Shop-floor Operator Research Articles

Production risk analysis and prioritization in manufacturing industry: A hybrid approach using Monte Carlo simulation and DEMATEL analysis

Risk identification and prioritization in production sector is critical if an enterprise wishes to compete successfully. To achieve this, Delphi technique was utilized to finalize 36 identified risks in a ‘metal plate production’ stage prior to risk analysis through risk probability and its impact. Risk prioritization is performed by Monte Carlo Simulation (MCS) and Decision Making and Trial Evaluation Laboratory (DEMATEL) analysis. The novelty of this study is to develop an effective hybrid approach by utilizing strengths of MCS (with iterations in thousands on the simulation) as well as DEMATEL analysis (to assess the causal relationship among risk factors). A more accurate risk identification by developing a consolidated list of risks in one of the important production stages followed by an efficient prioritization of interacting (process and product related) risks will help the shop floor operator making better decisions to enhance the quality by minimizing production time and cost.

Use Cases for Shop Floor Operator Support by means of Mixed Reality

The aim of this research is to explore the potential of Mixed Reality (MR) technologies for Operator Support in order to progress towards Industry 4.0 (I4.0) particularly for SMEs. Through a series of interventions and interviews conducted with local SMEs, potential use cases and their drawbacks have been identified. From this, insights were derived that serve as a starting point for conducting further experiments with MR technology in the smart manufacturing laboratory at the THUAS in Delft. The intervention consisted of a free form workshop in which the participants get ‘tinkering’ time to explore MR in their own work environment. The various levels of awareness were assessed in three stages: during an introductory interview, and after an instruction meeting and some ‘tinkering’. The study took place in the period from January 2022 to July 2022 with 10 local SMEs in the Netherlands. The results show that for all SMEs the awareness and understanding increased. The use cases identified by operators themselves concerned Quality Control, Diagnostics, Instruction, Specification and Improvement of Operations. Drawbacks foreseen related to Ergonomic Concerns, Resistance from operators, Technical considerations, Unavailability of MR device and an insufficient digital infrastructure to support MR in full extent. The use case most promising to the participants was further developed into a physical prototype for an ‘assisted assembly cell’ by which the aspects of ergonomics and the mentioned technical considerations could be analysed.

Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.

A tailor-made manufacturing information system from a shop-floor operator's perspective

This article aims to uncover the determinants of acceptance and the use of a tailor-made manufacturing information system implemented in a global bus manufacturer. A survey to map the perceptions of manufacturing operators working on two sites of production for components involved 166 workers. The unified theory of acceptance and use of technology (UTAUT) is the theoretical framework used in this study. Data analysis using cluster, crosstab and discriminant analysis, associated with personal and documentary observation, revealed the existence of four clusters of importance and satisfaction perceptions related to the use of this system. The analysis also reinforced the fact that managerial support plays a vital role in the adoption of manufacturing systems.

Data‐driven framework for the prediction of cutting force in turning

Cutting force is one of the most important parameters for assessing power consumption and tool wear. This work attempts to utilise the concept of data analytics for collecting and building a data warehouse in a cloud-based platform called Central Database Repository (CDR), which can share the information about the machining forces in turning operations directly to the process planner or shop floor operator. The estimation of cutting force is accomplished based on the input of process parameters. CDR uses multiple-linear regression on the data collected and stored in the data bank. Uncertainties in machining operations are taken care of in a separate cloud-based platform known as mini-repository. This study estimates the interval of cutting force with a certain level of confidence and a new concept of dynamic reliability is attached to it. The information from the CDR can be accessed from anywhere across the globe. There is a provision to update the database based on the feedback and filter out unnecessary data by evaluating Cook's distance. The proposed framework is explained through illustrative case studies.

A machine learning approach for improved shop-floor operator support using a two-level collaborative filtering and gamification features

The increasing gap in shopfloor operators’ skillset regarding advanced information and communication technologies along with workforce’s diversity require a cognitive system bridging such technical gaps in order to address evolving production demands and satisfy the human need for self-fulfillment and self-actualization at work. This study discusses on a two-level collaborative filtering approach to improve the distribution of information content provided to an operator for completing a manufacturing activity while considering his or her feedback. A prototype implementation is evaluated in a case study related to the operator’s job rotation on a shopfloor that involves multiple workstations and tasks.

