Knowledge-based multi-level aggregation for decision aid in the machining industry

Abstract

In the context of Industry 4.0, data management is a key point for decision aid approaches. Large amounts of manufacturing digital data are collected on the shop floor. Their analysis can then require a large amount of computing power. The Big Data issue can be solved by aggregation, generating smart and meaningful data. This paper presents a new knowledge-based multi-level aggregation strategy to support decision making. Manufacturing knowledge is used at each level to design the monitoring criteria or aggregation operators. The proposed approach has been implemented as a demonstrator and successfully applied to a real machining database from the aeronautic industry. Decision Making; Machining; Knowledge based system

Developing the Adaptive Knowledge Management in Context of Engineering Company Project Activities

“…Comparing the received results with existing works, the research of adaptive learning in [1,4,5] mainly focused on improving team learning achievements based only on single-source of personalization information, such as learning style, cognitive style or learning achievement. In article [6], an innovative adaptive learning approach is proposed, which is based on two main sources of personalization information: learning behavior and personal learning style.…”

“…The concept and nature of knowledge are at the center of the interests of the two theories, both learning and knowledge management. In articles [4,5] knowledge is linked with learning. Knowledge is defined as "the possibility or potential for action or decision-making by a person, group or organization".…”

A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective

“…Then, using data mining methods, machining data are analyzed and useful information is extracted (Godreau et al 2017) (Lenz et al 2018). Contextual classification and aggregation phases generate more meaningful data, called Smart Data, that can detect events that occur during machining (Ritou et al 2019). Smart Data are then used to generate Key Performance Indicators (KPIs) that describe the machining process and its potential detrimental phenomena.…”

A generic knowledge management approach towards the development of a decision support system

“…The machine's computer numerical control (CNC) provides additional information related to the operational context of manufacturing process (date and time of operation, working piece reference, program reference, operating mode, etc.). These heterogeneous data are then subjected to several aggregations and data mining operations to generate a new category of data called Smart Data (Ritou et al 2019). The use of these processing methods adds a new semantic layer representing links between the initial raw data.…”

“…T h ei n i t i a ls t a g ew a sm a d ep r i o rt ot h i sr e s e a r c hw o r k and concerns the defin tion of specifi data mining algorithms to analyse raw data and extract smart data (Wang et al 2019). This leads to the generation of traceability data and the management of heterogeneous reports (Ritou et al 2019). Together, these latter two publications explain the various operations required for the preliminary stage.…”