New insights into the methods for predicting ground surface roughness in the age of digitalisation

Highlights

• The classification of ground surface roughness prediction methods are discussed.

• Numerous methods for the predicition of the ground surface roughness with their principles and limitations are presented.

• The development trend of the ground surface roughness prediction models are analyzed.

• A futuristic multi-information fusion system for surface roughness prediction in the age of digitalisation is proposed.

Abstract

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed.

Introduction

Grinding is a displacement or position-controlled processing method where the excess material from the workpiece surface is removed by using an abrasive tool. The advantages of this important precision machining technology over other processing methods such as turning and milling include high material removal rate, better surface finishes and longer production runs. With the incorporation of electrolytic in-process dressing (ELID), self-dressing of the wheel is possible to achieve a longer tool life. Grinding is now being increasingly used in the processing of high precision parts [1,2] and to machine ‘difficult to cut’ glass ceramics [3,4], particularly for the large science programs requiring Precision-at-scale fabrication of telescope mirrors. Ground surface quality is governed by many factors e.g. process variables, loop stiffness, environment, stochastic distribution of the abrasive grits on the grinding wheel). Researchers have therefore long been working to develop predictive models to analyze the effect of grinding parameters on the surface topography [5] as well as the grinding wheel and workpiece properties [6].

Surface roughness has a significant impact on the service life and reliability of mechanical products as it can directly affect the tribological conditions and thus the corrosion, wear, fatigue and similar other attributes of the workpiece [7]. Ground surface roughness depends on interactions of a multitude of factors [8,9] and these interactions depend on the process parameters of grinding, properties of the processed material and the grinding wheel. Predictive values of the ground surface roughness can guide the design of machine tools to make them Industry 4.0 compliant which will eliminate the downstream material wastage and excessive generation of grinding sludge [10].

This review article aims to provide new insights into the methods and strategies for predicting the roughness of ground surfaces. We review, analyze, and categorize extant research to provide detailed insights into the state-of-the-art and identify future research directions, thus providing a comprehensive reference for ground surface roughness prediction, especially considering the digitalisation tools currently available. This paper is organized as follows. In Section 2, we present the classification of ground surface roughness prediction models. We review methods based on the machining theory in Section 3. We present a review of prediction models based on the experimental design and analysis in Section 4. In Section 5, we discuss artificial intelligence (AI) methods that can be used to make the grinding process more robust and resilient. We discuss current challenges and future trends for predicting the ground surface roughness in Section 6 and conclude by offering remarks on the latest developments in Section 7.