Using requirement-functional-logical-physical models to support early assembly process planning for complex aircraft systems integration

The assembly line process planning connects product design and manufacturing through translating design information to assembly integration sequence. The assembly integration sequence defines the aircraft system components installation and test precedence of an assembly process. This activity is part of the complex systems integration and verification process from a systems engineering view. In this paper, the complexity of modern aircraft is defined by classifying aircraft system interactions in terms of energy flow, information data, control signals and physical connections. At the early conceptual design phase of assembly line planning, the priority task is to understand these product complexities, and generate the installation and test sequence that satisfies the designed system function and meet design requirements. This research proposes a novel method for initial assembly process planning that accounts for both physical and functional integrations. The method defines aircraft system interactions by using systems engineering concepts based on traceable RFLP (Requirement, Functional, Logical and Physical) models and generate the assembly integration sequence through a structured approach. The proposed method is implemented in an industrial software environment, and tested in a case study. The result shows the feasibility and potential benefits of the proposed method

Process Planning for Assembly and Hybrid Manufacturing in Smart Environments

Manufacturers strive for efficiently managing the consequences arising from the product proliferation during the entire product life cycle. New manufacturing trends such as smart manufacturing (Industry 4.0) present a substantial opportunity for managing variety. The main objective of this research is to help the manufacturers with handling the challenges arising from the product variety by utilizing the technological advances of the new manufacturing trends. This research focuses mainly on the process planning phase. This research aims at developing novel process planning methods for utilizing the technological advances accompanied by the new manufacturing trends such as smart manufacturing (Industry 4.0) in order to manage the product variety. The research has successfully addressed the macro process planning of a product family for two manufacturing domains: assembly and hybrid manufacturing. A new approach was introduced for assembly sequencing based on the notion of soft-wired galled networks used in evolutionary studies in Biological and phylogenetic sciences. A knowledge discovery model was presented by exploiting the assembly sequence data records of the legacy products in order to extract the embedded knowledge in such data and use it to speed up the assembly sequence planning. The new approach has the capability to overcome the critical limitation of assembly sequence retrieval methods that are not able to capture more than one assembly sequence for a given product. A novel genetic algorithm-based model was developed for that purpose. The extracted assembly sequence network is representing alternative assembly sequences. These alternative assembly sequences can be used by a smart system in which its components are connected together through a wireless sensor network to allow a smart material handling system to change its routing in case any disruptions happened. A novel concept in the field of product variety management by generating product family platforms and process plans for customization into different product variants utilizing additive and subtractive processes is introduced for the first time. A new mathematical programming optimization model is proposed. The model objective is to provide the optimum selection of features that can form a single product platform and the processes needed to customize this platform into different product variants that fall within the same product family, taking into consideration combining additive and subtractive manufacturing. For multi-platform and their associated process plans, a phylogenetic median-joining network algorithm based model is used that can be utilized in case of the demand and the costs are unknown. Furthermore, a novel genetic algorithm-based model is developed for generating multi-platform, and their associated process plans in case of the demand and the costs are known. The model\u27s objective is to minimize the total manufacturing cost. The developed models were applied on examples of real products for demonstration and validation. Moreover, comparisons with related existing methods were conducted to demonstrate the superiority of the developed models. The outcomes of this research provide efficient and easy to implement process planning for managing product variety benefiting from the advances in the technology of the new manufacturing trends. The developed models and methods present a package of variety management solutions that can significantly support manufacturers at the process planning stage

Sustainable human-robot co-production for the bicycle industry

Bicycle production has not changed much over the last 100 years, it is still performed mainly by manual labor in mass production. During the global pandemic, the demand for ecologically friendly and customized transport has increased. Hence, customers start to impose the same requirements on bikes as on cars: they want more customized products and short delivery time. This publication describes an approach to transform bicycle manufacturing towards human-robot co-production to enable smaller batch sizes and production on-shoring. We list the challenges of this transformation, our applied methods, and presents preliminary results of the cobot-driven prototypes

