Selection and sequencing of machining processes for prismatic parts using process ontology model

Kang, Mi Jeong; Kim, Gyungha; Lee, Taemoon; Jung, Chang Ho; Eum, Kwangho; Park, Myon Woong; Kim, Jae Kwan

doi:10.1007/s12541-016-0048-2

Cited by 15 publications

(3 citation statements)

References 8 publications

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“…Rule-based reasoning method Y. Zhang et al [12] Ma et al [13] Kang et al [14] This method improves the efficiency of process planning systems, but predefined rules may cause confusion due to conflicting rules.…”

Section: Researchers Characteristicsmentioning

confidence: 99%

Process Planning for Large Container Ship Propeller Shaft Machining Based on an Improved Ant Colony Algorithm

Du,

Ma,

Bai

et al. 2024

JMSE

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To accommodate the production and manufacture of complex and customized marine components and to avoid the empirical nature of process planning, machining operations can be automatically sequenced and optimized using ant colony algorithms. However, traditional ant colony algorithms exhibit issues in the context of machining process planning. In this study, an improved ant colony algorithm is proposed to address these challenges. The introduction of a tiered distribution of initial pheromones mitigates the blindness of initial searches. By incorporating the number of iterations into the expectation heuristic function and introducing a ‘reward–penalty system’ for pheromones, the contradictions between convergence speed and the tendency to fall into local optima are avoided. Applying the improved ant colony algorithm to the process planning of large container ship propeller shaft machining, this study constructs a ‘distance’ model for each machining unit and develops a process constraint table. The results show significant improvements in initial search capabilities and convergence speed with the improved ant colony algorithm while also resolving the contradiction between convergence speed and optimal solutions. This verifies the feasibility and effectiveness of the improved ant colony algorithm in intelligent process planning for ships.

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Section: Researchers Characteristicsmentioning

confidence: 99%

Process Planning for Large Container Ship Propeller Shaft Machining Based on an Improved Ant Colony Algorithm

Du,

Ma,

Bai

et al. 2024

JMSE

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“…In the literature, the creation of models for generating routings can be rule-based (i.e., the user enters the model manually) or data-based (i.e., the model is created based on data using data analysis). On the one hand, rule-based creation is done for part manufacturing applications based on manufacturing feature models [23][24][25]. Here, a technology database is built that assigns a manufacturing technology and a processing station to individual manufacturing features.…”

Section: Previous Approaches For the Creation And Validation Of Syste...mentioning

confidence: 99%

Creation and validation of systems for product and process configuration based on data analysis

Frey

May

Lanza

2022

Prod. Eng. Res. Devel.

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In the course of increasing individualization of customer demand, configurable products are gaining importance. Nowadays, variant-specific bills of materials and routings for configurable products are created with the help of rule-based configuration systems, so-called low-level configuration systems. The rules and generic structures on which such configuration systems are based are created manually today. This is challenging because it can be difficult and sometimes impossible to directly transfer expert knowledge into those systems. Furthermore documents that have already been created by experts in the past such as bills of material and routings contain relevant information as well which may be exploited to compose configuration systems. However, in the literature, there are no approaches yet to systematically transfer expert knowledge into configuration systems or to consider existing documents. In addition, the creation of such configuration systems is prone to error due to their complexity. Although there are already numerous approaches to the formal testing of configuration systems, approaches based on data analysis to support the validation of such systems have not yet been considered. Therefore, in this paper an approach is presented to automatically create low-level configuration systems by means of exemplary variant-specific bill of materials and routings using machine learning. The super bill of materials and the super routing as well as the dependencies between the product characteristics and the components respectively the operations are learned. Furthermore, it is shown how errors in the input data as well as errors in the resulting low-level configuration system can be detected by means of anomaly detection.

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“…In [96], Kang et al develop an ontology-based representation model to select appropriate machining processes as well as the corresponding inference rules. The ontology is quantified in terms of features, process capability with relevant properties, machining process, and relationships between concepts.…”

Section: Other Applicationsmentioning

confidence: 99%

Ontology-Based Feature Selection: A Survey

2021

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The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

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Selection and sequencing of machining processes for prismatic parts using process ontology model

Cited by 15 publications

References 8 publications

Process Planning for Large Container Ship Propeller Shaft Machining Based on an Improved Ant Colony Algorithm

Process Planning for Large Container Ship Propeller Shaft Machining Based on an Improved Ant Colony Algorithm

Creation and validation of systems for product and process configuration based on data analysis

Ontology-Based Feature Selection: A Survey

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