A Tailor-Made Manufacturing Information System From Shop-Floor Operator’s Perspective

A Tailor-Made Manufacturing Information System From Shop-Floor Operator’s Perspective

Effectivity of Learning Factories to convey problem solving competencies

The ability to solve problems in a structured way is an essential competence of people in a factory, from the shopfloor operator to the management level. This is already the case for established lean management approaches and is gaining relevance with the increasing complexity of production systems through progressive automation and digitisation. As learning factories have shown to be an effective approach for learning and teaching production-related topics in a practical manner, a learning module for the A3 problem solving methodology was implemented at an industrial learning factory. In order to continuously improve the learning and teaching productivity, the effectiveness of different approaches to convey the A3 methodology was investigated with over 100 participants. The results show that while the setting of learning factories can be used in a beneficial way for learning the A3 methodology, additional advantages regarding learning as well as process performance can be generated by including real und current content from the learner‘s specific workplaces.

The future shop-floor operators, demands, requirements and interpretations

The evolution of the manufacturing industry reveals continuous progress and development throughout the years. This evolution not only includes production methodologies and the production equipment, it also includes the working environment of the shop-floor operators. The demands faced by the shop-floor operators have developed from strictly controlled, simple and monotonic tasks to self-controlled team work requiring a holistic approach that aims at continuous improvements and achieving a high degree of flexibility, adaptability and initiative.This paper describes the evolution of the shop-floor operator, according to the research literature and interviews with manufacturing managers and human resources specialists. In addition, the paper presents the response of future Swedish shop-floor operators, today’s high-school students, to a description of their possible future work as shop-floor operators. The Swedish manufacturing industry competes, to a large extent, on and responds to the international market. The findings made in this paper are thus also interesting for other industries and countries acting on the international market.

On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions

In this paper, we consider a permutation flowshop scheduling problem with the total flow time as the schedule performance measure. A proactive–reactive approach is designed to simultaneously deal with stochastic disruptions (e.g., machine breakdowns) and dynamic events (e.g., newly arriving jobs and delay in job availability). In the proactive stage, the stochastic machine breakdown is hedged against the construction of a robust and stable baseline schedule. This schedule is either optimized by incorporating uncertainty into two surrogate measures or obtained by simulation. Robustness is measured by the expected schedule performance, while stability is measured by the aggregation of dissatisfactions of manager, shopfloor operator, and customers using the prospect theory. In the reactive stage, we assume that the stochastic and dynamic disruptions concurrently occur. Unlike the simple right-shifting method, a more effective rescheduling approach is proposed to balance the realized schedule performance with stability. A common issue in these two stages is the conflict between objectives. Thus, we propose a hybridization strategy that successfully enhances the classic Non-dominated Sorting Genetic Algorithm (NSGA-II and the hybridized algorithm outperforms NSGA-II, multiobjective evolutionary algorithm based on decomposition, and multiobjective memetic algorithms designed for deterministic scheduling problems. Finally, extensive computational studies on the Taillard flowshop benchmark instances are conducted to illustrate the effectiveness of the proposed proactive–reactive approach and the algorithm hybridization strategy.

Why I want to be a Future Swedish Shop-floor Operator

When looking in rear view mirrors the Swedish as well as the international production industries can overview several years of progress covering all aspects of production. Production methodologies and machines etc. have changed and evolved, and so has the environment of the shop-floor operator. The demands on the shop-floor operators have grown from simple monotonic tasks with low complexity to pro-active team work requiring flexibility, continuous improvements and a holistic approach.With a base in a study where production and HR-managers at six Swedish manufacturing industries have been interviewed this paper identifies the role of today's and the future Swedish shop-floor operator. The response to the described role of the future operator is compiled from the ones who will become the future Swedish shop-floor operators – today's teenagers attending technical high-school. Their views of the environment of the future shop-floor operator are described by accuracy, development, a good working environment and team work. The paper also reveals what the offer should include to make these teenagers say: I want to be a future Swedish shop-floor operator.

