An ontology-based modeling and CBR method for cable process planning

Purpose – In micro cold forming, the high degree of technological dependencies between manufacturing, quality inspection and handling technologies leads to an extremely complex planning of process chains. In addition, the lack of standardised processes and interfaces further complicates the planning. The paper aims to discuss these issues. Design/methodology/approach – In order to provide consistent and comprehensive planning of micro manufacturing processes, this paper discusses a method, which integrates the planning of process flows, the planning of technological dependencies and capabilities, as well as of the corresponding material flow. Findings – The paper presents the micro-process chain planning and analysis (μ-ProPlAn) framework. It consists of a specific modelling method, a simultaneous engineering procedure model for the model creation, as well as of methods for the analysis of technological dependencies and logistic key values along the modelled process chains. Research limitations/implications – As the results presented in this paper originate from an on-going research project, the paper focuses on a detailed presentation of the modelling methodology and the procedure model. Practical implications – In practice, the μ-ProPlAn framework provides process designers in the field of micro manufacturing with tools and methods to clearly depict the interdependencies between and within a product's different manufacturing stages. Originality/value – By following a simultaneous engineering approach, μ-ProPlAn aims to reduce the efforts in process design by supporting the design of manufacturing processes in the early stages of the product design and by providing suitable methods for the analysis of these process chains.

In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, taking the double-deck bottom segment of a ship as an example, the process knowledge map of the heterogeneous model is constructed to realize the rapid design of ship process, which shows that the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship.

In the world of electricity, transformers are widely used, both in the field of electric power and electronics. The use of transformers in the electric power system is used for each of its needs, for example the need for high voltage in the delivery of electrical power over long distances. Electrical equipment transformers are expensive and are very vital equipment. If the transformer is disturbed or in an abnormal condition, there may be an unexpected temporary cessation of electricity distribution and will cause losses for PLN and consumers who use electricity by disrupting the activities carried out. The Case Based Reasoning (CBR) method is a case-based reasoning system that uses previous experiences or cases so that they can solve new problems or cases. There are several stages in the case based reasoning method, including retrieve, reuse, revise, and retain. Case-based Reasoning (CBR) collects previous cases that are similar to the new problem and tries to modify the solution to fit the new case. The basic idea of ​​Case-Based reasoning is the assumption that similar problems have similar solutions. While this assumption is not always true, it does depend on many practical domains. Where the previous case experienced an overvoltage transformer damage, using the CBR method will look for similarities to the previous case with existing characteristics, the CBR method will look for the same case on the existing Knowladge so that the damage is quickly detected. From the characteristics of the damage that is on the transformer, it is easy to diagnose transformer damage using the CBR method and the results of the CBR method will find a ranking that is almost similar to the existing case.

Specification of software process measurement

The design, machining, and assembly processes used to manufacture molds are knowledge-intensive, costly, and time-consuming operations requiring considerable human resources. Mold manufacturing is a typical make-to-order system. The characteristics of new products (molds or products produced using molds) determine the process and resource plan for mold manufacturing. Therefore, it is essential to store and inherit knowledge regarding products or mold design and process planning. In this study, we extract product features from images of products. We measure the similarity between features of new products and those of products previously manufactured. The clustering method is used to search for similar processes and resources among previously manufactured products. Moreover, design characteristics such as mold type, structure, and size, obtained from design data, are used to support this search for similar processes and resources. Herein, we propose a framework for a similarity retrieval model based on results of the product and design feature extraction. Convolutional neural networks and an agglomerative hierarchical clustering algorithm are used to derive similar processes. Furthermore, we propose a semiautomatic procedure to construct a collaborative process model that includes product, process, and resource information. This collaborative process modeling supports the visualization and analyses of complex processes for new products. Through this study, we endeavor to construct a collaborative process model by formulating it as an optimization problem.

Reliability consideration in the design and analysis of cellular manufacturing systems

Integrating multi-granularity model and similarity measurement for transforming process data into different granularity knowledge

It is difficult to make optimal process planning for polishing product because of the complex processes and the multi-criteria, attributes and vagueness of process parameters. To solve this problem, this paper combines the methodologies of case based reasoning (CBR) and fuzzy set theory (FST) to support process planners in planning processes and making decisions effectively for polishing product. Moreover, various mathematical models are designed and integrated to the Web based portal system (WBPS) which supports the optimization computation of process parameter settings and case reasoning for polishing product. Finally, some cooker samples from the collaborating company have been collected to demonstrate the effectiveness of case based process planning (CBPP) model.

