Editorial

5 pages, 228 KiB

Open AccessEditorial

Industrial Process Improvement by Automation and Robotics

by Raul D. S. G. Campilho and Francisco J. G. Silva

Machines 2023, 11(11), 1011; https://doi.org/10.3390/machines11111011 - 06 Nov 2023

Viewed by 2564

Automation and robotics have revolutionized industrial processes, making them more efficient, precise, and flexible [...] Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

Research

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26 pages, 11387 KiB

Open AccessArticle

Managing Delays for Realtime Error Correction and Compensation of an Industrial Robot in an Open Network

by Seemal Asif and Phil Webb

Machines 2023, 11(9), 863; https://doi.org/10.3390/machines11090863 - 28 Aug 2023

Viewed by 985

Abstract

The calibration of articulated arms presents a substantial challenge within the manufacturing domain, necessitating sophisticated calibration systems often reliant on the integration of costly metrology equipment for ensuring high precision. However, the logistical complexities and financial burden associated with deploying these devices across [...] Read more.

The calibration of articulated arms presents a substantial challenge within the manufacturing domain, necessitating sophisticated calibration systems often reliant on the integration of costly metrology equipment for ensuring high precision. However, the logistical complexities and financial burden associated with deploying these devices across diverse systems hinder their widespread adoption. In response, Industry 4.0 emerges as a transformative paradigm by enabling the integration of manufacturing devices into networked environments, thereby providing access through cloud-based infrastructure. Nonetheless, this transition introduces a significant concern in the form of network-induced delays, which can significantly impact realtime calibration procedures. To address this pivotal challenge, the present study introduces an innovative framework that adeptly manages and mitigates network-induced delays. This framework leverages two key components: controller and optimiser, specifically the MPC (Model Predictive Controller) in conjunction with the Extended Kalman Filter (EKF), and a Predictor, characterised as the Dead Reckoning Model (DRM). Collectively, these methodologies are strategically integrated to address and ameliorate the temporal delays experienced during the calibration process. Significantly expanding upon antecedent investigations, the study transcends prior boundaries by implementing an advanced realtime error correction system across networked environments, with particular emphasis on the intricate management of delays originating from network traffic dynamics. The fundamental aim of this research extension is twofold: firstly, it aims to enhance realtime system performance on open networks, while concurrently achieving an impressive level of error correction precision at 0.02 mm. The employment of the proposed methodologies is anticipated to effectively surmount the intricacies and challenges associated with network-induced delays. Subsequently, this endeavour serves to catalyse accurate and efficient calibration procedures in the context of realtime manufacturing scenarios. This research significantly advances the landscape of error correction systems and lays a robust groundwork for the optimised utilisation of networked manufacturing devices within the dynamic realm of Industry 4.0 applications. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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34 pages, 22799 KiB

Open AccessArticle

An Improved Automation System for Destructive and Visual Measurements of Cross-Sectional Geometric Parameters of Microdrills

by Wen-Tung Chang and Yu-Yun Lu

Machines 2023, 11(6), 581; https://doi.org/10.3390/machines11060581 - 23 May 2023

Viewed by 983

Abstract

Microdrills are specific cutting tools widely used to drill microholes and microvias. For certain microdrill manufacturers, a conventional sampling inspection procedure is still manually operated for carrying out the destructive and visual measurements of two essential cross-sectional geometric parameters (CSGPs), called the cross-sectional [...] Read more.

