Assembly sequence and path planning for monotone and nonmonotone assemblies with rigid and flexible parts

Masehian, Ellips; Ghandi, Somayé

doi:10.1016/j.rcim.2021.102180

Cited by 24 publications

(5 citation statements)

References 49 publications

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“…Removing the constant term [29], we define the i-th query's sparsity measurement as expressed by Equation (11).…”

Section: Encodermentioning

confidence: 99%

“…Utilizing traditional data-driven methods involves processing and predicting collected data, thereby establishing a mapping between the physical world and the digital world, known as a digital twin. Although these traditional data-driven methods exhibit strong operability (such as utilizing genetic algorithms to analyze assembly or disassembly sequences, among others) and can effectively improve overall assembly geometric errors, their accuracy and robustness are compromised when predicting errors in the assembly process of increasingly complex products [8][9][10][11]. To overcome the limitations of traditional data-driven methods, machine learning techniques have emerged as the mainstay in this field.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Assembly Geometric Errors Based on Transformer Neural Networks

Wang,

Li,

Liu

et al. 2024

Machines

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Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known as Predicting Assembly Geometric Errors based on Transformer (PAGEformer). This model accurately captures long-range assembly relationships and predicts final assembly errors. The proposed model incorporates two unique features: firstly, an enhanced self-attention mechanism to more effectively handle long-range dependencies, and secondly, the generation of positional information regarding gaps and fillings to better capture assembly relationships. This paper collected actual assembly data for folding rudder blades for unmanned aerial vehicles and established a Mechanical Assembly Relationship Dataset (MARD) for a comparative study. To further illustrate PAGEformer performance, we conducted extensive testing on a large-scale dataset and performed ablation experiments. The experimental results demonstrated a 15.3% improvement in PAGEformer accuracy compared to ARIMA on the MARD. On the ETH, Weather, and ECL open datasets, PAGEformer accuracy increased by 15.17%, 17.17%, and 9.5%, respectively, compared to the mainstream neural network models.

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“…Removing the constant term [29], we define the i-th query's sparsity measurement as expressed by Equation (11).…”

Section: Encodermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Assembly Geometric Errors Based on Transformer Neural Networks

Wang,

Li,

Liu

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…In Equation ( 2), during the assembly of a product, the assembly directions of the parts are determined based on the information provided in Table 1. When the assembly direction changes between two adjacent assembly parts, it incurs a certain amount of time and labor cost [46][47][48]. Each change in assembly direction increases the reversal count L c,p,i of the assembly.…”

Section: Number Of Assembly Direction Changesmentioning

confidence: 99%

An Assembly Sequence Planning Method Based on Multiple Optimal Solutions Genetic Algorithm

Wan,

Liu,

Qiu

et al. 2024

Mathematics

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Assembly sequence planning (ASP) is an indispensable and important step in the intelligent assembly process, and aims to solve the optimal assembly sequence with the shortest assembly time as its optimization goal. This paper focuses on modular cabin construction for large cruise ships, tackling the complexities and challenges of part assembly during the process, based on real engineering problems. It introduces the multiple optimal solutions genetic algorithm (MOSGA). The MOSGA analyzes product constraints and establishes a mathematical model. Firstly, the traditional genetic algorithm (GA) is improved in the case of falling into the local optimum when facing complex problems, so that it can jump out of the local optimum under the condition of satisfying the processing constraints and achieve the global search effect. Secondly, the problem whereby the traditional search algorithm converges to the unique optimal solution is solved, and multiple unique optimal solutions that are more suitable for the actual assembly problem are solved. Thirdly, for a variety of restrictions and emergencies that may occur during the assembly process, the assembly sequence flexible planning (ASFP) method is introduced so that each assembly can be flexibly adjusted. Finally, an example is used to verify the feasibility and effectiveness of the method. This method improves the assembly efficiency and the diversity of assembly sequence selection, and can flexibly adjust the assembly sequence, which has important guiding significance for the ASP problem.

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“…ASP ensures that the final product is safe and reliable in operations and reduces the production cost and time to market. As the assembly process accounts for approximately 20-30% of the manufacturing cost and total production time, it is essential to reduce the assembly time and cost through ASP [17] [29].…”

Section: Introductionmentioning

confidence: 99%

A Web-based Patient Management System in Teeth Segmentation Using Deep Learning

Lin,

Su,

Hsu

2024

CAD&A

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Assembly operations of products can be determined by assembly sequence planning to meet assembly criteria. Although the genetic algorithm (GA) is one of the common algorithms used to find the optimal sequence of components in the product assembly, the optimal sequence is selected after the elimination of sequences that do not meet constraints. There is a lack of research on the effect of different types of crossover operators on GA performance. This paper introduces applications of different GA operators in the search for the optimal product assembly sequence. Four versions of GA are evaluated for their performances by employing different crossover operators in the algorithm. The roulette wheel is used as the selection mechanism. The solutions are examined by the statistical analysis in three case studies. This investigation obtains the pros and cons of each method to select the most suitable GA crossover operator for solving this specific optimization problem.

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Assembly sequence and path planning for monotone and nonmonotone assemblies with rigid and flexible parts

Cited by 24 publications

References 49 publications

Predicting Assembly Geometric Errors Based on Transformer Neural Networks

Predicting Assembly Geometric Errors Based on Transformer Neural Networks

An Assembly Sequence Planning Method Based on Multiple Optimal Solutions Genetic Algorithm

A Web-based Patient Management System in Teeth Segmentation Using Deep Learning

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