Support Systems on the Industrial Shop-floors of the Future – Operators’ Perspective on Augmented Reality

With augmented reality, virtual information can be overlaid on the real world in order to enhance a human's perception of reality. In this study, we aim to deepen the knowledge of augmented reality in the shop-floor context and analyze its role within smart factories of the future. The study evaluates a number of approaches for realizing augmented reality and discusses advantages and disadvantages of different solutions from a shop-floor operator's perspective. The evaluation is done in collaboration with industrial companies, including Volvo Cars and Volvo GTO amongst others. The study also identifies important future research directions for utilizing the full potential of the technology and successfully implement it on industrial shop-floors.

Online Support for Shop-Floor Operators Using Body Movements Tracking

This paper presents results of experiments on the integration of human operators and automation in the context of assembly systems. Several experiments have been conducted to evaluate the use of augmented reality applications in order to provide dynamic work instructions and online feedback information to a shop-floor operator. Based on these results, an experimental platform has been developed so that the shop-floor operator and the automated system behave as a closed-loop system.

Object oriented modelling and development of a dispatching algorithm for automated guided vehicles

The main problem for an automated guided vehicle (AGV) dispatching system is to assign vehicles to transport demands which optimise some predetermined objectives of a manufacturing shop. This paper presents a framework for an AGV dispatching system based on an object oriented approach using the unified modelling language (UML), and the development of a dispatching algorithm to facilitate a human controller to dispatch efficiently a fleet of AGVs in response to calls from any shop floor (or machine) operator. The main reason for this work is to model an AGV dispatching system as well as to develop a dispatching algorithm which can record details of the AGV position and movement and ensure their allocation of new orders. The provision of both immediate and pre-booked orders for an AGV is also incorporated in the proposed AGVs dispatching system. The underlying AGV dispatching system and algorithm are capable of dispatching a vehicle automatically to handle a call at the required time. In order to overcome difficulties associated with tackling immediate orders, pre-booked orders, and processing of information related to AGVs, a comprehensive dispatching algorithm is developed which aims to minimise lateness, traveling time and distance of empty vehicles in a simulated job-shop scenario. The effectiveness of the proposed framework for an AGV dispatching system is shown through a test problem.

Computer-aided assessment of debonds in laminates from shearographic fringe patterns

Shearography has shown to be a potentially viable non-destructive testing technique, especially for the detection and evaluation of debonds in composites. With this technique, debonds in an object are revealed readily from anomalies in the shearographic fringe pattern; these anomalies also enable accurate estimation of both the size and depth of debonds. For the technique to be acceptable to industry, it would be useful to automate the interpretation of fringe patterns so that prior knowledge of shearography is not required of the shop-floor operator: this paper demonstrates that such an objective can be realised. Digitization and storage of shearographic fringes are first performed using an imaging device connected to a personal computer, after which the operator will examine the stored image for debonds. Finally, estimation of the size and depth of the debonds detected will be automated using a computer programme.

Advanced manufacturing technology and work simplification: An empirical study

AbstractThis paper is concerned with the effects of advanced manufacturing technology on shopfloor operator jobs and work attitudes. A strong line of argument from labour process theory suggests that such technology will simplify and deskill jobs, and reduce the quality or working life. This proposition was explored within a large electronics company which assembles computer boards, where two different applications of information technology have been experienced. These were compared with two traditional manual assembly jobs. Assessment of job content, perceived job characteristics and operator work attitudes, showed no uniform deskilling effects of advanced manufacturing technologies. Much larger differences existed both between the different new technology applications and between the traditional jobs. This demonstrates that choice of technology is important to skill use and employees' attitudes, as are the choices for the organization of work around it. These are not uniform processes even within a single organization. Experience of operating advanced manufacturing technology was also accompanied by more positive views concerning its impact on the shopfloor.