Integrating Operator Information for Manual Grinding and Characterization of Process Performance Based on Operator Profile

Abstract

Due to its high versatility and scalability, manual grinding is an important and widely used technology in production for rework, repair, deburring, and finishing of large or unique parts. To make the process more interactive and reliable, manual grinding needs to incorporate “skill-based design,” which models a person-based system and can go significantly beyond the considerations of traditional human factors and ergonomics to encompass both processing parameters (e.g., feed rate, tool path, applied forces, material removal rate (MRR)), and machined surface quality (e.g., surface roughness). This study quantitatively analyzes the characteristics of complex techniques involved in manual operations. A series of experiments have been conducted using subjects of different levels of skill, while analyzing their visual gaze, cutting force, tool path, and workpiece quality. Analysis of variance (ANOVA) and multivariate regression analysis were performed and showed that the unique behavior of the operator affects the process performance measures of specific energy consumption and MRR. In the future, these findings can be used to predict product quality and instruct new practitioners.

Prediction of Tool Forces in Manual Grinding Using Consumer-Grade Sensors and Machine Learning

“…Hence, they are more difficult to predict. Other influences such as the wear of the grinding disc [1], state of charge of the battery in hand-held cordless grinders, and lower stiffness of the tools complicate the prediction. The comparison with the prediction of body forces and movements such as knee joint forces (KJF) with one or more IMUs shows a similar approach (leave-one-out cross-validation and classical and deep learning approaches) for regression [23][24][25][26][27][28].…”

“…The high accuracies can be explained by the fact that the grinding normal force has a physical relation with the torque of the hand-held grinding machine and, thus, with the current of the motor of the grinding machine [19]. The grinding tangential force is dependent on the grinding normal force via the friction coefficient between tool and workpiece [1]. The prediction of the axial force for cutting with a cut-off wheel (rMAE = 566.95% and r = 0.13) seems not feasible.…”

“…For the manual grinding process, the forces between the tool and the workpiece, known as the tool forces, are of great importance. The knowledge of these forces can be used, for example, to determine the productivity by means of material removal and machined surface quality [1], as well as the quality of the work result [2]. Knowledge of tool forces enables many improvements in product development and the use of hand-held grinding machines.…”

“…This means that the user influences the grinding tangential force and grinding normal force, hence the consistency of the whole grinding process [14]. In addition, the grinding forces are influenced by grinding disc wear and manual feed [1]. The correlation between the average tool forces with grinding process parameters indicates that manual grinding with hand-held power tools is a force-controlled process and not a path-controlled process like automated grinding [15].…”

Enhancing Performance Evaluation Through Augmented Reality Smart Glasses for Industrial Operators

“…Manufacturing processes include many variables, and adjustments in one variable can greatly influence OP and product quality. Moreover, OP can be difficult to quantify accurately since it frequently requires subjective judgments about the quality of work [4]. Lastly, the lack of a standardised method to monitor OP across sectors can make comparing performance data and identifying best practices challenging.…”