Microdrills are specific cutting tools widely used to drill microholes and microvias. For certain microdrill manufacturers, a conventional sampling inspection procedure is still manually operated for carrying out the destructive and visual measurements of two essential cross-sectional geometric parameters (CSGPs), called the cross-sectional web thickness (CSWT) and the cross-sectional outer diameter (CSOD), of their straight (ST) and undercut (UC) type microdrill products. In order to comprehensively automate the conventional sampling inspection procedure, a destructive and visual measuring system improved from an existing vision-aided automation system, for both the hardware and the automated measuring process (AMP), is presented in this paper. The major improvement of the hardware is characterized by a machine vision module consisting of several conventional machine vision components in combination with an innovative and lower cost optical subset formed by a set of plano-concave achromatic (PCA) lenses and a reflection mirror, so that the essential functions of visually positioning the drilltip and visually measuring the CSGPs can both be achieved via the use of merely one machine vision module. The major improvement of the AMP is characterized by the establishment of specific image processing operations for an auto-focusing (AF) sub-process based on two-dimensional discrete Fourier transform (2D-DFT), for a web thickness measuring (WTM) sub-process based on an iterative least-square (LS) circle-fitting approach, and for an outer diameter measuring (ODM) sub-process based on integrated applications of an iterative LS circle-fitting approach and an LS line-fitting-based group-dividing approach, respectively. Experiments for measuring the CSGPs of microdrill samples were conducted to evaluate the actual effectiveness of the developed system. It showed that the developed system could achieve good repeatability and accuracy for the measurements of the CSWTs and CSODs of both ST and UC type microdrills. Therefore, the developed system could effectively and comprehensively automate the conventional sampling inspection procedure. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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22 pages, 13416 KiB

Open AccessArticle

2D and 3D Wires Formability for Car Seats: A Novel Full-Automatic Equipment Concept towards High Productivity and Flexibility

by Manuel Gaspar, Francisco J. G. Silva, Arnaldo G. Pinto and Raul D. S. G. Campilho

Machines 2023, 11(3), 410; https://doi.org/10.3390/machines11030410 - 21 Mar 2023

Cited by 1 | Viewed by 3727

Abstract

The automotive industry demands high quality at very low prices. To this end, it is necessary to constantly innovate, making processes increasingly competitive, while continuing to ensure high levels of quality. Model diversification has forced the automotive industry to make its manufacturing processes [...] Read more.

The automotive industry demands high quality at very low prices. To this end, it is necessary to constantly innovate, making processes increasingly competitive, while continuing to ensure high levels of quality. Model diversification has forced the automotive industry to make its manufacturing processes more flexible, without losing competitiveness. This has been the case for car seats, where the quantities to be produced per batch are significantly lowering due to the diversity of existing models. The objective of this work was to increase the production rate of bent wires used in car seat cushions and increase the flexibility of changing wire types in production. After benchmarking the existing solutions so far, it was verified that none are capable of complying with the required production rate, while also offering the desired flexibility. Thus, it is necessary to start with a new concept of conformation of the wires used in these seat cushions. The new concept developed and integrated some of the previously known solutions, developing other systems capable of providing the desired response in terms of productivity and flexibility. To this end, new mechanical solutions and automated systems were developed, which, together with other existing ones, made it possible to design equipment that complies with all the necessary requirements. The developed concept is innovative and can be employed to other types of products in which it can be applied. The new concept developed yields a production rate of 950 parts/hour (initial goal: 800 parts/hour), features a setup time of around 30 min, ensuring the desired flexibility, and the tool costs about 90% less than traditional tools. The payback period is around 5 months, given that the equipment cost was EUR 122.000 in terms of construction and assembly, and generated a gain of EUR 280.000 in the first year of service. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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14 pages, 861 KiB

Open AccessArticle

Production Planning Process Based on the Work Psychology of a Collaborative Workplace with Humans and Robots

by Felicita Chromjakova

Machines 2023, 11(2), 160; https://doi.org/10.3390/machines11020160 - 23 Jan 2023

Viewed by 1527

Abstract

This study focuses on discerning how economics, as it pertains to work psychology, is lent a new perspective by the compatibility of humans and robots cooperating in the manufacturing sector. The stability of production plans, flexibility of the organizations, and the management of [...] Read more.

