental impact is. . There are close links between the above five decision-making goals, and they together constitute a tool-making target system for green manufacturing. The corresponding decision vector decomposition is shown in Figure 1:
Figure 1 Cutting tool decision-making for green manufacturing The environmental impact of multi-objective tools mainly includes the aspects of ecological environment impact, occupational health and safety management, and the impact of production safety. Variables for tooling decision-making problems for green manufacturing Description The optimal decision-making problem for tools is to use program optimization problems, that is, to choose the best or relatively optimal ones from several possible solutions. For the use plan optimization of the tool, the variables can be described by an n-dimensional vector, ie: X=[x1,x2,...,xn]T (1) where n is the number of possible machining plans; xi(i=1) , 2, ..., n) represent the first processing plan and have xi=
0 Do not use the ith scheme 1 The ith solution is adopted. Therefore, for the optimization of the use of the tool, all the variables involved can be represented by an n-dimensional vector. Based on the above analysis, the decision framework model can establish a mathematical model (I) for the green manufacturing tool decision framework. T(X) describes the time decision goal, Q(X) describes the quality decision goal, C(X) describes the cost decision goal, R(X) describes the resource consumption decision goal, and E(X) describes the environmental decision goal. The preferred decision for a tool is always: X=[x1,x2,...,xn]T Find: X*=[x*1,x*2,...,x*n]T
Satisfy:
Gu(x)≤0(u=1,2,...,k) Hv(x)=0(v=1,2,...,p
X* is the optimal tool selection
Gu (X) inequality constraints hv (X) equality constraints In production practice, the values ​​of cutting speed and feed are not arbitrarily chosen, and they are subject to various limitations of production conditions. For example, the maximum feed rate is limited not only by the tool's durability, but also by the roughness of the machined surface, the rigidity of the work piece, the strength and stiffness of the tool, and the reliability of the clamping mechanism. The above selection model is a system under a given environmental conditions (ie, system constraints, such as limited resource control, quality objectives, cost objectives, environment objectives and raw material constraints, and meeting process requirements) and goals. The multi-objective planning model has two constraints, where gu(X) is the model inequality constraint and hv(X) is the model's equality constraint. Model Solving Method Due to the complexity of the preferred decision-making problems for green manufacturing tools, many of the above goals in the overall decision-making framework are difficult to quantify and analyze. Many problems can only be solved by a combination of qualitative analysis, quantitative analysis, and logical judgment. . The combination of the quantitative analysis of the fuzzy clustering comprehensive evaluation and the qualitative reasoning of the expert scoring method is one of the effective methods to solve this problem. Fuzzy clustering analysis and evaluation method is a method of using fuzzy set theory to comprehensively evaluate and make decisions on the system. The information about the priority of each candidate program can be obtained. Therefore, it is also one of the effective methods for the analysis of green manufacturing decisions. The main steps of applying the quantitative analysis of fuzzy clustering comprehensive evaluation and the qualitative reasoning of expert scoring method are as follows: To establish the evaluation index set UU={u1,u2,...,ui,...,um} where m is the number of evaluation aspects, In the i-th evaluation integration ui (i=1, 2, ..., n), N is the number of the first evaluation element, and can continue to be divided if necessary. For a certain index, fuzzy evaluation is performed on the next layer of elements dominated by the index, and an evaluation level set VV=(v1,v2,...,vj,...,vm) can be used to easily take the same number of evaluation levels. number. Get the original data matrix of n programs, ie
