Disassembly issues have been widely attracted in today’s sustainable development context. One of them is the selection of disassembly tools and their efficiency comparison. To deal with such issue, taking the bolt as a removal object, this work designs their removal experiments for different removal tools considering some factors influencing its removal process. Moreover, based on the obtained experimental data, the removal efficiency for different removal tools is optimized by a hybrid algorithm integrating neural networks (NN) and genetic algorithm (GA). Their efficiency comparison is discussed. Some numerical examples are given to illustrate the proposed idea and the effectiveness of the proposed methods.
Rapidly growing mechanical and electrical waste has long been known to cause severe environmental problems. A major contributing factor of this is the rapid technological development of new products and an increasing demand for the latest technology by the consumers. The desire to obtain the latest technology often leads to premature purging of still functioning products. This, in turn, has led to a diminishing number of landfills and rapid depletion of virgin resources [
Disassembly is defined by Brennan et al. [
Gungor and Gupta present an evaluation and planning methodology to choose the best disassembly process among several alternative processes based on the total time for disassembly [
Based on the above overview, the current research mainly focuses on the integrated disassemblability evaluation and design method incorporating one or multiple disassembly influence factors. In fact, to some extent, a factor has a significant impact on the disassembly decision making and design guideline of DFD (design for disassembly). Motivated by these, factor analysis issues on disassembly are discussed. For example, Tang and Zhou discuss the influence of operation fluency of human operators on disassembly using fuzzy logic [
Although some researchers have addressed some influence factors on disassembly, that is, the fluency of human operators, connection type, and ergonomic factor, they pay little attention to the tool type, which is one of the key factors of product disassembly. This work addresses disassembly efficiency optimization and their comparison for different disassembly tools using experiment and simulation methods.
The rest of this paper is organized as follows. Section
By taking the specified bolt of a transmission as an objective, this work obtains its removal times of different removal tools as presented hereinafter.
In this work, the M17 bolt of a transmission is taken as an objective to be removed. Its basic parameters are presented as follows: the bolt type is hexagonal, the maximum nominal diameter is 12 mm, the number of pitches is 12, and the thread pitch is 1.82 mm.
There are four types of equipment used in this experiment, that is, wrench for tightening, dynamometer for removing, stopwatch, and removal tools. Wrench for tightening is used to fasten the bolt and demarcate the tightening torque of the bolt. Dynamometer for removing is applied to measure the maximum tension of operators. Stopwatch is used to measure the removal operation time of the bolt. Removal tools are composed of two types of tools, which are used to remove the bolt. One is a ratchet wrench, as shown in Figure
Removal tools. (a) Tool I: ratchet wrench; (b) tool II: general wrench (round end).
In the removal experiment, we consider only two factors, that is, the removal condition of the bolt and the quality of removal operators.
In terms of the removal condition of the bolt, it is simulated by the tightening torque of the bolt. It is demarcated by three levels, that is, 25 Nm, 50 Nm, and 75 Nm. Note that Nm is the unit of torque, that is, Newton metre.
In terms of the quality of operators, during the removal experiment, since 5 skilled ones are selected, their operation fluency is considered as being high. Based on this premise, the quality of operators is determined by their maximum tension used. Usually, the larger of the maximum tension an operator, the better their ability of disassembly, and the smaller the needed disassembly time of disassembling a product. In this experiment, it is demarcated by five levels, respectively, that is, 28 kgf, 24 kgf, 20 kgf, 15 kgf, and 12 kgf. Note that the kgf (kilogram force) is the unit of tension (1 kgf = 9.8 N).
In this experiment, based on different tightening torque levels of the bolt and quality levels of removal operators, the corresponding 15 data points of removal standard time for two types of tools are obtained, as shown in Table
Removal experimental data table.
Exp. |
Tightening |
Maximum tension |
Standard time (second) | |
---|---|---|---|---|
Tool I | Tool II | |||
1 | 25 | 28 | 18.07 | 29.56 |
2 | 50 | 28 | 18.12 | 30.49 |
3 | 75 | 28 | 22.34 | 34.37 |
4 | 25 | 24 | 18.06 | 30.77 |
5 | 50 | 24 | 18.56 | 31.02 |
6 | 75 | 24 | 22.38 | 35.73 |
7 | 25 | 20 | 18.25 | 29.18 |
8 | 50 | 20 | 18.94 | 30.86 |
9 | 75 | 20 | 23.17 | 37.04 |
10 | 25 | 15 | 18.21 | 33.41 |
11 | 50 | 15 | 18.80 | 34.71 |
12 | 75 | 15 | 23.65 | 39.92 |
13 | 25 | 12 | 19.05 | 34.05 |
14 | 50 | 12 | 20.17 | 34.87 |
15 | 75 | 12 | 24.63 | 40.39 |
Although we obtain the experimental data, the smallest one is not optimal in certain conditions. To obtain the optimal one, thus it is essential to optimize them based on experimental data. NN-GA is a hybrid intelligent optimization algorithm integrating neural networks (NN) and genetic algorithm (GA). Since it makes full use of the nonlinear fitting ability of NN and the nonlinear fitting ability optimization ability of GA, it is proved to be effective and feasible to deal with nonlinear and discrete optimization issues, that is, nonlinear structural and creep feed grinding optimization problems [
A neural network is treated as a nonlinear mapping system consisting of neurons (processing units), which are linked by weighted connections. It usually consists of three layers: input, hidden, and output layers [
Firstly, the method to determine the number of neurons of the input, hidden, and output layers is presented as follows.
The number of input neurons of the NN structure is the number of input variables, namely, two influence factors; thus the number of input neurons is 2.
