MONITORING OF THE STENCIL PRINTING PROCESS USING A MODIFIED REGRESSION RESIDUAL CONTROL CHART: AN EMPIRICAL STUDY

This paper aims at developing a regression residual control chart to economically detect the abnormal patterns of the stencil printing process (SPP), so as to predict significant deviations that might result in nonconforming products. The SPP is widely recognized as the main contributor of soldering defects in a surface mount assembly (SMA). The earlier those abnormal conditions can be detected in the SPP stage, the less expensive the defect correction costs. Shewhart control chart is frequently used to monitor the amount of solder paste volume. However, its results can be error-prone since the solder paste volume is significantly affected by other process factors. For developing the proposed control chart, a 3^8-3 experimental design was first conducted and validated to formulate the relationship between the control variables and the SPP response. Eight process factors for SPP were initially defined, including stencil thickness, component pitch, aperture area, snap-off height, squeegee speed, squeegee pressure, solder paste viscosity, and solder paste type. The control variables of the SPP can be expressed as a linear regression function, and a regression residual control chart can then be constructed using the significant variables through the results of ANOVA analysis. Finally, the proposed control chart is employed to detect out-of-control conditions of the SPP. A Monte-Carlo simulation and an empirical evaluation were also carried out to demonstrate the effectiveness of the proposed methodology. The empirical evaluation shows that the proposed regression residual control chart provides approximately 90% of detection accuracy for the SPP

The Application of Advanced Composites for the Construction of Commercial Transport Aircraft

This individual Capstone project examined and evaluated current industry methods of testing, certification, and maintenance of advanced composite materials for the construction of commercial transport aircraft and the FAA regulations governing their use. The project critically compared and contrasted existing FAA standards and regulations governing the testing, certification, and maintenance of advanced composites for commercial transport aircraft structural applications with current industry practices to determine whether there were any areas of conflict between the two in order to accept or reject that current testing, certification, and maintenance procedures for advanced composites used in primary and secondary commercial transport aircraft structures are standardized throughout the aerospace industry and sufficiently capable of detecting damage or component failure. This was accomplished by performing a qualitative and quantitative analysis utilizing meta-analysis to contrast and compare past and current aerospace composite materials studies with non-destructive inspection (NDI) testing and structural health monitoring (SHM) data to determine statistical significance that supported or refuted the hypothesis of comprehensive process improvement throughout the industry. The results of the analysis showed that the hypothesis was accepted for testing and certification, but overwhelmingly rejected for current maintenance and repair. In addition, industry concerns were examined to determine whether limitations exist that would preclude the future use of advanced composites in structural applications based on current FAA standards and regulations. This project determined how current industry practices and FAA methodologies for the testing, certification, and maintenance of advanced composites in commercial transport aircraft structural applications may need to be modified in order to capture and address future industry use

Inspection planning by defect prediction models and inspection strategy maps

Designing appropriate quality-inspections in manufacturing processes has always been a challenge to maintain competitiveness in the market. Recent studies have been focused on the design of appropriate in-process inspection strategies for assembly processes based on probabilistic models. Despite this general interest, a practical tool allowing for the assessment of the adequacy of alternative inspection strategies is still lacking. This paper proposes a general framework to assess the efectiveness and cost of inspection strategies. In detail, defect probabilities obtained by prediction models and inspection variables are combined to defne a pair of indicators for developing an inspection strategy map. Such a map acts as an analysis tool, enabling positioning assessment and benchmarking of the strategies adopted by manufacturing companies, but also as a design tool to achieve the desired targets. The approach can assist designers of manufacturing processes, and particularly low-volume productions, in the early stages of inspection planning

BUSINESS STRATEGY: USING SHIFT LEFT PRINCIPLES TO MANAGE IT PROJECTS EFFECTIVELY

Customer satisfaction is seen as a key differentiator in a competitive market place. As per Gartner 2012 report, only 75%-80% of IT projects are successful. Customer Satisfaction should be part of the business strategy. As a project manager, the associated project parameters should be pro-actively managed and the project outcome needs to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. Focus should be to pro-actively manage and shift left in the life cycle. Identify the problem upfront in the project cycle and not wait for lessons to be learnt and take re-active steps. This paper gives the practical applicability of shift left techniques and illustrates use of predictive model in a project to predict system testing defects thus helping to reduce residual defect