Aiming at the requirements of CAD/CAPP data and function integration, adopted modeling based on feature, the part model design is accomplished through calling standard features in the feature database or user defined feature (UDF). The design feature model is reconstructed into a machining feature model through mapping rules. The information of machining geometric and machining properties is packaged in machining feature. CAD model does not provide all the information necessary for process planning, such as the materials information and the accuracy information. These must be provided manually. All information establishes reference of the shape feature. The machining feature, the processing method and manufacturing resources are correlated through establishing decision rules of process knowledge stored in a knowledge base, to realize the automatic generation of feature-based machining procedures and operations. A process-planning tree is constructed through matching the feature process tree with a typical process template, to realize the automatic generation of the part process route. A complete process document can be obtained by manually editing the automatically generated processing route. The feasibility of the method is verified by a prototype system developed in Pro/E environment.

As the shipbuilding industry advances toward intelligent manufacturing, the design of ship sub-assembly welding processes faces significant challenges due to the fragmented and heterogeneously formatted nature of welding process knowledge, alongside a substantial dependence on engineers’ empirical expertise in process planning. To address these issues, a method for computing the similarity between ship welding cases was proposed by leveraging knowledge graphs (KGs) and a graph matching network (GMN). First, a KG of ship welding processes is constructed to enable standardized representation and unified management of process knowledge. Then, a multi-level GMN incorporating local normalization and segmented encoding is employed. The principal function of this network is to assess the similarity between welding cases, thereby facilitating the intelligent retrieval and reuse of process cases. Experimental results demonstrate that the proposed method can effectively identify analogous welding process cases, providing valuable technical support for the intelligent transformation of shipbuilding processes.

Product process design relies upon the process knowledge support acquired by process designers and enterprises. Process knowledge recommendation has been attached more importance by enterprises in that it can quickly provide process designers with accurate and appropriate process knowledge. In this paper, a “scene-label-classification” knowledge recommendation scheme for process knowledge graph is established, and a knowledge content matching degree calculation method supporting process knowledge recommendation is proposed. Design requirements are described by parameterized characteristics from various dimensions such as material, precision requirements. Based on knowledge coding, the attribute characteristics of process knowledge are uniformly identified and associated with requirement scene characteristics. A requirements-knowledge semantic vector space model is established, and a calculation method of matching degree between design requirements and knowledge content is proposed based on improved cosine distance. This scheme enables quickly positioning the corresponding process knowledge based on coding labels, and final process knowledge recommendation candidate set is obtained through matching degree calculation and filtering sort, which thus enables dynamic classification and appropriate recommendation of process knowledge.

Among all the influential factors, the service quality of machine tools is the most important one for workers to decide their machine tools selection in process planning. This paper proposes an evaluation model for service quality of machine tools, aiming to help workers select machine tools on a reasonable basis. An evaluation indicator system of machine tools’ service quality is established based upon a thorough investigation into how the service quality of machine tools manifest themselves. The evaluation indicator system includes the following five aspects: processing cost, processing time, machining precision, green attributes and reliability. The least square method is used to establish an optimization model that determines the weight of indicators based on subjective weight-giving and objective Weight-giving. Finally, a grey relational analysis is conducted to evaluate the service quality of machine tools. Thus we provided an effective decision method for workers to decide their machine tools selection in process planning.

The basic idea of case-based reasoning (CBR) is to retrieve and modify the most relevant prior case to match new requirements. In this paper, a framework for a process planning system for machining of axisymmetric parts using case-based reasoning is introduced. It is composed of four major components: retriever, modifier, simulator, and repairer. When a desired axisymmetric part is to be machined, the proposed system first retrieves the most relevant case from the case library as a plan candidate. Since the plan candidate is rarely the same as the desired one, the system performs modification, simulation, and reparation on it. The proposed system has been implemented on a Sun workstation using the ACIS geometric modeler and C++.

Joint optimization of product tolerance design, process plan, and production plan in high-precision multi-product assembly

The Cambridge Engineering Selector (CES) is a tool that focuses on material, process, and shape combinations of product design. The Resource Consumption Model (RCM) is a tool that focuses on production process resources, resource costs, production volume, production capacity, and manufacturing cycle time. Both tools help identify viable manufacturing process alternatives to meet stated manufacturing objectives.