The number of output neurons is one representing one removal time function.
In terms of the NN structure, the main problem is to determine the best number of hidden neurons. The number of hidden neurons can be infinite in theory but finite in practice due to two reasons. Too many hidden neurons increase the training time and response time of the trained NN. On the other hand, too few hidden neurons make the NN lack generalization ability [
Secondly, backpropagation is the most commonly used method to calculate values for the weight and bias terms of the neural network model. In the backpropagation method, all weights are adjusted according to the calculated error term using a gradient method. Learning in an NN, that is, the calculation of the weights of the connections, is achieved by minimizing the error between its output and the actual output over a number of available training data points. In this work, the error term is controlled by the following MATLAB function, namely,
The NN algorithm is presented as follows.
Genetic algorithms (GA) form a class of adaptive heuristics based on principles derived from the dynamics of natural population genetics [
Produce randomly pop_size initial population
Based on a chromosome obtained by the initialization process, weights and thresholds of the NN are assigned. If specified training data are input, forecast output of the trained NN can be obtained. The inversed function of the sum of absolute differences between forecast outputs of the trained NN and actual outputs is considered as the fitness value
A selection operation is implemented by pinning the roulette wheel method; that is, a selection strategy is executed according to the fitness value. Thus, the selection probability
A crossover operation is implemented by a real number crossover method. The crossover operation in position
A mutation operation is implemented. The detailed mutation operation of the
The GA algorithm usually has the following steps.
NN-GA is a hybrid intelligent optimization algorithm integrating neural networks (NN) and genetic algorithm (GA). Its basic idea is described as follows. Firstly, based on the feature of actual problem and related knowledge of NN, an appropriate NN model is established and its prediction result is output. Additionally, the prediction output is considered as the fitness value of a chromosome of GA, and then its selection, crossover and mutation operations are executed accordingly. When the given maximum generation is reached, the algorithm ends. The best chromosome is assigned to the optimal solution of the corresponding problem. Its basic steps are presented hereinafter [
In addition, its flow chart is presented, as shown in Figure
Flow chart of NN-GA algorithm.
The above algorithms have been implemented in the MATLAB (R2009b) programming language.
Based on the obtained experimental data with different removal tools, combined with the above-mentioned NN-GA algorithm, removal efficiency (time) optimization and their comparison for two types of removal tools are presented hereinafter.
The parameter of NN-GA algorithm is set as follows: the population size pop_size is 30, the probability of crossover
First, the NN is trained using 11 groups of data randomly selected from Table
Comparative analysis between experimental data and test result of NN for tool I.
Data |
Experiment |
Test result | Error | Relative error |
---|---|---|---|---|
1 | 18.06 | 17.52 | 0.54 | 3.00 |
2 | 23.17 | 21.53 | 1.64 | 7.07 |
3 | 19.05 | 18.26 | 0.7888 | 4.15 |
4 | 22.34 | 22.35 | 0.01 | 0.05 |
Based on the results of Table
Initialize 30 chromosomes of GA and their fitness values are calculated by the trained NN, the fitness curve of the best individual can be obtained after the GA algorithm is executed, which is shown in Figure
The fitness curve of best individual of disassembly efficiency optimization for tool I.
After the algorithm is run, the optimal solution, that is, 16.99, and the optimal individual, that is,
Similarly, after the NN is trained, the test results of 4 test data of tool II are shown in Table
Comparative analysis between experimental data and test result of NN for tool II.
Data |
Experiment |
Test result | Error | Relative error |
---|---|---|---|---|
1 | 35.73 | 35.29 | 0.44 | 1.2 |
2 | 39.92 | 39.26 | 0.66 | 1.6 |
3 | 30.49 | 29.64 | 0.85 | 2.8 |
4 | 34.87 | 36.97 | 2.10 | 5.7 |
Based on the results of Table
According to the trained NN, after the algorithm is run, the optimal solution, namely, 27.15, and the optimal individual, namely,
The fitness curve of the best individual of disassembly efficiency optimization for disassembly tool II.
In order to measure the removal efficiency of different removal tools, the removal efficiency ratio of one tool to another tool is defined in this paper.
Removal efficiency ratio of one tool to another tool is defined as the increased or decreased ratio of removal efficiency of one tool to another tool. Mathematically,
Based on the defined removal efficiency ratio, combined with the optimal results of the removal efficiency of two different tools, the comparison of their removal efficiency is presented.
According to the obtained optimal results of removal efficiency of two different tools in Section-A and Section-B, their removal efficiency ratio is obtained, namely
Disassembly tools selection and their disassembly efficiency analysis play an important role in the product disassembly decision making. In order to guide decision makers in making better disassembly decisions, this work presents the optimization and comparison of removal efficiency for different removal tools for the first time. Firstly, taking the bolt as an object to be removed, this work designs removal experiments by considering some factors influencing the removal process for two types of removal tools. Secondly, based on the obtained experimental data, a NN-GA algorithm integrating neural networks (NN) and genetic algorithm (GA) is proposed to optimize the removal efficiency of two different removal tools. From the results, we can see that the efficiency of tools I and II is improved by 6.96% and 5.92%, respectively. It denotes that the disassembly efficiency of disassembly tool is effectively improved by the optimization of integrated NN-GA algorithm.
The future work is to find and use actual industrial disassembly data to validate this method to provide the best decision support for disassembly practice. In addition, in the future we should study the control issue of disassembly process based on related theories [
This work is financially supported by the Postdoctoral Science Foundation Project of China under Grant no. 2013M541329, Youth Academic Backbone Project for University of Heilongjiang Province, and Student Research Training Project under Grant no. KY2013006.