Marco Faber

Design and Implementation of a Cognitive Simulation Model for Robotic Assembly Cells

Against the background of a changing global economy, new production technologies have to be developed to stay competitive in high-wage countries. Therefore, an integrated cognitive simulation model (CSM) has been developed to support the human operator and the assembly process. By making the behavior of the system more intuitive the cognitive compatibility between the operator and the production system is enhanced significantly. The presented CSM faces three different challenges: (1) visualizing the behavior of the system to give the human operator an understanding of the technical systems, (2) cognitive control of a real robotic assembly cell and (3) performing mass simulations in order to evaluate parameters, new assembly or planning strategies or the assembly of new products. Additionally, a graph-based planner supports the cognitive planning instance for realizing complex tasks.

Cognition-Enhanced, Self-optimizing Production Networks

This research area focuses on the management systems and principles of a production system. It aims at controlling the complex interplay of heterogeneous processes in a highly dynamic environment, with special focus on individualized products in high-wage countries. The project addresses the comprehensive application of self-optimizing principles on all levels of the value chain. This implies the integration of self-optimizing control loops on cell level, with those addressing the production planning and control as well as supply chain and quality management aspects. A specific focus is on the consideration of human decisions during the production process. To establish socio-technical control loops, it is necessary to understand how human decisions are made in diffuse working processes as well as how cognitive and affective abilities form the human factor within production processes.

A Symbolic Approach to Self-optimisation in Production System Analysis and Control

With steadily increasing customer requirements on quality of both products and processes, companies are faced with increasing organisational and technical challenges. The market is characterised by individualised customer wishes which result in individual adaptations of the products. In order to manage this rapidly growing variety of products, the production system has to become much more flexible with respect to the product structure to be manufactured and the corresponding production and assembly processes. Especially in the field of assembly systems the increasing variety of products adds new complexities to the planning process and increases the costs, because (re-)planning efforts tend to grow exponentially to the number of variants.

Requirements for Modeling the Human Operator in Socio-Technical Production Systems

The changing world economy makes high demands on todays production systems. In order to stay competitive, companies, especially in high-wage countries, have to adjust their production for enabling customer individual wishes. The human operator provides meaningful skills including sensorimotorical skills and the capability of creative thinking from which the production system can significantly benefit. For establishing effective human-machine cooperation, both the employee and the technical system need to have an understanding of each other so that they can estimate the counterpart. In this paper, the requirements for introducing the human operator in technical models of production systems are described. Furthermore, first solutions are presented to implement effective human-machine cooperation.

Cognitive Engineering for Direct Human-Robot Cooperation in Self-optimizing Assembly Cells

In a work system with direct human robot cooperation the conformity of the operators expectation with the behavior of the robotic device is of great importance. In this contribution a novel approach for the numerical control of such a system based on human cognition and a cognitive engineered approach for the encoding of the systems a priori knowledge is introduced. The implementation using an established method in the field of design of cognitive systems is compared to a schema describing human decision making. Finally, simulation results of the implementation are compared to empirical tests with individuals.

A Comparative Empirical Evaluation of the Accuracy of the Novel Process Language MTM-Human Work Design

The paper presents the comparative evaluation of the building block system MTM-HWD® (Human Work Design). It aims at verifying the statistical accuracy of the method to predict motion times compared to the generic MTM-1 system. The MTM-HWD® system is for use in serial production and especially for cycle times between 30 and 120 s. The accuracy of the system as the mean difference between MTM-HWD® and MTM-1 cycle times at the 95 % confidence level are determined by using a sample of 43 real work place analyses summing up 12,499 MTM-1 process building blocks. The sample size is representative by comparing it to a similar study during the development of MTM-2 made by the Swedish MTM Association. The findings show a statistical significant difference between MTM-HWD® and MTM-1 (α = 0.05). However, the cycle times analyzed with MTM-HWD® deviate on average not more than 5 % from those using MTM-1.

Cognitively automated assembly processes: a simulation based evaluation of performance.

The numerical control of an experimental assembly cell with two robots--termed a cognitive control unit (CCU)--is able to simulate human information processing at a rule-based level of cognitive control. To enable the CCU to work on a large range of assembly tasks expected of a human operator, the cognitive architecture SOAR is used. The CCU can plan assembly processes autonomously and react to ad-hoc changes in assembly sequences effectively. Extensive simulation studies have shown that cognitive automation based on SOAR is especially suitable for random parts supply, which reduces planning effort in logistics. Conversely, a disproportional increase in processing time was observed for deterministic parts supply, especially for assemblies containing large numbers of identical parts. In this contribution, the effect of phase-shifts in deterministic part supply is investigated for assemblies containing maximal different parts. It can be shown that the concept of cognitive automation is as well suitable for these planning problems.

Human-Robot Collaboration in Manual Assembly – A Collaborative Workplace

The integration of humans into the assembly process in terms of human-robot collaboration (HRC) enables the flexibility of production processes with a high degree of automation, which are not flexible enough to overcome the challenges in nowadays production in all cases. However, this form of cooperation raises issues such as occupational safety or acceptance. In order to address these questions, a HRC workstation has been designed that is on the one hand characterized by traditional ergonomic design aspects with regard to conventional industrial requirements. On the other hand, data from intelligent sensors are used to adapt the system’s behavior to the way the working person works. The workstation was developed on the basis of the results of a requirement analysis. This article presents the ergonomic concepts of the workplace and their implementation.

Cognition-enhanced assembly sequence planning for ergonomic and productive human-robot collaboration in self-optimizing assembly cells

Self-optimizing robotized assembly systems are able to compensate the restricted operation purpose of traditional robotized automation, in order to adapt dynamically to changed production conditions. As the human operator is directly involved in the assembly process, the interaction between the human and the robot has to be designed carefully to avoid exposing the human to excessive physical and cognitive strain. For controlling a robotized assembly cell, a Cognitive Control Unit (CCU) was developed that uses the cognitive software Soar and human-like assembly strategies to achieve a transparent and understandable assembly process. To minimize the cognitive and ergonomic risks during assembly, the CCU was extended by a graph-based assembly sequence planner (GASP). The GASP is able to find the optimal assembly sequence by using a complete assembly graph of the final product as well as generic production rules for assessing the ergonomic conditions of the individual assembly steps. The presented simulation study validates the functionality of the GASP with respect to the number of workflow switches between the human and the robot, the number of switches between the robotic tools, as well as the number of assembly group switches required to collaboratively assemble a model of a Stromberg carburetor. The results show a significant reduction of all three measures. The number of parts and the type of assessment of the assembly steps have a significant impact here.

Model-Based Evaluation of Cooperative Assembly Processes in Human-Robot Collaboration

The increasing variety in product range demand high flexibility of the production technologies and assembly systems of producing companies. Integrating the human into the assembly process by establishing collaboration between the human and robotized assembly systems seems to be a promising approach to achieve this flexibility even for very small lot sizes. This paper presents a model for assessing the ergonomic risk in such collaboration scenarios. Criteria for assigning assembly steps to the human or the robot are introduced as well as for describing the physical and cognitive ergonomic risk of an individual assembly step. The presented risk model is finally applied to the process of assembly sequence planning, in order to find the optimal assembly sequence in situations of human-robot collaboration.