Segmentation method of U-net sheet metal engineering drawing based on CBAM attention mechanism

In the manufacturing process of heavy industrial equipment, the specific unit in the welding diagram is first manually redrawn and then the corresponding sheet metal parts are cut, which is inefficient. To this end, this paper proposes a U-net-based method for the segmentation and extraction of specific units in welding engineering drawings. This method enables the cutting device to automatically segment specific graphic units according to visual information and automatically cut out sheet metal parts of corresponding shapes according to the segmentation results. This process is more efficient than traditional human-assisted cutting. Two weaknesses in the U-net network will lead to a decrease in segmentation performance: first, the focus on global semantic feature information is weak, and second, there is a large dimensional difference between shallow encoder features and deep decoder features. Based on the CBAM (Convolutional Block Attention Module) attention mechanism, this paper proposes a U-net jump structure model with an attention mechanism to improve the network's global semantic feature extraction ability. In addition, a U-net attention mechanism model with dual pooling convolution fusion is designed, the deep encoder's maximum pooling + convolution features and the shallow encoder's average pooling + convolution features are fused vertically to reduce the dimension difference between the shallow encoder and deep decoder. The dual-pool convolutional attention jump structure replaces the traditional U-net jump structure, which can effectively improve the specific unit segmentation performance of the welding engineering drawing. Using vgg16 as the backbone network, experiments have verified that the IoU, mAP, and Accu of our model in the welding engineering drawing dataset segmentation task are 84.72%, 86.84%, and 99.42%, respectively

A bio-knowledge based method to prevent control system instability

This study presents a novel bio-inspired method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative (PID) controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is firstly identified. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a liquid-level laboratory plant

A bio-inspired robust controller for a refinery plant process

This research presents a novel bio-inspired knowledge method, based on gain scheduling, for the calculation of Proportional-Integral-Derivative controller parameters that will prevent system instability. The aim is to prevent a transition to control system instability due to undesirable controller parameters that may be introduced manually by an operator. Each significant operation point in the system is identified first. Then, a solid stability structure is calculated, using transfer functions, in order to program a bio-inspired model by using an artificial neural network. The novel method is empirically verified under working conditions in a real refinery plant process

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

An integrated system to design machine layouts for modular special purpose machines

This thesis introduces the development of an integrated system for the design of layouts for special purpose machines (SPMs). SPMs are capable of performing several machining operations (such as drilling, milling, and tapping) at the same time. They consist of elements that can be arranged in different layouts. Whilst this is a unique feature that makes SPMs modular, a high level of knowledge and experience is required to rearrange the SPM elements in different configurations, and also to select appropriate SPM elements when product demand changes and new layouts are required. In this research, an integrated system for SPM layout design was developed by considering the following components: an expert system tool, an assembly modelling approach for SPM layouts, an artificial intelligence tool, and a CAD design environment. SolidWorks was used as the 3D CAD environment. VisiRule was used as the expert system tool to make decisions about the selection of SPM elements. An assembly modelling approach was developed with an SPM database using a linked list structure and assembly relationships graph. A case-based reasoning (CBR) approach was developed and applied to automate the selection of SPM layouts. These components were integrated using application programing interface (API) features and Visual Basic programming language. The outcome of the application of the novel approach that was developed in this thesis is reducing the steps for the assembly process of the SPM elements and reducing the time for designing SPM layouts. As a result, only one step is required to assemble any two SPM elements and the time for the selection process of SPM layouts is reduced by approximately 75% compared to the traditional processes. The integrated system developed in this thesis will help engineers in design and manufacturing fields to design SPM layouts in a more time-effective manner

The strategic value of targeted knowledge management - case study of an Australian refrigeration company

 This thesis is a study of design and implementation of an engineering knowledge management system to facilitate knowledge capture, sharing and reuse to both ensure business continuity and resolve a make-span problem in an Australian refrigeration company. The company had encountered problems with a number of engineering staff in the small product development team leaving the company and taking their expertise with them. This situation has impacted the business continuity of the company, because the knowledge and expertise used in the refrigerated display cabinet development process is a combination of explicit and tacit knowledge as the engineers conduct the product development process intuitively. Records of previous design and testing processes were either non-existent or stored in ways that were not accessible. The other business problem in the company resulted from product development taking too long, in effect from 6 weeks up to the worst case of one year. The company needed research solutions to both of these problems to strategically maintain the competitiveness of the company business. This research applied a single case study research method with a problem-solving paradigm, Design Science methodology, to develop and then test solutions. Design Science as a research methodology has two components, first design development and second, design evaluation. The researcher developed an engineering knowledge based system as an artefact to solve the problem of enabling company business continuity. Using ontology as a structural base, the KBS contains both knowledge elements captured from the engineers during the data collection process and existing knowledge artefacts in the company. The research used a set of multilayered research techniques, including semi-formal and formal interviews, serendipitous interviews, group meetings, observation and shadowing, to capture and then structure both the tacit and explicit knowledge. The resultant ontology was used to build the KBS to store both tacit and explicit knowledge and answer the engineers’ questions about their existing and previous product development processes. The KBS developed in this research is a knowledge repository to maintain records of the products design and testing processes in a searchable form. Use and then an evaluation of the system by the engineers and the executive staff of the company confirmed that the intention of the system to address the business continuity problem by knowledge capture, classification and storage was achieved and met the company’s business needs. This research also applied Heuristic Process Mining to the knowledge stored in the KBS to address the second problem identified initially by the company, that of lengthy make span in new product design and development. HPM is a technique using mathematical models to find relationships between tasks in the process. HMP measures dependency and frequency values between tasks and tasks with low D/F value can be eliminated from the process. This then can lead to the shorter product testing process. The research showed that the application of HPM to the stored process knowledge in the KMS was able to significantly reduce the product design and testing process in the company