With the advances in new-generation information technologies, smart process planning is becoming the focus for smart process planning with less time and lower cost. Big data-based reusing and evaluating the multi-dimensional process knowledge is widely accepted as an effective strategy for improving competitiveness of enterprises. However, there was little research on how to reuse and evaluate process knowledge with dynamical changing machining status. In this paper, we propose a novel digital twin-based approach for reusing and evaluating process knowledge. First, the digital twin-based process knowledge model which contains the geometric information and real-time process equipment status is introduced to represent the purpose and requirement of machining planning. Second, the process big data is constructed based on the three-layer and its association rules for accumulating process knowledge. Moreover, the similarity calculation algorithm of the scene model is proposed to filter the unmatched process knowledge. For accurately reusing the process knowledge, the process reusability evaluation approach of the candidate knowledge set is presented based on the real-time machining status and the calculated confidence. Finally, the diesel engine parts are applied in the developed prototype module to verify the effectiveness of the proposed method. The proposed method can promote the development and application of the smart process planning.

Digital twin-based process reuse and evaluation approach for smart process planning

The concept of smart manufacturing has become an important issue in the manufacturing industry since the start of the twenty-first century in terms of time and production cost. In addition to high production quality, a quick response could determine the success or failure of many companies and factories. One the most effective concepts for achieving a smart manufacturing industry is the use of computer-aided process planning (CAPP) techniques. Computer-aided process planning refers to key technology that connects the computer-aided design (CAD) and the computer-aided manufacturing (CAM) processes. Researchers have used many approaches as an interface between CAD and CAPP systems. In this field of research, a lot of effort has been spent to take CAPP systems to the next level in the form of automatic computer-aided process planning (ACAPP). This is to provide complete information about the product, in a way that is automated, fast, and accurate. Moreover, automatic feature recognition (AFR) techniques are considered one of the most important tasks to create an ACAPP system. This article presents a comprehensive survey about two main aspects: the degree of automation in each required input and expected output of computer-aided process planning systems as well as the benefits and the limitations of the different automatic feature recognition techniques. The aim is to demonstrate the missing aspects in smart ACAPP generation, the limitations of current systems in recognising new features, and justifying the process of selection.

/pdf/a-survey-on-smart-automated-computer-aided-process-planning-2c8uvjxqwi.pdf

A survey on smart automated computer-aided process planning (ACAPP) techniques

As businesses started to embrace Industry 4.0, along came the Fifth Industrial Revolution. Industry 5.0 complements the existing Industry 4.0 paradigm for the not-too-distant future by highlighting research and innovation as drivers for a transition to a sustainable, human-centric, and resilient industry. The core values of Industry 5.0 including human-centricity, sustainability, and resilience have prompted formal discussions that manufacturing should be human-centric. Human-centric smart manufacturing (HSM) takes full advantage of human flexibility, machine precision, and new-generation information technologies to construct a super smart, sustainable, and resilient manufacturing system. Nowadays, extensive research on the concept, architecture, enabling technologies, and applications of smart manufacturing have been conducted. However, research on HSM is relatively lacking, yet starting to gain traction rapidly. Hence, this paper primarily carries out a systematic literature review on HSM to identify promising research topics with high potential for further investigations. Firstly, this paper reveals the concept and connotation of HSM, on which the state-of-the-art architecture of HSM is introduced. Then, several promising Industry 4.0 technologies are identified as the key enablers that can continue to support the construction and operation of HSM, where a comprehensive survey and discussion on these technologies are conducted. Thirdly, what’s new in HSM applications and how HSM will reshape the entire lifecycle of industrial products are explored. Finally, this paper identifies limitations, barriers, and challenges that will be encountered during the developing, operating, and maintaining HSM, and provides valuable research directions to continuously improve HSM. 

Towards new-generation human-centric smart manufacturing in Industry 5.0: A systematic review

The transition to intelligent manufacturing provides a fulcrum for the revolution of product lifecycle like design, manufacturing and maintenance, so does it for process planning. Specifically, digital twin manufacturing cell (DTMC) is regarded as a new means of and also a basic unit for implementing intelligent manufacturing. Incorporating process planning in DTMC could improve the integrity of DTMC and enhance the feasibility of process planning. Consequently, this paper proposes a deep learning-enabled framework for intelligent process planning towards DTMC. Firstly, a process knowledge reuse network (PKR-Net) that takes deep residual networks as base architecture is embedding into the framework, which could understand design intents expressed in a drawing or a 3D computer-aided design (CAD) model via its views and automatically retrieve relevant knowledge for the quick generation of theorical processes. Then, an evaluation twin is constructed to transform the theorical processes into practical operations and produce an optimal process plan. Finally, a test bed of the framework is constructed and the experimental results demonstrate the feasibility and effectiveness of the approach.

Deep learning-enabled intelligent process planning for digital twin manufacturing cell

Computer-aided process planning is the bridge between computer-aided design and computer-aided manufacturing. With the advent of the intelligent manufacturing era, process knowledge is important for process planning. Knowledge graph is a semantic representation method of knowledge that has attracted extensive attention from the industry and academia. Process planning using the process knowledge graph has become an important development direction for computer-aided process planning. From the analysis of the published reviews, there have been many computer-aided process planning reviews with different focuses. We focus on the techniques and applications of knowledge graph in manufacturing process planning. Therefore, this paper comprehensively reviews knowledge graphs in manufacturing process planning. We analyze the key technologies of process knowledge graph, including process knowledge representation, process knowledge extraction, process knowledge graph construction, process knowledge graph refinement, process knowledge graph validation, and process generation. We also explore the combination of process knowledge graphs and large language models. Finally, potential future research directions are proposed. 

Knowledge graph-based manufacturing process planning: A state-of-the-art review

Multi-tasking machine is capable of performing both milling and turning operations, it contributes to highly efficient machining and space conservation. However, prior to machining a lot of lead time is consumed in deciding efficient process plan, and generating machining tool path. Although the current CAM systems are highly integrated, the efficiency of the generated tool path is highly relied on the experience of the CAM programmer. In this research, an automatic process planning system for multi-tasking machine was developed. It is capable of recognizing manufacturing features and deciding efficient process plan from CAD model automatically. In this developed system, the CAD model is described as Attributed Adjacency Graph (AAG), and each feature is defined by AAG and its geometrical properties. Totally 8 milling features and 9 turning features can be recognized. The optimal machining plan is calculated based on machining cost evaluation. In addition, in this research a new method based on subfeature combination is proposed in order to recognize intersecting features. Furthermore, in this research the connection relationship of each feature is classified, and machining priority is assigned to adjacent features. It prevents the time consuming evaluation for checking all possible machining sequences. Finally, according to the experiment results, it is confirmed this developed system is capable of obtaining optimal machining plan properly and rapidly.

https://www.jstage.jst.go.jp/article/jamdsm/9/3/9_2015jamdsm0034/_pdf

Graph based automatic process planning system for multi-tasking machine

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Graph based automatic process planning system for multi-tasking machine