Development of an ultrasonic vibration assisted minimum quantity lubrication system for Ti-6Al-4V grinding

Minimum quantity lubrication (MQL) is widely used in machining/grinding as a competent cooling-lubrication technique owing to its advantages in terms of better cooling, lubrication, and lower coolant consumption. Ultrasonic vibration can be used to enhance the efficiency of MQL system by atomising the cutting fluid into fine and uniform droplets. In this study, an ultrasonic vibration assisted MQL (UAV-MQL) system is indigenously developed to effectively atomise the cutting fluid using the ultrasonic vibration of a suitably designed horn. To check the effectiveness of the developed UAV-MQL system, a set of experiments have been conducted on Ti-6Al-4V alloy during surface grinding operation, and the results have been compared with dry, flood and air-assisted conventional MQL grinding process using soluble oil as a cutting fluid

Prospects of digital twin for dynamic life cycle assessment of smart manufacturing systems

Smart manufacturing systems are poised to revolutionize industrial processes by leveraging advanced technologies for increased efficiency and productivity. However, alongside these advancements, there is a growing imperative to address environmental sustainability concerns. Conventional static life cycle assessment (LCA) methods often provide valuable insights into the environmental impacts of such manufacturing systems but often fall short in capturing real-time data and dynamic system interactions. Further, using the digital twin technology, physical assets can be virtually replicated in order to monitor, evaluate, and improve the particular manufacturing system. The dynamic properties can be effectively brought to LCA investigations by utilizing this technique. This paper explores the prospects of integrating digital twin technology for facilitating the dynamic LCA to enable comprehensive and timely environmental performance evaluation of smart manufacturing systems. We discuss the concepts, technological components, and potential applications of digital twin-enabled dynamic LCA, along with challenges and future research directions

A digital twin-driven ultra-precision machining system

The demand for ultra-precision machining has expanded significantly across industries such as aerospace, automotive, electronics, and medical sectors. These industries require parts manufactured to micrometre tolerances in a timely and cost-effective manner. To address these demands, efforts have been focused on developing digital twin technology for ultra-precision machining, aimed at improving production accuracy and efficiency. One of the primary challenges in ultra-precision machining is time-consuming setup due to manual workpiece handling. Additionally, machining speeds are limited to mitigate dynamic errors, further prolonging production times. This paper proposes a digital twin system designed to automate workpiece handling and correct dynamic errors in real time to tackle these challenges. The proposed digital twin comprises two systems: one for controlling a collaborative robot arm (COBOT) to automate workpiece handling with corrective action, eliminating the need for manual loading and unloading; and another for controlling a hybrid mill to mitigate dynamic errors through real-time machine learning-based prediction of elastic deformation allowing for higher machining speeds. In this paper, the current progress is discussed, and a methodology for validating this digital twin system is proposed. The proposed validation process will involve machining microfluidic devices using the digital twin system, compared to conventional machining methods to assess the effectivenes

Towards next-gen smart manufacturing systems : the explainability revolution

The paper shares the author’s perspectives on the role of explainable-AI in the evolving landscape of AI-driven smart manufacturing decisions. First, critical perspectives on the reasons for the slow adoption of explainable-AI in manufacturing are shared, leading to a discussion on its role and relevance in inspiring scientific understanding and discoveries towards achieving complete autonomy. Finally, to standardize explainability quantification, a new Transparency–Cohesion–Comprehensibility (TCC) evaluation framework is proposed and demonstrated

Intrinsic and post-hoc XAI approaches for fingerprint identification and response prediction in smart manufacturing processes

In quest of improving the productivity and efficiency of manufacturing processes, Artificial Intelligence (AI) is being used extensively for response prediction, model dimensionality reduction, process optimization, and monitoring. Though having superior accuracy, AI predictions are unintelligible to the end users and stakeholders due to their opaqueness. Thus, building interpretable and inclusive machine learning (ML) models is a vital part of the smart manufacturing paradigm to establish traceability and repeatability. The study addresses this fundamental limitation of AI-driven manufacturing processes by introducing a novel Explainable AI (XAI) approach to develop interpretable processes and product fingerprints. Here the explainability is implemented in two stages: by developing interpretable representations for the fingerprints, and by posthoc explanations. Also, for the first time, the concept of process fingerprints is extended to develop an interpretable probabilistic model for bottleneck events during manufacturing processes. The approach is demonstrated using two datasets: nanosecond pulsed laser ablation to produce superhydrophobic surfaces and wire EDM real-time monitoring dataset during the machining of Inconel 718. The fingerprint identification is performed using a global Lipschitz functions optimization tool (MaxLIPO) and a stacked ensemble model is used for response prediction. The proposed interpretable fingerprint approach is robust to change in processes and can responsively handle both continuous and categorical responses alike. Implementation of XAI not only provided useful insights into the process physics but also revealed the decision-making logic for local predictions

Development of an ultrasonic vibration assisted minimum quantity lubrication system for Ti-6Al-4V grinding

Minimum quantity lubrication (MQL) is widely used in machining/grinding as a competent cooling-lubrication technique owing to its advantages in terms of better cooling, lubrication, and lower coolant consumption. Ultrasonic vibration can be used to enhance the efficiency of MQL system by atomising the cutting fluid into fine and uniform droplets. In this study, an ultrasonic vibration assisted MQL (UAV-MQL) system is indigenously developed to effectively atomise the cutting fluid using the ultrasonic vibration of a suitably designed horn. To check the effectiveness of the developed UAV-MQL system, a set of experiments have been conducted on Ti-6Al-4V alloy during surface grinding operation, and the results have been compared with dry, flood and air-assisted conventional MQL grinding process using soluble oil as a cutting fluid

Investigation of grinding performance in ultrasonic vibration assisted grinding of Ti-6Al4V alloy using minimum quantity lubrication

Minimum quantity lubrication (MQL) is an efficient cooling and lubrication technique usually used these days in grinding operation. It isfound to be advantageousfor improving the grinding performance in terms of reduced grinding forces, surface integrity and production cost since it offers better cooling, lubrication, and lower coolant consumption. Ultrasonic vibration assisted grinding (UAG) has also shown the improvement in the grinding performance owing to the change in the nature of cutting process in UAG. In this study, the grinding performance of UAG combined with MQL using soluble oil on Ti-6Al-4V alloy is studied through surface grinding experiments. The results show significant improvement in surface finish and reduction in grinding forces are achieved in UAG with MQL grinding process as compared with conventional dry and ultrasonic vibration assisted dry grinding