An experimental investigation on the relationship between perceived assembly complexity and product design complexity

Complexity is one of the main drivers inducing increased assembly cost, operational issues and increased lead time for product realisation, and continues to pose challenges to manual assembly operations. In the literature, assembly complexity is widely viewed from both objective and subjective perspectives. The objective perspective relates complexity directly to the characteristics of a process without accounting the characteristics of performers, whereas, subjective perspective considers complexity as a conjunction between process and performer characteristics. This article aims to investigate the link between perceived assembly complexity and product complexity by providing a prediction model relying on a series of natural experiments. In these experiments, the participants were asked to assemble a series of ball-and-stick models with varying degree of product complexity based on a clear 2D assembly work instruction. Complexity of each model was objectively estimated by considering structural properties associated with handling and insertion of assembly parts and their connectivity pattern. Moreover, perceived complexity is approached based on the subjective interpretations of the participants on the difficulty associated with the assembly operation of each model. The results showed that product complexity and assembly time is super-linearly correlated; an increase in the product complexity is accompanied with an increase in assembly time, rework rate and human errors. Moreover, a sigmoid curve is proposed for the relationship between perceived assembly complexity and product complexity indicating that human workers start to perceive assembly operation of a particular product as complex if the product complexity reaches a critical threshold which can vary among individuals with different skill sets, experience, training levels and assembly preferences

Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches

A multiplicity of factors including technological innovations, dynamic operating environments, and globalisation are all believed to contribute towards the ever-increasing complexity of manufacturing systems. Although complexity is necessary to meet functional needs, it is important to assess and monitor it to reduce life-cycle costs by simplifying designs and minimising failure modes. This research paper identifies and describes two key industrially relevant methods for assessing complexity, namely a data-centric approach using the information theoretic method and a human-centric approach based on surveys and questionnaires. The paper goes on to describe the benefits and shortcomings of each and contributes to the body of knowledge by proposing a holistic framework that combines both assessment methods

Assessing operational complexity of manufacturing systems based on algorithmic complexity of key performance indicator time-series

This article presents an approach to the assessment of operational manufacturing systems complexity based on the irregularities hidden in manufacturing key performance indicator time-series by employing three complementary algorithmic complexity measures: Kolmogorov complexity, Kolmogorov complexity spectrum’s highest value and overall Kolmogorov complexity. A series of computer simulations derived from discrete manufacturing systems are used to investigate the measures’ potentiality. The results showed that the presented measures can be used in quantitatively identifying operational system complexity, thereby supporting operational shop-floor decision-making activities

A virtual engineering based approach to verify structural complexity of component-based automation systems in early design phase

Highly diverse factors including technological advancements, uncertain global market and mass personalisation are believed to be main causes of ever-growing complexity of manufacturing systems. Although complex systems may be needed to achieve global manufacturing requirements, complexity affects on various factors, such as: system development effort and cost, ease of re-configuration, level of skill required across the system life-cycle (e.g. design, operate and maintain). This article aims to develop a scientifically valid and industrially applicable complexity assessment approach to support early life-cycle phases of component-based automation systems against unwanted implications of structural system complexity

Macroscopic loop formation in circular DNA denaturation

The statistical mechanics of DNA denaturation under fixed linking number is qualitatively different from that of the unconstrained DNA. Quantitatively different melting scenarios are reached from two alternative assumptions, namely, that the denatured loops are formed in expense of 1) overtwist, 2) supercoils. Recent work has shown that the supercoiling mechanism results in a BEC-like picture where a macroscopic loop appears at Tc and grows steadily with temperature, while the nature of the denatured phase for the overtwisting case has not been studied. By extending an earlier result, we show here that a macroscopic loop appears in the overtwisting scenario as well. We calculate its size as a function of temperature and show that the fraction of the total sum of microscopic loops decreases above Tc, with a cusp at the critical point.Comment: 5 pages, 3 figures, submitted for publicatio

A digital life-cycle management framework for sustainable smart manufacturing in energy intensive industries

