Smart manufacturing platform based on input-output empirical relationships for process monitoring

IF 2.6 3区材料科学 Q2 ENGINEERING, MANUFACTURING

International Journal of Material Forming Pub Date : 2024-07-09 DOI:10.1007/s12289-024-01844-w

Giuseppina Ambrogio, Luigino Filice, Francesco Gagliardi

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Abstract

Intelligent monitoring and maintenance protocols are undoubtedly crucial for improving manufacturing processes. Accordingly, machine learning techniques and predictive control models have been customized and optimized to account for the specific characteristics of the processes under investigation. In this context, the management of manufacturing processes in a “smart way” requires the development of specific models based on input-output empirical data. The aim of the proposed research was to develop an easily customizable application integrated into a milling process executed at the laboratory level. The application was designed to identify and record the operator, the order and the specific work sequences. It also supports the operator in setting processing parameters according to the type of work sequence to be performed. The application analyses specific process outputs, such as the wear growth on the inserts of the cutter in relation to the main input process parameters: depth of cut, feed rate, and spindle speed. This analysis is implemented by leveraging empirical evidence.

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基于输入输出经验关系的智能制造平台，用于过程监控

智能监控和维护协议对于改进生产流程无疑是至关重要的。因此，我们对机器学习技术和预测控制模型进行了定制和优化，以考虑到所研究过程的具体特点。在这种情况下，要以 "智能方式 "管理生产流程，就需要开发基于输入输出经验数据的特定模型。拟议研究的目的是开发一种易于定制的应用程序，将其集成到在实验室执行的铣削过程中。该应用程序旨在识别和记录操作员、订单和具体工作顺序。它还支持操作员根据要执行的工作顺序类型设置加工参数。该应用程序分析特定的加工输出，例如与主要输入加工参数（切削深度、进给率和主轴转速）相关的刀具刀片磨损增长情况。该分析通过经验证据来实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Material Forming ENGINEERING, MANUFACTURING-MATERIALS SCIENCE, MULTIDISCIPLINARY

CiteScore

5.10

自引率

4.20%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal publishes and disseminates original research in the field of material forming. The research should constitute major achievements in the understanding, modeling or simulation of material forming processes. In this respect ‘forming’ implies a deliberate deformation of material. The journal establishes a platform of communication between engineers and scientists, covering all forming processes, including sheet forming, bulk forming, powder forming, forming in near-melt conditions (injection moulding, thixoforming, film blowing etc.), micro-forming, hydro-forming, thermo-forming, incremental forming etc. Other manufacturing technologies like machining and cutting can be included if the focus of the work is on plastic deformations. All materials (metals, ceramics, polymers, composites, glass, wood, fibre reinforced materials, materials in food processing, biomaterials, nano-materials, shape memory alloys etc.) and approaches (micro-macro modelling, thermo-mechanical modelling, numerical simulation including new and advanced numerical strategies, experimental analysis, inverse analysis, model identification, optimization, design and control of forming tools and machines, wear and friction, mechanical behavior and formability of materials etc.) are concerned.