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This paper establishes a quality model for automation assembly of range hood impeller based on generalized grey relational degree, it improves the debugging efficiency of the newly developed assembly workstation.

First, spot check the trial production impellers and obtain three indexes that reflect the assembly quality of the impellers. Then, analyze the parameters that affect the assembly quality of the impeller using grey relational analysis (GRA), establish a model for the assembly quality of the range hood impeller based on the generalized grey relational degree and identify the main parameters. After that, analyze the transmission structure of automation assembly workstation, identify the reasons that affect parameters and propose improvement plans. Finally, a trial production is conducted on the automation assembly workstation after adopting the improved plan to verify the quality model of impeller automation assembly.

The research shows that compared to manual assembly, the automation assembly quality of the impeller using GRA model has been improved, shortening the debugging cycle of the newly developed assembly workstation.

The newly developed automation equipment will have some problems in the trial production stage, which often rely on the experience of engineers for debugging. In this paper, the automation assembly quality model of range hood impeller based on GRA is established, which can not only ensure the quality of finished impeller but also shorten the debugging cycle of the equipment. In addition, GRA can be widely used in the commissioning of other automation equipment.

This study has developed a set of impeller automation assembly workstation. The debugging method in the trial production stage is beneficial to shorten the trial production time and improve the economic benefits.

The paper aims to discuss the importance of considering both the physical and cognitive automation when aiming for a flexible or reconfigurable assembly system. This is done in order to handle the increased demand for mass customized production and to maintain or improve the social sustainability within the company.

The methodologies used in this paper are a theoretical review about task allocation and levels of automation and a methodology called DYNAMO++ for the industrial case studies.

The paper provides both theoretical and empirical insights about the importance of considering both the cognitive and physical automation when aiming for a reconfigurable assembly system.

The paper will only discuss the cognitive strategy from a social sustainability perspective and not from an economical or environmental angle.

The paper presents data from three industrial case studies, mostly in the automotive industry. The result points towards a need for a more structured and quantitative method when choosing automation solutions, furthermore an increased use of cognitive automation solution.

The results from the case studies show that when the complexity and variety of products increases, a cognitive support for the operators is needed. This strengthens the theory of a need for a cognitive automation strategy within companies.

The paper demonstrates an advance in the state of the art in task allocation. The concept model and the DYNAMO++ method can be seen as a step closer towards quantitative measures of task allocation (i.e. changes in both physical and cognitive LoA) and dynamic changes over time.

Over the past years, collaborative robots have been introduced as a new generation of industrial robotics working alongside humans to share the workload. These robots have the potential to enable human–robot collaboration (HRC) for flexible automation. However, the deployment of these robots in industrial environments, particularly in assembly, still comprises several challenges, of which one is skills-based tasks distribution between humans and robots. With ever-decreasing product life cycles and high-mix low volume production, the skills-based task distribution is to become a frequent activity. This paper aims to present a methodology for tasks distribution between human and robot in assembly work by complexity-based tasks classification.

The assessment method of assembly tasks is based on the physical features of the components and associated task description. The attributes that can influence assembly complexity for automation are presented. Physical experimentation with a collaborative robot and work with several industrial cases helped to formulate the presented method.

The method will differentiate the tasks with higher complexity of handling, mounting, human safety and part feeding from low-complexity tasks, thereby simplifying collaborative automation in HRC scenario. Such structured method for tasks distribution in HRC can significantly reduce deployment and changeover times.

Assembly attributes affecting HRC automation are identified. The methodology is presented for evaluating tasks for assigning to the robot and creating a work–load balance forming a human–robot work team. Finally, an assessment tool for simplified industrial deployment.

This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.

Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.

Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.

Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.

It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.

Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.

The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

Aims to present an alternative way of interpreting unfolding events as these pertain to the organisation of manufacturing practices in the assembly plants of the leading Japanese car assembler, Toyota.

This is an analysis of assembly plant automation in the automotive industry.

Fifteen years ago, it was argued that the lean car assembly plants of the future would be comprehensively automated, but that in the meantime organization rather than automation was the watch‐word for efficient plants. Today it is possible to invert this prognosis as it applies to the leading “lean” car assembler, Toyota. Automation certainly played a much larger role in accounting for high labour productivity in the late 1980s than has generally been understood; but in the subsequent years priority has been given to managing the manual component in car assembly, and aggressive automation as a preferred strategy has been put on ice.

The findings raise new questions about future trends in the world automotive industry.

Industry 4.0 emerged as the Fourth Industrial Revolution aiming at achieving higher levels of operational efficiency, productivity and automation. In this context, manual assembly systems are still characterized by high flexibility and low productivity, if compared to fully automated systems. Therefore, the purpose of this paper is to propose the design, engineering and testing of a prototypal adaptive automation assembly system, including greater levels of automation to complement the skills and capabilities of human workers.

A lab experimental field-test is presented comparing the assembly process of a full-scale industrial chiller with traditional and adaptive assembly system.

The analysis shows relevant benefits coming from the adoption of the adaptive automation assembly system. In particular, the main findings highlight improvements in the assembly cycle time and productivity, as well as reduction of the operator’s body movements.

The prototype is applied in an Italian mid-size industrial company, confirming its impact in terms of upgrades of the assembly system flexibility and productivity. Thus, the research study proposed in this paper provides valuable knowledge to support companies and industrial practitioners in the shift from traditional to advanced assembly systems matching current industrial and market features.

This paper expands the lacking research on adaptive automation assembly systems design proposing an innovative prototype able to real-time reconfigure its structure according to the product to work, e.g. work cycle, and the operator features.

This paper provides an overview of five types of assembly equipment and indicates where each type is typically suited to small parts assembly operations

Market turbulence forces assembly plants to constantly adjust their production volume of products, variants and quantities. At the same time, assembly plant managers must protect long‐term investments in the flexible assembly system. For reconfigurability and agility the best solution is the modular semi‐automatic approach by combining flexible automation and human skills. It gives managers possibility to adjust volume by adding new modules or to automate the manual tasks step by step. The control of material handling and information flow in the agile assembly system is important. To keep flexibility, the combination of an intelligent pallet, i.e. use of escort memory, carrying a single product together with other hardware providing paperless production even supports a lot size of one. The article shows how to create flexible capability and capacity in the final assembly systems.

A paper presented at the Paris Assembly Automation conference dealt with a typical project concerning assembly automation of an LPG gas valve which had not been designed for automatic assembly. A series of suggestions for design modifications were proposed with the purpose of simplifying assembly.

This article presents a study on assembly automation in small subcontracting enterprises. In this scenario the evaluated production volumes of each product limit the competitiveness of flexible assembly systems. At the same time the uncertainty of the economic life of the product causes high risk factors in the use of assembly transfer lines. The proposed solution is characterized by the employment of an asynchronous line with single‐purpose automatic stations and manual stations.