This study focuses on discerning how economics, as it pertains to work psychology, is lent a new perspective by the compatibility of humans and robots cooperating in the manufacturing sector. The stability of production plans, flexibility of the organizations, and the management of production constitute the basis for such analysis. In this context, initial findings revealed that steady performance by an individual was significantly influenced by a production plan, while the cycle and lead times in place fundamentally affected the behaviour of employees. Observations were made over five years of 200 workers at 100 manufacturers. Times given over to operations and cycles, and throughput, were primarily defined by the technical cycle of the robot. The secondary element of production planning was the employee, whose operator cycle time was informed by that of the robot. The authors set out to deduce which key factors altered the work psychology in situ in manufacturing environments where collaboration occurred between humans and robots. Prerequisites for optimal psychological conditions were identified (the cooperating human, production planner, collaborative workplace, standardized durations of complete tasks, distance between the worker and robot, and data analytics of production flow). Ensuring circumstances are optimal in terms of work psychology is essential to raising productivity and employee performance. Results showed that the operator was directly dependent on the robot in relation to mutual, continuous production flow. A model of production plan stability was devised, informed by the dependence of specific parameters of the planning model. Research was conducted on the reliance of selected parameters, leading to establishment of prerequisites for an optimal work psychology setting in enterprises with such a collaborative structure. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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21 pages, 6190 KiB

Open AccessArticle

Process Simulation and Optimization of Arc Welding Robot Workstation Based on Digital Twin

by Qinglei Zhang, Run Xiao, Zhen Liu, Jianguo Duan and Jiyun Qin

Machines 2023, 11(1), 53; https://doi.org/10.3390/machines11010053 - 02 Jan 2023

Cited by 11 | Viewed by 3539

Abstract

For the welding cell in the manufacturing process of large excavation motor arm workpieces, a system framework, based on a digital twin welding robot cell, is proposed and constructed in order to optimize the robotic collaboration process of the welding workstation with digital [...] Read more.

For the welding cell in the manufacturing process of large excavation motor arm workpieces, a system framework, based on a digital twin welding robot cell, is proposed and constructed in order to optimize the robotic collaboration process of the welding workstation with digital twin technology. For the automated welding cell, combined with the actual robotic welding process, the physical entity was digitally modeled in 3D, and the twin welding robot operating posture process beats and other data were updated in real time, through real-time interactive data drive, to achieve real-time synchronization and faithful mapping of the virtual twin as well as 3D visualization and monitoring of the system. For the robot welding process in the arc welding operation process, a mathematical model of the kinematics of the welding robot was established, and an optimization method for the placement planning of the initial welding position of the robot base was proposed, with the goal of smooth operation of the robot arm joints, which assist in the process simulation verification of the welding process through the virtual twin scenario. The implementation and validation process of welding process optimization, based on this digital twin framework, is introduced with a moving arm robot welding example. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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17 pages, 5529 KiB

Open AccessEditor’s ChoiceArticle

A GAN-BPNN-Based Surface Roughness Measurement Method for Robotic Grinding

by Guojun Zhang, Changyuan Liu, Kang Min, Hong Liu and Fenglei Ni

Machines 2022, 10(11), 1026; https://doi.org/10.3390/machines10111026 - 04 Nov 2022

Cited by 5 | Viewed by 1734

Abstract

Existing machine vision-based roughness measurement methods cannot accurately measure the roughness of free-form surfaces (with large curvature variations). To overcome this problem, this paper proposes a roughness measurement method based on a generative adversarial network (GAN) and a BP neural network. Firstly, this [...] Read more.

