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Digital Toolkit for optimisation of operators and technology in manufacturing partnerships (DigiTOP)

用于优化制造合作伙伴关系中的操作员和技术的数字工具包 (DigiTOP)

基本信息

批准号：
EP/R032718/1
负责人：
Sarah Sharples
金额：
$ 242.66万
依托单位：
University of Nottingham
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FR032718%2F1
关键词：
Digital Toolkit optimisation operators technology

项目摘要

The manufacturing industry, with the drive towards 'Industrie 4.0', is experiencing a significant shift towards Digital Manufacturing. This increased digitisation and interconnectivity of manufacturing processes is inevitably going to bring substantial change to worker roles and manual tasks by introducing new digital manufacturing technologies (DMT) to shop floor processes. At the same time, the manufacturing workforce is itself also changing - globally and nationally - comprising of an older, more mobile, more culturally diverse and less specialist / skilled labour pool.It may not be enough to simply assume that workers will adopt new roles bestowed upon them; to ensure successful worker acceptance and operational performance of a new system it is important to incorporate user requirements into Digital Manufacturing Technologies design. In the past, Human Factors has shaped the tools used in manufacturing, to make people safe, to make work easy, and to make the workforce more efficient. New approaches to capture and predict the impact of the changes that these new types of technologies, such as robotics, rapidly evolvable workspaces, and data-driven systems are required. These approaches consist of embedded sensor technologies for capture of workplace performance, machine learning and data analytics to synthesise and analyse these data, and new methods of visualisation to support decisions made, potentially in real-time, as to how digital manufacturing workplaces should function. The DigiTOP project will develop the new fundamental knowledge required to reliably and validly capture and predict the performance of a digital manufacturing workplace, integrating the actions and decision of people and technology. It will deliver this knowledge via a Digital Toolkit, which will have three elements: i) Specification of sensor integration and data analytics for performance capture in Digital Manufacturingii) Quantitative analysis of the impact of four industrial Digital Manufacturing use casesiii) Online interactive tool(s) to support manufacturing decision making for implementation of Digital Manufacturing TechnologiesThe DigiTOP project brings together a team with expertise in manufacturing, human factors, robotics and human computer interaction, to develop new methods to capture and predict the impact of Digital Manufacturing on future work. This project will work closely with a range of industry partners, including Jaguar Landrover, BAE Systems, Babcock International and the High Value Manufacturing Catapult to co-create industry-specified use cases to examine. The overall goal of DigiTOP is to produce a toolkit, derived from new fundamental engineering and science knowledge, that will enable industry to increase productivity, support Digital Manufacturing Technology adoption and de-risk the implementation of future Digital Manufacturing Technologies through the consideration of human requirements and capabilities.

随着“工业4.0”的发展，制造业正在经历向数字制造的重大转变。这种制造过程的数字化和互联性的增加不可避免地会通过将新的数字制造技术（DMT）引入车间流程来给工人角色和手工任务带来实质性的变化。与此同时，全球和全国的制造业劳动力本身也在发生变化，包括年龄更大、移动的更多、文化更多样化和专业/技术更少的劳动力。为了确保新系统的成功的工人接受和操作性能，将用户需求纳入数字系统中是很重要的。制造技术设计。在过去，人为因素塑造了制造业中使用的工具，使人们更安全，使工作更容易，并使劳动力更有效率。新的方法来捕捉和预测这些新类型的技术，如机器人技术，快速发展的机器人技术和数据驱动系统的变化的影响是必需的。这些方法包括用于捕获工作场所性能的嵌入式传感器技术，用于合成和分析这些数据的机器学习和数据分析，以及用于支持数字化制造工作场所应如何运作的决策的新的可视化方法。DigiTOP项目将开发可靠有效地捕获和预测数字化制造工作场所的性能所需的新的基础知识，整合人员和技术的行动和决策。它将通过一个数字工具包提供这方面的知识，该工具包将有三个要素：i）用于数字制造中性能捕获的传感器集成和数据分析规范ii）对四个工业数字制造用例的影响进行定量分析iii）在线交互工具支持数字化制造技术实施的制造决策DigiTOP项目汇集了一个具有以下专业知识的团队：制造业，人为因素，机器人和人机交互，开发新的方法来捕捉和预测数字制造对未来工作的影响。该项目将与一系列行业合作伙伴密切合作，包括Jaguar Landrover，BAE Systems，Babcock International和High Value Manufacturing Catapult，共同创建行业指定的用例。DigiTOP的总体目标是生产一个工具包，来自新的基础工程和科学知识，这将使行业能够提高生产力，支持数字制造技术的采用，并通过考虑人类的需求和能力来降低未来数字制造技术实施的风险。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Physiological Data Measurement in Digital Manufacturing

数字制造中的生理数据测量

DOI：
10.1109/icmt53429.2021.9687200
发表时间：
2021
期刊：
影响因子：
0
作者：
Agrawal S
通讯作者：
Agrawal S

Physiological indicators of task demand, fatigue, and cognition in future digital manufacturing environments

DOI：
10.1016/j.ijhcs.2020.102522
发表时间：
2021-01-01
期刊：
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES
影响因子：
5.4
作者：
Argyle, Elizabeth M.;Marinescu, Adrian;Sharples, Sarah
通讯作者：
Sharples, Sarah

Augmented reality training for improved learnability

增强现实培训可提高可学习性

DOI：
10.1016/j.cirpj.2023.11.003
发表时间：
2024
期刊：
CIRP Journal of Manufacturing Science and Technology
影响因子：
4.8
作者：
Ariansyah D
通讯作者：
Ariansyah D

Investigating the Impact of Human in-the-Loop Digital Twin in an Industrial Maintenance Context

研究人在环数字孪生在工业维护环境中的影响

DOI：
10.2139/ssrn.3717797
发表时间：
2020
期刊：
SSRN Electronic Journal
影响因子：
0
作者：
Al-Yacoubb A
通讯作者：
Al-Yacoubb A

Effective Human-Robot Collaboration Through Wearable Sensors

DOI：
10.1109/etfa46521.2020.9212100
发表时间：
2020-09
期刊：
2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
影响因子：
0
作者：
Ali Al-Yacoub;A. Buerkle;Myles Flanagan;P. Ferreira;Ella‐Mae Hubbard;N. Lohse
通讯作者：
Ali Al-Yacoub;A. Buerkle;Myles Flanagan;P. Ferreira;Ella‐Mae Hubbard;N. Lohse