International Journal of Mechatronics and Manufacturing Systems (5 papers in press)

Special Issue on: IJSM2023 Artificial Intelligence in Robotics and Manufacturing Automation

• Open Access

  Challenges in designing a human-centred AI system in manufacturing ( Free Full-text Access )

  by Yuji Yamamoto, Alvaro Aranda Muñoz, Kristian Sandström 
  Abstract: Despite successful AI system deployments in manufacturing, methodological support for developing and integrating AI systems into manufacturing processes remains underdeveloped. This paper aims to identify gaps in the methodological support for the early design phase of AI system development in manufacturing. The study reveals the thinking-level challenges that design participants face in the early design phase and identifies remedies for those challenges, which are only superficially addressed in current manufacturing literature. The paper contributes to uncovering the current knowledge gap in developing an actionable methodology for AI system development in manufacturing contexts.
  Keywords: human-centred AI; manufacturing; AI system design; machine learning; socio-technical systems; design guidance.
  DOI: 10.1504/IJMMS.2024.10068969
   
  
• Trimming process sheet thickness characterisation with mel-frequency cepstral coefficients and an artificial neural network  
  by Tushar Y. Badgujar, Harshal A. Chavan, Shubham R. Suryawanshi, Vijay P. Wani 
  Abstract: Sheet metal trimming is one of the important manufacturing processes in modern industry. Shear stress in the trimming process has a significant impact on material behaviour, tool wear, and product quality. When other parameters remain unchanged, the thickness of the sheet metal determines the shear stress, so it becomes important to maintain thickness within a predetermined range. The present study aims to monitor sheet thickness variation in the trimming process online using the wavelet transform as a signal filter, the mel-frequency cepstral coefficients (MFCC’s) as a feature, and artificial neural networks (ANNs) as a classifier. Experimental results showed accuracy of 91.63% and 89.5% with pre-recorded acoustic signals and experimental trials, respectively. The proposed online sheet thickness monitoring system has the potential to improve productivity by reducing inspection time and providing insight into shear stress.
  Keywords: sheet metal trimming; shear stress; MFCC; mel-frequency cepstral coefficient; ANN; artificial neural networks; online monitoring.
  DOI: 10.1504/IJMMS.2024.10068286
   
  
• Artificial intelligent denoising spectrograms approach for enhanced chatter detection in robotic machining  
  by Dialoke Ejiofor Matthew, Hongrui Cao, Jianghai Shi 
  Abstract: Accurate chatter detection becomes vital to preventing chatter issues. Because of the complicated dynamics involved, chatter can be particularly difficult to identify and reduce in robotic machining, where robotic arms are used for material removal operations. In order to detect chatter in robotic machining, this paper provides an artificial intelligence (AI) approach called attention-based denoising and adaptive thresholding methods. A potent method for spectrogram denoising, which provides flexibility to adaptively focus on pertinent information while maintaining significant characteristics. By utilising an efficient attention mechanism that can extract pertinent information from the input data, it provides a high-resolution spectrogram in comparison to the conventional method. The efficiency of the proposed method was demonstrated by a series of experimental tests. The average entropy of the spectrograms generated by the conventional and proposed methods is found to be 12.66 and 10.01, respectively.
  Keywords: attention-based denoising; chatter detection; robotic milling; VMD; variational mode decomposition; machining dynamics.
  DOI: 10.1504/IJMMS.2024.10068970
   
  
• A critical review of contribution of evolutionary techniques to machining parameter optimisation  
  by Uday Shanker Dixit, Faladrum Sharma 
  Abstract: Optimisation of machining processes has been an active research area for over six decades. In the last three decades, several evolutionary optimisation methods have been utilised. This paper offers a brief history of optimisation in machining followed by a detailed discussion on the application of evolutionary optimisation methods. The focus is narrowed down to traditional machining processes, where a wedge shaped material removes the material in the form of chips. The paper highlights the challenges in choosing a suitable algorithm, given the empirical nature of available comparative studies lacking a solid mathematical basis. Further research is needed to understand the similarities and contrasts among evolutionary optimisation methods. It is also prudent to focus on the proper modelling of the processes (preferably with the help of big data), integrating the machine tools with proper sensors and developing strategies for online optimisation.
  Keywords: evolutionary optimisation; machining; soft computing; nonlinear programming; genetic algorithm; PSO; particle swarm optimisation.
  DOI: 10.1504/IJMMS.2024.10066233
   
  
• Optimising data driven value creation in industrial services incorporating circularity  
  by Jürg Meierhofer, Jochen Wulf, Viola Gallina, Barna Gal, Stefanie Eisl 
  Abstract: In the context of twin transition, companies encounter complex challenges. In the manufacturing sector, efficient utilisation of economic resources is crucial. Climate change and environmental pressures are driving companies to adopt Circular Economy (CE) business models that emphasise remanufacturing and product-service systems (PSS). However, specific guidelines for implementing these business models are lacking. In this study, we analyse CE business models based on remanufacturing and PSS to identify key elements needed to set up these specific models. We conduct a case study analysis and draft a comprehensive list of configuration options for both remanufacturing and PSS business models. Our results can assist manufacturing companies in reshaping their value proposition, delivery, creation, and capture toward a CE based on remanufacturing and PSS. The model integrates perspectives from providers, customers, and society. Numerical evaluations demonstrate that economic and environmental value creation can be simultaneously achieved and optimised through specific service arrangements.
  Keywords: PSS; product-service system; remanufacturing; sustainability; data driven value creation.
  DOI: 10.1504/IJMMS.2024.10068971