Intelligent robot of inclined assembly sequence planning in Industrial 4.0

In the industry 4.0, the Cyber-physical system (CPS) is one of the most important core which makes the manufacturing process more intelligent. Intelligent assembly operation is an important key in intelligent manufacturing of CPS. To complete the intelligent assembly operation, the cooperation between assembly robotic arm and assembly sequence planning (ASP) is necessary. However, the ASP and writing robotic codes manually is time consuming and requires professional knowledge and experience. Because the Local Coordinate System (LCS) is often ignored when checking for interference. If product have inclined interference and without considering LCS and causing and infeasible ASP. Therefore, this paper proposes a LCCPIAS (Local Coordinate Cyber-Physical Intelligent Assembly System) system to achieve three objective functions. First, this paper presents a dual-projected-based interference analysis approach (DPIAA) that analyzes the relations between components. Second, this paper generates optimal assembly sequence automatically to let the assembly sequence more suitable for the robotic arm to perform the assembly operation. The last one is LCS can recognize inclined interference between components and generate feasible ASP. Furthermore, this paper uses CAD model to verify that the DPIAA is faster and consider LCS interference can solve inclined interference problem. In the future assembly factory, the proposed method can help to realize intelligent manufacturing

Aircraft assembly process design for complex systems installation and test integration.

The assembly line planning process connects product design and manufacturing through translating design information to assembly integration sequence. The assembly integration sequence defines the aircraft system components installation and test precedence of an assembly process. From a systems engineering view point, this activity is part of the complex systems integration and verification process. At the early conceptual design phase of assembly line planning, the priority task of assembly process planning is to understand product complexities in terms of systems interactions, and generate the installation and test sequence to satisfy the designed system function and meet design requirements. This research proposes to define these interactions by using systems engineering concept based on traceable RFLP (Requirement, Functional, Logical and Physical) models and generate the assembly integration sequence through a structured approach. A new method based on systems engineering RFLP framework is proposed to generate aircraft installation and test sequence of complex systems. The proposed method integrates aircraft system functional and physical information in RFLP models and considers these associated models as new engineering data sources at the aircraft early development stage. RFLP modelling rules are created to allow requirements, functional, logical and physical modes be reused in assembly sequence planning. Two case studies are created to examine the method. Semi- structured interviews are used for research validation. The results show that the proposed method can produce a feasible assembly integration sequence with requirements traceability, which ensures consistency between design requirements and assembly sequences.PhD in Aerospac

Conditions For Effective Use Of Community Sustainability Indicators And Adaptive Learning

Thesis (Ph.D.) University of Alaska Fairbanks, 2012As the number of community sustainability indicator programs (SIPs) increases in many regions of the world, including in the United States, questions continue to arise regarding how decision makers can use sustainability indicators (SIs) to contribute in a meaningful way to their efforts to build resilient and sustainable communities. Through an analysis of the sustainability activities in sample cities from across the U.S. and a case study of one city that adopted SIs but has yet to implement them, this study seeks to uncover the conditions for effective SI implementation and use. The study began with a review of the literature on communities' sustainability efforts and the historical roots of sustainability and resilience theory leading up to today's sustainability indicator projects. A heuristic model for adaptive learning is presented to illustrate the relationships among sustainability, resilience, and administrative concepts, including the goals and domains of sustainability indicators. The study's data collection and analysis began with an Internet-based investigation of 200 U.S. cities. A five-tiered system was devised to categorize findings regarding sustainability patterns and trends in studied cities, ranging from an absence of sustainability activities through fully implemented sustainability indicators. The second phase of data collection employed an electronic survey completed by informants from a 38-city sample of the 200 investigated cities, followed by phone interviews with informants from cities that ranked high for developed sustainability programs. A case study using focus group research was then conducted of one small U.S. city, Juneau, Alaska, where local government adopted sustainability indicators in the 1990s but fell short of implementing them. Most cities in the U.S. have not developed sustainability indicator projects, and, among those that have, few have been able to implement them fully. Among highly ranked cities with sustainability indicators, several approaches, including innovative organizational structures and adaptive learning processes, were found to be present. Recommendations for incorporating such innovations and for grounding sustainability indicator projects in sustainability science, resilience thinking, and public administration theory are offered to help ensure sustainability indicators become fully operational in Juneau, as well as in other communities seeking to establish successful sustainability indicator programs

Development of a supply planning methodology in the automotive industry

Supply Planning in the Automotive Industry is a vital ingredient for Supply Chain Integration. The role and function of a Supply Planner, although clearly defined in European developed methods, lacks the practical dimension. This paper describes such a practical approach that was developed for Supply Planning in the South African Automotive Industry. The framework highlights all the aspects – from a business and functional perspective - that need to be considered on a global and local scale. The framework describes the role and responsibilities of the Supply Planner as an active supply chain designer during the product/production development process.Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2006.Industrial and Systems Engineeringunrestricte

Automated decision making and problem solving. Volume 2: Conference presentations

Related topics in artificial intelligence, operations research, and control theory are explored. Existing techniques are assessed and trends of development are determined