Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm

Wang, Hao-nan; He, Qiqi; Zhang, Zheng; Peng, Tao; Tang, Renzhong

doi:10.1631/jzus.a2000480

Cited by 27 publications

(11 citation statements)

References 40 publications

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“…After checking the control table, it can be seen that G7 and G8 processes of the production line are three-level processing. Compared with G2 and G6 processes, the unqualified rate is 4.55> P 0.27, which shows that the screening improves the yield [10].…”

Section: Improvement Plan Of Yield Ratementioning

confidence: 90%

Research on Lean Improvement of a Powder Production Line Based on Contradiction Analysis

Guo,

Chen,

Long

et al. 2023

J. Phys.: Conf. Ser.

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The intelligent improvement of production and manufacturing plays an important role in providing automated production for a company. In response to the problems of low production efficiency, high defect rate, and slow mold change in a certain company’s powder production site, this study combines the ideas of quality management and lean improvement, and analyzes the equipment utilization and production capacity through methods such as contradiction analysis, process function analysis, and fishbone diagram, By using Flexsim simulation and analytic hierarchy process, an optimization plan was obtained, which shortened the processing time and mold change time, improved the production capacity of the production line, and provided specific solutions and new ideas for the company’s production and operation. This helps to improve the technical performance of process oriented manufacturing production.

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Section: Improvement Plan Of Yield Ratementioning

confidence: 90%

Research on Lean Improvement of a Powder Production Line Based on Contradiction Analysis

Guo,

Chen,

Long

et al. 2023

J. Phys.: Conf. Ser.

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“…In addition, we divide the first 70% of the order into the training set, 15% into the validation set, and the last 15% into the test set and then normalize the divided data to [ 0,1]. In order to determine the optimal number of nodes in the hidden layer (succession layer), this paper uses the following pseudocode for calculation: for a � 1:10 hiddennum � fix(sqrt(inputnum + outputnum))+a; net � newelm(inputnum, outputnum,hiddennum,{"tansig", "purelin"}, "traingdx"); net.trainParam.epochs � 10000; net.trainParam.lr � 0.01; net.trainParam.goal � 0.00001; net � train(net,inputn, outputn); an � sim(net,inputn); mse11 � mse(outputn,an); if mse11<1e+05 hiddennum_best � hiddennum; break; end end By substituting the data in Table 3 into the calculation, it can be obtained that the optimal number of nodes in the hidden layer (succession layer) is 3. erefore, when predicting the demand of products, the input layer, hidden layer, successor layer, and output layer of the Elman neural network are, respectively, [4,3,3,4]. Similarly, when predicting the relevant parameter variables of the failure rate of the equipment, we substitute the data in Table 4 into the calculation according to the same process, and we can obtain the input layer, hidden layer, succession layer, and output of the Elman neural network under different equipment failure rate parameters.…”

Section: Simulation Analysismentioning

confidence: 99%

“…is paper uses MATLAB for programming. Taking the market demand as an example, we substitute the data in Table 3 into the constructed Elman neural network whose input layer, hidden layer, successor layer, and output layer are [4,3,3,4], respectively, and the pass "trainingdx" function of the adaptive learning algorithm with momentum term starts training.…”

Section: Simulation Analysismentioning

confidence: 99%

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A Model Predictive Control for Lot Sizing and Scheduling Optimization in the Process Industry under Bidirectional Uncertainty of Production Ability and Market Demand

Tang

Wang

2022

Computational Intelligence and Neuroscience

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In the face of bidirectional uncertainty of market demand and production ability, this paper establishes a multiobjective mathematical model for lot sizing and scheduling integrated optimization of the process industry considering both material network and production manufacturing and finds the optimal decision of the model through model predictive control to minimize total completion time and total production cost. While realizing the model predictive control proposed in this paper, the Elman neural network predicts the relevant parameters required by learning historical orders for the uncertain market demand and equipment production ability. Then, the calculation formulas of product supply and demand matching and equipment production ability are formed and introduced into the next stage of the model as a constraint condition. In addition to the above constraints for constructing lot sizing and scheduling integrated models in the process industry, this paper also considers both the material network and production manufacturing and uses the IMOPSO algorithm to solve the problem iteratively. So far, a complete model predictive control can be generated. Through the model predictive control, the production system can respond in advance, make appropriate changes to offset the foreseeable interference, and obtain the lot sizing and scheduling scheme considering bidirectional uncertainty, thereby improving the system’s overall robustness. Finally, this paper realizes the model's predictive control process through example simulation and analyzes the operation results combined with the scheduling Gantt chart to verify the applicability and effectiveness of the model.

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“…According to the requirement of users, intelligent furniture can make intelligent decisions and actions [4]. Thus, intelligent furniture is favored by more and more consumers due to its unique convenience and intelligence [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Current Status and Development Trends of Chinese Intelligent Furniture Industry

Xiong¹,

Yue²,

Zhang³

2023

Journal of Renewable Materials

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In this work, the current status, technical capabilities, and development trends of the Chinese intelligent furniture industry were in focus. Based on combining a literature review with field investigations and analysis of major scientific research projects in Zhejiang Province, China, an in-depth overview and discussion about previous experience, features, technologies, products and control methods in the intelligent furniture industry in China were conducted. The key technologies in current Chinese intelligent furniture industry include embedded systems, sensors, short-range wireless communication, artificial intelligence and intelligent interaction techniques. This work also mentions the challenges and opportunities for the industry, pointing out how to innovate and dominate the furniture market in the era of transformation from the traditional furniture industry to intelligent upgrading. The results are proposed to help readers from all over the world to understand the development trend and progress of the Chinese intelligent furniture industry.

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Framework of automated value stream mapping for lean production under the Industry 4.0 paradigm

Cited by 27 publications

References 40 publications

Research on Lean Improvement of a Powder Production Line Based on Contradiction Analysis

Research on Lean Improvement of a Powder Production Line Based on Contradiction Analysis

A Model Predictive Control for Lot Sizing and Scheduling Optimization in the Process Industry under Bidirectional Uncertainty of Production Ability and Market Demand

Current Status and Development Trends of Chinese Intelligent Furniture Industry

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