Energy intensive industries can be classified into those that process metal, glass, ceramics, paper, cement, and bulk chemicals. They are associated with significantly high proportions of carbon emissions, consume a lot of energy and raw materials, and cause energy wastage as a result of heat escaping from furnaces, reheating of products, and rejection of parts. In alignment with UN sustainable development goals of industry, innovation, infrastructure and responsible consumption and production, it is important to ensure that the energy consumption of EIIs are monitored and reduced such that their energy efficiency can be improved. Towards this aim, it is possible to employ the concepts of digitalization and smart manufacturing to identify the critical areas of improvement and establish enablers that can help improve the energy efficiency. The aim of this research is to review the current state of digitalisation in energy-intensive industries and propose a framework to support the realisation of sustainable smart manufacturing in Energy Intensive Industries (EIIs). The key objectives of the work are (i) the investigation of process mining and simulation modelling to support sustainability, (ii) embedding intelligence in EIIs to improve energy and material efficiency and (iii) proposing a framework to enable the digital transformation of EIIs. The proposed five-layer framework employs data acquisition, process management, simulation & modelling, artificial intelligence, and data visualisation to identify and forecast energy consumption. A detailed description of the various phases of the framework and how they can be used to support sustainability and smart manufacturing is demonstrated using business process data obtained from a machining industry. In the demonstrated case study, the process management layer utilises Disco for process mining, the simulation layer utilises Matlab SimEvent for discrete-event simulation, the artificial intelligence layer utilises Matlab for energy prediction and the visualisation layer utilises grafana to dashboard the e-KPIs. The findings of the research indicate that the proposed digital life-cycle framework helps EIIs realise sustainable smart manufacturing through better understanding of the energy-intensive processes. The study also provided a better understanding of the integration of process mining and simulation & modelling within the context of EIIs

Convertibility Evaluation of Automated Assembly System Designs for High Variety Production

© 2017 The Authors. The recent advancements in technology and the high volatility in automotive market compel industries to design their production systems to offer the required product variety. Although, paradigms such as reconfigurable modular designs, changeable manufacturing, holonic and agent based systems are widely discussed to satisfy the need for product variety management, it is essential to practically assess the initial design at a finer level of granularity, so that those designs deemed to lack necessary features can be flagged and optimised. In this research, convertibility expresses the ability of a system to change to accommodate product variety. The objective of this research is to evaluate the system design and quantify its responsiveness to change for product variety. To achieve this, automated assembly systems are decomposed into their constituent components followed by an evaluation of their contribution to the system's ability to change. In a similar manner, the system layout is analysed and the measures are expressed as a function of the layout and equipment convertibility. The results emphasize the issues with the considered layout configuration and system equipment. The proposed approach is demonstrated through the conceptual design of battery module assembly system, and the benefits of the model are elucidated

A method to assess assembly complexity of industrial products in early design phase

Complexity is one of the factors, inducing high cost, operational issues, and increased lead time for product realization and continues to pose challenges to manufacturing systems. One solution to reduce the negative impacts of complexity is its assessment, which can help designers to compare and rationalize various designs that meet the functional requirements. In this paper, a systemic approach is proposed to assess complexity of a product's assembly. The approach is based on Hückel's molecular orbital theory and defines complexity as a combination of both the complexity of product entities and their topological connections. In this model, the complexity of product entities (i.e., components and liaisons) is defined as the degree to which the entity comprises structural characteristics that lead to challenges during handling or fitting operations. The characterization of entity complexities is carried out based on the widely used DFA principles. Moreover, the proposed approach is tested on two case studies from electronics industry for its validity. The results showed that the approach can be used at initial design stages to improve both the quality and assemblability of products by reducing their complexity and accompanying risks

A Design Process Framework to Deal with Non-functional Requirements in Conceptual System Designs

To simultaneously satisfy the user needs and project-specific technical requirements, it is imperative that complex engineering systems are designed using contemporary, systematic approaches. This study presents a framework that combines Axiomatic Design and Fuzzy Analytic Hierarchy Process to ensure that designers can concurrently satisfy the functional and non-functional requirements along with the design constraints of conceptual system designs. A conceptual design case of an autonomous battery charging system for Unmanned Aerial Vehicles is presented as an illustrative case study. The results showed that the approach can aid decision-making processes by systematic evaluation and comparison of conceptual designs such that the selected solutions satisfy user needs whilst also realising both functional and non-functional requirements of the system

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly