The use of a complexity model to facilitate in the selection of a fuel cell assembly sequence

Various tools and methods exists for arriving at an optimised assembly sequence with most using a soft computing approach. However, these methods have issues including susceptibly to early convergence and high computational time. The typical objectives for these methods are to minimise the number of assembly change directions, orientation changes or the number of tool changes. This research proposes an alternative approach whereby an assembly sequence is measured based on its complexity. The complexity value is generated using design for assembly metrics and coupled with considerations for product performance, component precedence and material handling challenges to arrive at a sequence solution which is likely to be closest to the optimum for cost and product quality. The case presented in this study is of the assembly of a single proton exchange membrane fuel cell. This research demonstrates a practical approach for determining assembly sequence using data and tools that are used and available in the wider industry. Further work includes automating the sequence generation process and extending the work by considering additional factors such as ergonomi

Computer-Aided Assembly Sequence Planning for High-Mix Low-Volume Products in the Electronic Appliances Industry

Electronic appliance manufacturers are facing the challenge of frequent product orders. Based on each product order, the assembly process and workstations need to be planned. An essential part of the assembly planning is defining the assembly sequence, considering the mechanical product’s design, and handling of the product’s components. The assembly sequence determines the order of processes for each workstation, the overall layout, and thereby time and cost. Currently, the assembly sequence is decided by industrial engineers through a manual approach that is time-consuming, complex, and requires technical expertise. To reduce the industrial engineers’ manual effort, a Computer-Aided Assembly Sequence Planning (CAASP) system is proposed in this paper. It compromises the components for a comprehensive system that aims to be applied practically. The system uses Computer-Aided Design (CAD) files to derive Liaison and Interference Matrices that represent a mathematical relationship between parts. Subsequently, an adapted Ant Colony Optimization Algorithm generates an optimized assembly sequence based on these relationships. Through a web browser-based application, the user can upload files and interact with the system. The system is conceptualized and validated using the CAD file of an electric motor example product. The results are discussed, and future work is outlined

The Use of a Complexity Model to Facilitate in the Selection of a Fuel Cell Assembly Sequence

Various tools and methods exists for arriving at an optimised assembly sequence with most using a soft computing approach. However, these methods have issues including susceptibly to early convergence and high computational time. The typical objectives for these methods are to minimise the number of assembly change directions, orientation changes or the number of tool changes. This research proposes an alternative approach whereby an assembly sequence is measured based on its complexity. The complexity value is generated using design for assembly metrics and coupled with considerations for product performance, component precedence and material handling challenges to arrive at a sequence solution which is likely to be closest to the optimum for cost and product quality. The case presented in this study is of the assembly of a single proton exchange membrane fuel cell. This research demonstrates a practical approach for determining assembly sequence using data and tools that are used and available in the wider industry. Further work includes automating the sequence generation process and extending the work by considering additional factors such as ergonomics

Robot-Enabled Construction Assembly with Automated Sequence Planning based on ChatGPT: RoboGPT

Robot-based assembly in construction has emerged as a promising solution to address numerous challenges such as increasing costs, labor shortages, and the demand for safe and efficient construction processes. One of the main obstacles in realizing the full potential of these robotic systems is the need for effective and efficient sequence planning for construction tasks. Current approaches, including mathematical and heuristic techniques or machine learning methods, face limitations in their adaptability and scalability to dynamic construction environments. To expand the ability of the current robot system in sequential understanding, this paper introduces RoboGPT, a novel system that leverages the advanced reasoning capabilities of ChatGPT, a large language model, for automated sequence planning in robot-based assembly applied to construction tasks. The proposed system adapts ChatGPT for construction sequence planning and demonstrate its feasibility and effectiveness through experimental evaluation including Two case studies and 80 trials about real construction tasks. The results show that RoboGPT-driven robots can handle complex construction operations and adapt to changes on the fly. This paper contributes to the ongoing efforts to enhance the capabilities and performance of robot-based assembly systems in the construction industry, and it paves the way for further integration of large language model technologies in the field of construction robotics.Comment: 14 pages, 20 figures, submitted to IEEE Acces

Ontology based semantic-predictive model for reconfigurable automation systems

Due to increasing product variety and complexity, capability to support reconfiguration is a key competitiveness indicator for current automation system within large enterprises. Reconfigurable manufacturing systems could efficiently reuse existing knowledge in order to decrease the required skills and design time to launch new products. However, most of the software tools developed to support design of reconfigurable manufacturing system lack integration of product, process and resource knowledge, and the design data is not transferred from domain-specific engineering tools to a collaborative and intelligent platform to capture and reuse design knowledge. The focus of this research study is to enable integrated automation systems design to support a knowledge reuse approach to predict process and resource changes when product requirements change. The proposed methodology is based on a robust semantic-predictive model supported by ontology representations and predictive algorithms for the integration of Product, Process, Resource and Requirement (PPRR) data, so that future automation system changes can be identified at early design stages

Computer Aided Optimal Robotic Assembly Sequence Generation

Robots are widely used for assembly operations across manufacturing industries to attain high productivity through automation. An appropriate robotic assembly sequence further minimizes the total production lead time and overall cost by minimizing the number of assembly direction changes, assembly gripper changes and assembly energy thus selection of a valid optimal robotic assembly sequence is significantly essential to achieve economized manufacturing process. An optimal assembly sequence must comply with various assembly requirements in order to make sure that the sequence of assembly operations is functionally feasible in physical environment. In order to test an assembly sequence for its practical possibility, necessary assembly information must be collected accurately from the product. Obtaining such assembly information from product drawings or Computer Aided Design (CAD) models in manual mode were involved in lots of complexity and needs high level skills to ensure correctness. Though retrieving such information from products with less number of parts is simple and less time consuming, for products composed of huge number parts it is very complicated and time consuming. Besides retrieving the assembly information, using it for validating an assembly sequence further raises the complexity of the Assembly Sequence Generation (ASG) problem. To perform optimal feasible assembly sequence generation efficiently, an effective computer aided automated method is developed and executed at two phases. The first phase of research is mainly focused on representing the assembly information in a streamlined manner by considering all possible states of assembly configurations for ease of computerization and developing efficient methods to extract the assembly information automatically from CAD environment though Computer Aided Automation (CAA). These methods basically use assembly contact analysis, part transformations and laws of equilibrium & balancing of rigid bodies. From the existing ASG methods, it is observed most of the researchers ignored/not-considered few of the assembly information such as assembly stability data and mechanical feasibility data due to higher complexity in retrieving it from CAD environment....

Application of Decentralized and Self-Regulating Knowledge Bases for Assembly Design Automation

During product development, changes to parts that are already built into assemblies usually lead to the need to check the function and consistency of the assembly. This procedure is very time-consuming and has to be performed again for each change. In this paper, an approach is presented in which the individual parts are represented as agents that adapt themselves to new conditions. The agents are combined in a multi-agent system (MAS) and interact via communication over messages. For this purpose, a methodical procedure for the development of the MAS and the implementation in a CAD development environment is presented. The validation of the MAS is carried out on the application example of a gearbox

Enabling modular dosage form concepts for individualized multidrug therapy: Expanding the design window for poorly water-soluble drugs

Multidrug dosage forms (aka combination dosage forms, polypills, etc.) create value for patients through reduced pill burdens and simplified administration to improve adherence to therapy. Enhanced flexibility of multidrug dosage forms would provide further opportunities to better match emerging needs for individualized therapy. Through modular dosage form concepts, one approach to satisfy these needs is to adapt multidrug dosage forms to a wider variety of drugs, each with a variety of doses and release profiles. This study investigates and technically explores design requirements for extending the capability of modular multidrug dosage form concepts towards individualization. This builds on our recent demonstration of independent tailoring of dose and drug release, which is here extended towards poorly water-soluble drugs. The challenging design requirement of carrying higher drug loads in smaller volumes to accommodate multiple drugs at their clinical dose is here met regarding dose and release performance. With a modular concept, we demonstrate high precision (<5% RSD) in dose and release performance of individual modules containing felodipine or naproxen in Kollidon VA64 at both a wide drug loading range (5% w/w and 50% w/w drug) and a small module size (3.6 mg). In a forward-looking design-based discussion, further requirements are addressed, emphasizing that reproducible individual module performance is predictive of dosage form performance, provided the modules are designed to act independently. Therefore, efforts to incorporate progressively higher drug loads within progressively smaller module volumes will be crucial to extend the design window further towards full flexibility of future dosage forms for individualized multidrug therapy

Ontology based semantic engineering framework and tool for reconfigurable automation systems integration

Digital factory modelling based on virtual design and simulation is now emerging as a part of mainstream engineering activities, and it is typically geared towards reducing the product design cycle time. Reconfigurable manufacturing systems can benefit from reusing the existing knowledge in order to decrease the required skills and design time to launch new product generations. The various industrial simulation systems are currently integrating product design, matching processes and resource requirements to decrease the required skills and design time to launch new products. However, the main focus of current reconfigurable manufacturing systems has been modular production lines to support different manufacturing tasks. Additionally, the design data is not transferrable from various domain-specific software to a collaborative and intelligent platform, which is required to capture and reuse design knowledge. Product design is still dependent on the knowledge of designers and does not link to the existing knowledge on processes and resources, which are in separate domains. To address these issues, this research developed an integration method based on semantic technologies and product, process, resource and requirements (PPRR) ontologies called semantic-ontology engineering framework (SOEF). SOEF transferred original databases to an ontology-based automation data structure with a semantic analysis engine. A pre-defined semantic model is developed to recognise custom requirement and map existing knowledge with processing data in the automation assembly aspect. The main research contribution is using semantic technology to process automation documentation and map semantic data to the PPRR ontology structure. Furthermore, this research also contributes to the automatic modification of system simulation based on custom requirements. The SOEF uses a JAVA-based command-line user interface to present semantic analysis results and import ontology outputs to the vueOne system simulation tool for system evaluation

Independent tailoring of dose and drug release via a modularized product design concept for mass customization

Independent individualization of multiple product attributes, such as dose and drug release, is a crucial overarching requirement of pharmaceutical products for individualized therapy as is the unified integration of individualized product design with the processes and production that drive patient access to such therapy. Individualization intrinsically demands a marked increase in the number of product variants to suit smaller, more stratified patient populations. One established design strategy to provide enhanced product variety is product modularization. Despite existing customized and/or modular product design concepts, multifunctional individualization in an integrated manner is still strikingly absent in pharma. Consequently, this study aims to demonstrate multifunctional individualization through a modular product design capable of providing an increased variety of release profiles independent of dose and dosage form size. To further exhibit that increased product variety is attainable even with a low degree of product modularity, the modular design was based upon a fixed target dosage form size of approximately 200 mm3 comprising two modules, approximately 100 mm3 each. Each module contained a melt-extruded and molded formulation of 40% w/w metoprolol succinate in a PEG1500 and Kollidon\uae VA64 erodible hydrophilic matrix surrounded by polylactic acid and/or polyvinyl acetate as additional release rate-controlling polymers. Drug release testing confirmed the generation of predictable, combined drug release kinetics for dosage forms, independent of dose, based on a product’s constituent modules and enhanced product variety through a minimum of six dosage form release profiles from only three module variants. Based on these initial results, the potential of the reconfigurable modular product design concept is discussed for unified integration into a pharmaceutical mass customization/mass personalization context