Existing machine vision-based roughness measurement methods cannot accurately measure the roughness of free-form surfaces (with large curvature variations). To overcome this problem, this paper proposes a roughness measurement method based on a generative adversarial network (GAN) and a BP neural network. Firstly, this method takes images and curvature of free-form surfaces as training samples. Then, GAN is trained for roughness measurement through each game between generator and discriminant network by using real samples and pseudosamples (from generator). Finally, the BP neural network maps the image discriminant value of GAN and radius of curvature into roughness value (Ra). Our proposed method automatically learns the features in the image by GAN, omitting the independent feature extraction step, and improves the measurement accuracy by BP neural network. The experiments show that the accuracy of the proposed roughness measurement method can measure free-form surfaces with a minimum roughness of 0.2 μm, and measurement results have a margin of 10%. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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17 pages, 5844 KiB

Open AccessArticle

Decision Support Method for Dynamic Production Planning

by Simona Skėrė, Aušra Žvironienė, Kazimieras Juzėnas and Stasė Petraitienė

Machines 2022, 10(11), 994; https://doi.org/10.3390/machines10110994 - 29 Oct 2022

Cited by 3 | Viewed by 1183

Abstract

Small and medium-sized engineering production companies face challenges that are related to unpredicted rapid changes of availability of the work force, materials and equipment. Those challenges are especially difficult to solve for companies focusing on unit or batch production and when they are [...] Read more.

Small and medium-sized engineering production companies face challenges that are related to unpredicted rapid changes of availability of the work force, materials and equipment. Those challenges are especially difficult to solve for companies focusing on unit or batch production and when they are collaborating with customers who require short lead times. A four-month observation was carried out in a metal processing company in Lithuania to understand the most common rising problems and developing solution for computerised decision support systems. It was discovered that the company needs a computerised “employee centred” system for the improvement of the allocation of tasks to employees. Such a need proved to be the most urgent one, especially during pandemics. An algorithm for the analysis and automated allocation of the employees’ tasks has been developed and tested. The proposed algorithm is universal and may be applied in different SMEs for engineering production. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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20 pages, 6206 KiB

Open AccessArticle

Improving Industrial Robot Positioning Accuracy to the Microscale Using Machine Learning Method

by Vytautas Bucinskas, Andrius Dzedzickis, Marius Sumanas, Ernestas Sutinys, Sigitas Petkevicius, Jurate Butkiene, Darius Virzonis and Inga Morkvenaite-Vilkonciene

Machines 2022, 10(10), 940; https://doi.org/10.3390/machines10100940 - 17 Oct 2022

Cited by 9 | Viewed by 3033

Abstract

Positioning accuracy in robotics is a key issue for the manufacturing process. One of the possible ways to achieve high accuracy is the implementation of machine learning (ML), which allows robots to learn from their own practical experience and find the best way [...] Read more.

Positioning accuracy in robotics is a key issue for the manufacturing process. One of the possible ways to achieve high accuracy is the implementation of machine learning (ML), which allows robots to learn from their own practical experience and find the best way to perform the prescribed operation. Usually, accuracy improvement methods cover the generation of a positioning error map for the whole robot workspace, providing corresponding correction models. However, most practical cases require extremely high positioning accuracy only at a few essential points on the trajectory. This paper provides a methodology for the online deep Q-learning-based approach intended to increase positioning accuracy at key points by analyzing experimentally predetermined robot properties and their impact on overall accuracy. Using the KUKA-YouBot robot as a test system, we perform accuracy measurement experiments in the following three axes: (i) after a long operational break, (ii) using different loads, and (iii) at different speeds. To use this data for ML, the relationships between the robot’s operating time from switching on, load, and positioning accuracy are defined. In addition, the gripper vibrations are evaluated when the robot arm moves at various speeds in vertical and horizontal planes. It is found that the robot’s degrees of freedom (DOFs) clearances are significantly influenced by operational heat, which affects its static and dynamic accuracy. Implementation of the proposed ML-based compensation method resulted in a positioning error decrease at the trajectory key points by more than 30%. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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22 pages, 9970 KiB

Open AccessArticle

Design of a Spiral Double-Cutting Machine for an Automotive Bowden Cable Assembly Line

by André F. G. Barbosa, Raul D. S. G. Campilho, Francisco J. G. Silva, Isidro J. Sánchez-Arce, Chander Prakash and Dharam Buddhi

Machines 2022, 10(9), 811; https://doi.org/10.3390/machines10090811 - 15 Sep 2022

Cited by 2 | Viewed by 2284

Abstract

The manufacture of automotive components requires innovative technologies and equipment. Due to the competitiveness in the sector, the implementation of automatic and robotic equipment has been vital in its development to produce the largest number of products in the shortest amount of time. [...] Read more.

The manufacture of automotive components requires innovative technologies and equipment. Due to the competitiveness in the sector, the implementation of automatic and robotic equipment has been vital in its development to produce the largest number of products in the shortest amount of time. Automation leads to a significant reduction in defects and enables mass production and standardization of the final product. This work was based on the need of an automotive components’ company to increase the rate of spiral cable cutting, used as protection for Bowden (control) cables. Currently, this component, used in automotive systems, is processed with simple cutting machines and cleaning machines. Based on the design science research (DSR) methodology, this work aims to develop a machine capable of performing the cutting and cleaning of two spiral cables simultaneously and automatically. The development of this machine was based on existing machines, and the biggest challenge was the implementation of a double-cutting system. The designed machine met the initial requirements, such as enabling the simultaneous cut of two spirals, being fully automatic, doubling the output over the current solution, and fully complying with the current legislation. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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16 pages, 5684 KiB

Open AccessArticle

A Method for Measurement of Workpiece form Deviations Based on Machine Vision

by Wei Zhang, Zongwang Han, Yang Li, Hongyu Zheng and Xiang Cheng

Machines 2022, 10(8), 718; https://doi.org/10.3390/machines10080718 - 22 Aug 2022

Cited by 5 | Viewed by 1878

Abstract

Machine vision has been studied for measurements of workpiece form deviations due to its ease of automation. However, the measurement accuracy limits its wide implementation in industrial applications. In this study, a method based on machine vision for measurement of straightness, roundness, and [...] Read more.

Machine vision has been studied for measurements of workpiece form deviations due to its ease of automation. However, the measurement accuracy limits its wide implementation in industrial applications. In this study, a method based on machine vision for measurement of straightness, roundness, and cylindricity of a workpiece is presented. A subsumed line search algorithm and an improved particle swarm optimization algorithm are proposed to evaluate the straightness and roundness deviations of the workpiece. Moreover, an image evaluation method of cylindricity deviation by the least-square fitting of the circle’s center coordinates is investigated. An image acquisition system incorporating image correction and sub-pixel edge positioning technology is developed. The performance of the developed system is evaluated against the measurement results of the standard cylindricity measuring instrument. The differences in the measurement of straightness, roundness, and cylindricity are −4.69 μm, 3.87 μm, and 8.51 μm, respectively. The proposed method would provide a viable industrial solution for the measurement of workpiece form deviations. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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16 pages, 1435 KiB

Open AccessArticle

Brain–Computer Interface and Hand-Guiding Control in a Human–Robot Collaborative Assembly Task

by Yevheniy Dmytriyev, Federico Insero, Marco Carnevale and Hermes Giberti

Machines 2022, 10(8), 654; https://doi.org/10.3390/machines10080654 - 05 Aug 2022

Cited by 5 | Viewed by 2737

Abstract

Collaborative robots (Cobots) are compact machines programmable for a wide variety of tasks and able to ease operators’ working conditions. They can be therefore adopted in small and medium enterprises, characterized by small production batches and a multitude of different and complex tasks. [...] Read more.