Can also use the minimum and maximum closeness . Where: k∈(1,m). Therefore, the similarity matrix R=[rij] is used for cluster analysis. Generally, fuzzy equivalence relationship clustering method and maximum tree method are used. Since the fuzzy similarity relationship is not necessarily a fuzzy equivalence relationship, it is necessary to transform R into a fuzzy equivalence relationship R*, and then make a clustering graph and perform interception at the appropriate threshold value to perform the necessary classification. Establishing a weight set Based on the evaluation results obtained, the weights are determined using the expert's scoring method as follows: A=(a1,a2,...,aj,...,am), and satisfy Perform fuzzy transformations and normalize them. The fuzzy transformation formula is: B=A·R=(b1,b2,...,bk,...,b1), and the normalized formula is: 2 Case study The tool used in the NC rolling machining process of a machine tool plant is a conventional ordinary high-speed steel. During the machining process, the product surface machining quality, machining efficiency and tool life are lower, the tool wear amount is larger, and the machining process is in progress. The resulting oil mist, swarf, noise and other environmental pollution caused by large. The plant hopes to use a domestically developed TiN-coated high-speed steel cutting tool and imported TiN-coated high-speed steel cutting tools instead of conventional ordinary high-speed steel tools to improve the competitiveness of the product, and requires the authors to help make decisions. . In this paper, cutting tools for three different materials, including conventional high-speed steel, TiN-coated high-speed steel, and imported TiN-coated high-speed steel, are used on the same CNC machine tool YKX31320 (completely enclosed). Different cutting methods are adopted for the reverse milling and the down milling, respectively. With the same processing parameters, the 45 steel workpieces of the same workpiece were fired in the 0903201, and the flank wear test of the tool complied with the ISO 3685-1977(E) international standard. The system made a decision on this issue from the perspective of green manufacturing. Test items and test results are shown in Table 1 below. Table 1 Comparison Results of the 4 Projects The following items ABCD Tool parameters Material High-speed steel Up-cutting Coating High-speed steel Inverse milling Imported high-speed steel Inverse milling Imported high-speed steel Simulant milling modulus 4 4 3.941/4.00 3.941/4.00 Helical direction right-handed (2° 20') Right-handed (2°16') Right-handed (4°27'14") Righthanded (4°27'14") Specifications f40*f110*110 Accuracy Class AA Cutting specifications Number of cuts 5 Cutting speed m/min 43.2 69 67.2 Hob revolution r/min 125 200 Axial feed mm/r 1.4 Tool depth mm 8 5 Inspection item Processing time 19m6s 13m19s 6m56s 6m40s Surface roughness 2.125 1.75 1.25 0.825 Tool life (cutting length m) 1.5 2.25 3.75 4.0 Tool wear 0.105 0.071 0.0425 0.040 Energy consumption 2.6 kW (with spindle 1.64 kW) 3.9 kW (with spindle 2.7 kW) 3.6 kW (with spindle 2.6 kW) 3.45 kW (with spindle 2.5 kW) Cost per unit production tool ( (Yuan) 4.27 3.64 5.2 4.83 Cost (yuan) 9.56 9.33 9.0 8.55 Machine noise 78.5 78.0 77.3 76.6 Less liquid mist of cutting fluid General consumption of cutting fluid 1 0.70 0.37 Safety 0.5 0.6 0.8 Remarks Machining machine: YB3120 is a three-axis CNC Machine Tools; Workpieces: Steel 45°, Modulus 4, Teeth 49, Outer Diameter 209 Cutting width 40. Note: The cutting fluid consumption is based on standard high-speed steel as the reference value; noise is the same value that produces the maximum noise point. Variable description (plan description) X=(x1,x2,x3,x4) where xi(i=1,2,3,4)= 0—Do not use the ith scheme 1—Using the ith scheme X=
(1,0,0,0)=Solution A, ie, the high-speed steel tool milling method (x1=1, x2=0, x3=0, x4=0) (0,1,0,0)=Solution B, namely the up-cut milling method using homemade coated high speed steel tools (x1=0, x2=1, x3=0, x4=0)
(0,0,1,0)=Option C, that is, an up-cut milling method using an imported coated HSS tool (x1=0, x2=0, x3=1, x4=0)
(0,0,0,1)=Option D, that is, using the imported coated high-speed steel tool milling (x1=0, x2=0, x3=0, x4=1) objective function and preliminary analysis time function T ( X), the quality function Q(X), the cost function C(X), the resource consumption function R(X), and the environmental impact function E(X) include the contents shown in Fig. 1, based on the tool-making target for green manufacturing. The decomposition content of the system establishes four comprehensive evaluation systems. As shown in Table 1, in this decision process, the five objective functions are comprehensively evaluated using the solution method given in the decision model to obtain quantifiable results. Reflects the final optimization results of the 4 scenarios. The results of the comprehensive evaluation of the four schemes are based on the solution method of the above model, and the results of the comprehensive evaluation matrix B and comprehensive evaluation scores of the four schemes are shown in Table 2: Table 2 Comparison results of the four schemes