Digitalization of Human Operations in the Age of Cyber Manufacturing: Sensorimotor Analysis of Manual Grinding Performance

Abstract

This paper presents new techniques to analyze and understand the sensorimotor characteristics of manual operations such as grinding, and links their influence on process performance. A grinding task, though simple, requires the practitioner to combine elements from the large repertoire of his or her skillset. Based on the joint gaze, force, and velocity data collected from a series of manual grinding experiments, we have compared operators with different levels of experience and quantitatively described characteristics of human manual skill and their effects on manufacturing process parameters such as cutting energy, surface finish, and material removal rate (MRR). For instance, we find that an experienced subject performs the task in a precise manner by moving the tool in complex paths, with lower applied forces and velocities, and short fixations compared to a novice. A detailed understanding of gaze-motor behavior broadens our knowledge of how a manual task is executed. Our results help to provide this extra insight, and impact future efforts in workforce training as well as the digitalization of manual expertise, thereby facilitating the transformation of raw data into product-specific knowledge.

Canonical Correlation Analysis and a New Composite Index on Digitalization and Labor Force in the Context of the Industrial Revolution 4.0

“…Economic growth and job creation vary depending on the level of technological development of a country [17]. Other researchers [18][19][20] stated that employees in the service sector have a great role in increasing the rate of profit, which is the main purpose of the enterprises, to run the business smoothly, and to ensure customer satisfaction and loyalty. Digitization is one of the most visible phenomena of this decade, and its evolution is extremely fast.…”

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

“…Therefore, the process of manual grinding, as opposed to automated grinding, depends on the knowledge, skills, posture, gripping forces, and personal strength of the user [13]. 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].…”

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

“…A skilled operator is expected to be highly adaptable, flexible, and able to quickly learn from process changes. Studies have shown that skills involved in these manual tasks are largely procedural rather than declarative, meaning that they are self-regulatory and orchestrated by an individual operator [3,4]. Surprisingly, even though manual abrasive operations have a growing market, they are under-researched, and limited work has been done on manual process parameters and their effect on surface integrity.…”

“…A detailed investigation of the interrelationship between skill level and process performance can quantify the intricate behavioral complexity involved in manual operations. It can be anticipated that for manual operations, machining process parameters are largely dependent on humanmachine interactions [4] and can greatly influence product outcome [5,6]. To make this humanmachine interaction more effective, there is a need for a quantified, formal method of measuring, representing, transferring, and reasoning about human skills in order to (i) efficiently allocate resources and tasks among humans, between humans and machines, and cross-factories, (ii) expand and enhance human capabilities, and (iii) optimize system behavior to predict the product performance.…”

“…Graphical representation of unit manufacturing process (UMP) for manual grinding operation Experimental Setup consists of eye tracking glasses, kinematic sensors, camera, and piezoelectric force sensor, reprinted from[4].Product & Process Information Job Information Part Description: Surface Topographical Test Geometry: ½ Inch Square Coupon Ma teria l: 304 Sta inless Steel Operations: Manual Grinding Required Tool: Alumina sanding bands Variable definitions for transformation equations V s = Cutting wheel rotational speed (mm/sec) D = Wheel diameter (mm) N = Wheel revolution per minute bw = Width of wheel contact to workpiece F t = Tangential force Q = Specific removal rate e c = Specific grinding energy h eq = Equivalent chip thickness = Density (kg/mm 3 ) Z i (x)= Z o (x) = Initial and final surface height variation at x distance v w = Workpiece velocity F n = Normal force MRR= Material removal rate a e = Depth of cut R a = Average surface roughness R t =Maximum surface roughness P = Grinding power Inputs Processing energy, Workpiece Material i.e. steel Tool geometry Grinding force= f (Worker's Experience, Tool Wear, Manual Feed)…”