Collaborative robots (Cobots) are compact machines programmable for a wide variety of tasks and able to ease operators’ working conditions. They can be therefore adopted in small and medium enterprises, characterized by small production batches and a multitude of different and complex tasks. To develop an actual collaborative application, a suitable task design and a suitable interaction strategy between human and cobot are required. The achievement of an effective and efficient communication strategy between human and cobot is one of the milestones of collaborative approaches, which can be based on several communication technologies, possibly in a multimodal way. In this work, we focus on a cooperative assembly task. A brain–computer interface (BCI) is exploited to supply commands to the cobot, to allow the operator the possibility to switch, with the desired timing, between independent and cooperative modality of assistance. The two kinds of control can be activated based on the brain commands gathered when the operator looks at two blinking screens corresponding to different commands, so that the operator does not need to have his hands free to give command messages to the cobot, and the assembly process can be sped up. The feasibility of the proposed approach is validated by developing and testing the interaction in an assembly application. Cycle times for the same assembling task, carried out with and without the cobot support, are compared in terms of average times, variability and learning trends. The usability and effectiveness of the proposed interaction strategy are therefore evaluated, to assess the advantages of the proposed solution in an actual industrial environment. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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Review

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22 pages, 8254 KiB

Open AccessReview

Human–Machine Relationship—Perspective and Future Roadmap for Industry 5.0 Solutions

by Jakub Pizoń and Arkadiusz Gola

Machines 2023, 11(2), 203; https://doi.org/10.3390/machines11020203 - 01 Feb 2023

Cited by 26 | Viewed by 5578

Abstract

The human–machine relationship was dictated by human needs and what technology was available at the time. Changes within this relationship are illustrated by successive industrial revolutions as well as changes in manufacturing paradigms. The change in the relationship occurred in line with advances [...] Read more.

The human–machine relationship was dictated by human needs and what technology was available at the time. Changes within this relationship are illustrated by successive industrial revolutions as well as changes in manufacturing paradigms. The change in the relationship occurred in line with advances in technology. Machines in each successive century have gained new functions, capabilities, and even abilities that are only appropriate for humans—vision, inference, or classification. Therefore, the human–machine relationship is evolving, but the question is what the perspective of these changes is and what developmental path accompanies them. This question represents a research gap that the following article aims to fill. The article aims to identify the status of change and to indicate the direction of change in the human–machine relationship. Within the framework of the article, a literature review has been carried out on the issue of the human–machine relationship from the perspective of Industry 5.0. The fifth industrial revolution is restoring the importance of the human aspect in production, and this is in addition to the developments in the field of technology developed within Industry 4.0. Therefore, a broad spectrum of publications has been analyzed within the framework of this paper, considering both specialist articles and review articles presenting the overall issue under consideration. To demonstrate the relationships between the issues that formed the basis for the formulation of the development path. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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14 pages, 329 KiB

Open AccessReview

Environmental Risk Assessment and Management in Industry 4.0: A Review of Technologies and Trends

by Janaína Lemos, Pedro D. Gaspar and Tânia M. Lima

Machines 2022, 10(8), 702; https://doi.org/10.3390/machines10080702 - 17 Aug 2022

Cited by 10 | Viewed by 3020

Abstract

In recent decades, concern with workers’ health has become a priority in several countries, but statistics still show that it is urgent to perform more actions to prevent accidents and illnesses related to work. Industry 4.0 is a new production paradigm that has [...] Read more.

In recent decades, concern with workers’ health has become a priority in several countries, but statistics still show that it is urgent to perform more actions to prevent accidents and illnesses related to work. Industry 4.0 is a new production paradigm that has brought significant advances in the relationship between man and machine, driving a series of advances in the production process and new challenges in occupational safety and health (OSH). This paper addresses occupational risks, diseases, opportunities, and challenges in Industry 4.0. It also covers Internet-of-Things-related technologies that, by the real-time measurement and analysis of occupational conditions, can be used to create smart solutions to contribute to reducing the number of workplace accidents and for the promotion of healthier and safer workplaces. Proposals involving smart personal protective equipment (smart PPE) and monitoring systems are analyzed, and aspects regarding the use of artificial intelligence and the data privacy concerns are also discussed. Full article

(This article belongs to the Special Issue Industrial Process Improvement by Automation and Robotics)

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