Coping with Aging in Manual Assembly Systems: Age-differentiated Analyses and Mathematical Modeling for Predicting the Time Structure of Sensorimotor-skill Acquisition for Assembly in Series Production with Numerous Product Variants

该研究项目的重点是应对老化的手动系列组装与众多的产品变体。本研究的目的是建立一个年龄差异化的模型，并预测典型装配任务的学习过程的时间结构。研究目标由几个子目标组成。第一步是发展一个基于理论的数学模型的时间结构的学习过程，包括当前的学习理论和进一步的概念方面。这既适用于工人的早期和中心过程，也适用于随着时间的推移学习过程的数学公式。通过在研究项目中扩展现有知识，将连续验证模型。为此，将进行几项年龄差异研究。这些研究将分析年龄对学习和学习时间的影响以及其他因素。研究的重点将是对尚未分析或作为其他技术、业务或社会参数的一部分进行分析的因素进行实证研究，如不同类型的工作计划和不同的工作指导方法。此外，休息时间的长短（每次任务执行后）和时间类型对学习的影响也将是分析的一部分。这些影响很可能是共同的人的表现。所获得的结果和知识将得到巩固，以推导和开发一个有效的预测方法的学习时间。该方法将提供一个准确的和强大的估计学习时间的典型的手工任务在工业系列生产。这将在统计方法的帮助下实现，我的初步研究已经成功地使用了这种方法。学习效果的数学描述必须首先被识别，其参数作为预测的一部分被估计。与初步研究相比，将考虑其他因素。此外，还将使用更详细和更先进的统计方法，如向量自回归和周期向量自回归模型，其结构可以通过理论和经验数据加以证实。该项目中开发的模型的预测有效性将在最终的实验室研究中进行评估。

The research project focuses on coping with aging in manual series assembly with numerous product variants. The research objective is to produce an age-differentiated model and forecast of the time structure of learning processes for typical assembly tasks. The research objective consists of several sub-objectives. The first step is to develop a theory-based mathematical model of the time structure of learning processes that includes both current theories of learning and further conceptual aspects. This applies both to workers' early and central processes, as well as to the mathematical formulation of the learning progress over the course of time. By expanding existing knowledge during the research project, the model will be successively validated.To this end, several age-differentiated studies will be conducted. The impact of age on learning and learning time, as well as additional factors, will be analyzed in these studies. The focus of the studies will be on empirical research on factors that have not yet been analyzed or have been analyzed as part of other technical, operational or social parameters, such as different types of work plans and different job instruction methods. In addition, the influences of the length of breaks (after each task execution) and of chronotype on learning will be part of the analyses. These influences are probably conjunct in terms of human performance.The results and knowledge obtained will be consolidated in order to derive and develop a valid prediction method for learning times. The method will provide an exact and robust estimation of the learning time for typical manual tasks in industrial series production. This will be achieved with the help of a statistical approach which has already been used successfully in my preliminary studies. A mathematical description of the learning effect has first to be identified, the parameters of which are to be estimated as part of the prediction. Additional factors - compared to the preliminary studies - will be taken into account. In addition, more detailed and more advanced statistical approaches such as vector autoregressive and periodic vector autoregressive models, whose structure can be substantiated by theory and empirical data, will be used. The predictive validity of the model developed in the project will be assessed in a final laboratory study.