Comprehensive evaluation matrix Comprehensive evaluation score A (0.486 ,0.302,0.205) 67.47 B (0.567,0.302,0.122) 72.79 C (0.757,0.179,0.07) 80.67 D (0.764,0.168,0.068) 81.12 The final comprehensive evaluation results show that: The method for the milling of imported TiN coated high-speed steel > Plan for Inverted Milling of Imported TiN Coated High Speed ​​Steel> Plan for Upward Milling of TiN-Coated High Speed ​​Steel Tools Developed in China> Plan for Upward Milling of Ordinary High Speed ​​Steel. In particular, the experimental data describe the energy consumption and environmental impact factors of the target system, such as energy consumption, noise, machining accuracy, surface roughness, and tool life and tool wear. The imported TiN-coated high-speed steel tools are also superior to the TiN coating in sequence. The results of high-speed steel cutters and ordinary high-speed steel cutters are shown in Table 1. If the wear standard is 0.105mm, the domestic TiN-coated high-speed steel cutters and imported TiN coated high-speed steel cutters are durable. The degrees are about 2 and 5 times higher than the coated HSS tools, respectively. The surface roughness of the workpiece processed by the coated tool is reduced by about 50% compared with the surface roughness of the workpiece processed by the coated tool. From Table 1, it can be seen that although the cost of the single-piece production of imported TiN coated high-speed steel is high, due to the improvement of efficiency, the cost and labor costs of the machine tool are greatly reduced, and the overall cost is reduced. This is also an industrial development. The national manufacturing industry adopts a consistent business strategy. Therefore, under the consideration of the comprehensive cost under large-scale mass production, the plant still adopts the imported TiN-coated high-speed steel cutting tool program, and as a result, it has achieved significant comprehensive benefits (economic benefits and social benefits). Practical production shows that it has achieved good results. 4 Conclusions In the modern cutting process, the cutting tool is an important factor in ensuring the machining quality, improving the production efficiency and reducing the environmental pollution in the cutting process. The reasonable choice of tool is one of the important ways to improve the green performance of the cutting process. A multi-objective decision-making multi-objective system for the green manufacturing tool is established, including five major decision targets such as time T, quality Q, cost C, resource consumption R, and environmental impact E, and the decision vector in the target system is decomposed. On this basis, a comprehensive tool selection model for green manufacturing is established, and it is suggested that a method for solving the model is adopted by combining the quantitative analysis of the fuzzy clustering comprehensive evaluation with the qualitative reasoning of the expert system. The above model and method are verified by examples, which shows that the application of the model and the solution method is feasible.
Food-grade 85 phosphoric ACID has a clear appearance and no odor. The liquid has a thick consistency and can be easily dissolved in water. The density of this chemical is about 1.67 kg/l. Phosphoric acid food-grade 85 is highly acidic with a pH level less than 1.
Properties:
colorless and transparent viscous liquid. It is odorless, sour, melting point 42.35 ℃, easily soluble in water, soluble in ethanol, can irritate the skin and cause inflammation, destroy body tissues, and have hygroscopicity.
Uses:
Used as sour agent and yeast nutrient, can be used as seasoning, canned, refreshing drink sour agent, used as yeast nutrition source when brewing, to prevent multiplication of bacteria.
Phosphoric acid is mainly used in the manufacture of various phosphate, such as ammonium phosphate, potassium dihydrogen phosphate, disodium hydrogen phosphate, trisodium phosphate, etc and condensation phosphate class.
It should be stored in a cool, ventilated warehouse. In high-concentration phosphoric acid, it is necessary to pay attention to heat preservation in cold regions in winter to prevent freezing and leakage of containers. Do not store and transport together with alkalis, toxic materials and other corrosive materials. Protect from rain and direct sunlight during transportation. Handle with care during loading and unloading. Violent impact is strictly prohibited to prevent the packaging container from rupturing. In case of fire, dry sand or dry powder fire extinguisher can be used for fighting.
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