Mitigating Emergent Safety and Security Incidents of CPS by a Protective Shell

In today's modern world, Cyber-Physical Systems (CPS) have gained widespread prevalence, offering tremendous benefits while also increasing society's dependence on them. Given the direct interaction of CPS with the physical environment, their malfunction or compromise can pose significant risks to human life, property, and the environment. However, as the complexity of CPS rises due to heightened expectations and expanded functional requirements, ensuring their trustworthy operation solely during the development process becomes increasingly challenging. This thesis introduces and delves into the novel concept of the 'Protective Shell' – a real-time safeguard actively monitoring CPS during their operational phases. The protective shell serves as a last line of defence, designed to detect abnormal behaviour, conduct thorough analyses, and initiate countermeasures promptly, thereby mitigating unforeseen risks in real-time. The primary objective of this research is to enhance the overall safety and security of CPS by refining, partly implementing, and evaluating the innovative protective shell concept. To provide context for collaborative systems working towards higher objectives — common within CPS as system-of-systems (SoS) — the thesis introduces the 'Emergence Matrix'. This matrix categorises outcomes of such collaboration into four quadrants based on their anticipated nature and desirability. Particularly concerning are outcomes that are both unexpected and undesirable, which frequently serve as the root cause of safety accidents and security incidents in CPS scenarios. The protective shell plays a critical role in mitigating these unfavourable outcomes, as conventional vulnerability elimination procedures during the CPS design phase prove insufficient due to their inability to proactively anticipate and address these unforeseen situations. Employing the design science research methodology, the thesis is structured around its iterative cycles and the research questions imposed, offering a systematic exploration of the topic. A detailed analysis of various safety accidents and security incidents involving CPS was conducted to retrieve vulnerabilities that led to dangerous outcomes. By developing specific protective shells for each affected CPS and assessing their effectiveness during these hazardous scenarios, a generic core for the protective shell concept could be retrieved, indicating general characteristics and its overall applicability. Furthermore, the research presents a generic protective shell architecture, integrating advanced anomaly detection techniques rooted in explainable artificial intelligence (XAI) and human machine teaming. While the implementation of protective shells demonstrate substantial positive impacts in ensuring CPS safety and security, the thesis also articulates potential risks associated with their deployment that require careful consideration. In conclusion, this thesis makes a significant contribution towards the safer and more secure integration of complex CPS into daily routines, critical infrastructures and other sectors by leveraging the capabilities of the generic protective shell framework.:1 Introduction 1.1 Background and Context 1.2 Research Problem 1.3 Purpose and Objectives 1.3.1 Thesis Vision 1.3.2 Thesis Mission 1.4 Thesis Outline and Structure 2 Design Science Research Methodology 2.1 Relevance-, Rigor- and Design Cycle 2.2 Research Questions 3 Cyber-Physical Systems 3.1 Explanation 3.2 Safety- and Security-Critical Aspects 3.3 Risk 3.3.1 Quantitative Risk Assessment 3.3.2 Qualitative Risk Assessment 3.3.3 Risk Reduction Mechanisms 3.3.4 Acceptable Residual Risk 3.4 Engineering Principles 3.4.1 Safety Principles 3.4.2 Security Principles 3.5 Cyber-Physical System of Systems (CPSoS) 3.5.1 Emergence 4 Protective Shell 4.1 Explanation 4.2 System Architecture 4.3 Run-Time Monitoring 4.4 Definition 4.5 Expectations / Goals 5 Specific Protective Shells 5.1 Boeing 737 Max MCAS 5.1.1 Introduction 5.1.2 Vulnerabilities within CPS 5.1.3 Specific Protective Shell Mitigation Mechanisms 5.1.4 Protective Shell Evaluation 5.2 Therac-25 5.2.1 Introduction 5.2.2 Vulnerabilities within CPS 5.2.3 Specific Protective Shell Mitigation Mechanisms 5.2.4 Protective Shell Evaluation 5.3 Stuxnet 5.3.1 Introduction 5.3.2 Exploited Vulnerabilities 5.3.3 Specific Protective Shell Mitigation Mechanisms 5.3.4 Protective Shell Evaluation 5.4 Toyota 'Unintended Acceleration' ETCS 5.4.1 Introduction 5.4.2 Vulnerabilities within CPS 5.4.3 Specific Protective Shell Mitigation Mechanisms 5.4.4 Protective Shell Evaluation 5.5 Jeep Cherokee Hack 5.5.1 Introduction 5.5.2 Vulnerabilities within CPS 5.5.3 Specific Protective Shell Mitigation Mechanisms 5.5.4 Protective Shell Evaluation 5.6 Ukrainian Power Grid Cyber-Attack 5.6.1 Introduction 5.6.2 Vulnerabilities in the critical Infrastructure 5.6.3 Specific Protective Shell Mitigation Mechanisms 5.6.4 Protective Shell Evaluation 5.7 Airbus A400M FADEC 5.7.1 Introduction 5.7.2 Vulnerabilities within CPS 5.7.3 Specific Protective Shell Mitigation Mechanisms 5.7.4 Protective Shell Evaluation 5.8 Similarities between Specific Protective Shells 5.8.1 Mitigation Mechanisms Categories 5.8.2 Explanation 5.8.3 Conclusion 6 AI 6.1 Explainable AI (XAI) for Anomaly Detection 6.1.1 Anomaly Detection 6.1.2 Explainable Artificial Intelligence 6.2 Intrinsic Explainable ML Models 6.2.1 Linear Regression 6.2.2 Decision Trees 6.2.3 K-Nearest Neighbours 6.3 Example Use Case - Predictive Maintenance 7 Generic Protective Shell 7.1 Architecture 7.1.1 MAPE-K 7.1.2 Human Machine Teaming 7.1.3 Protective Shell Plugin Catalogue 7.1.4 Architecture and Design Principles 7.1.5 Conclusion Architecture 7.2 Implementation Details 7.3 Evaluation 7.3.1 Additional Vulnerabilities introduced by the Protective Shell 7.3.2 Summary 8 Conclusion 8.1 Summary 8.2 Research Questions Evaluation 8.3 Contribution 8.4 Future Work 8.5 Recommendatio

Eddy current defect response analysis using sum of Gaussian methods

This dissertation is a study of methods to automatedly detect and produce approximations of eddy current differential coil defect signatures in terms of a summed collection of Gaussian functions (SoG). Datasets consisting of varying material, defect size, inspection frequency, and coil diameter were investigated. Dimensionally reduced representations of the defect responses were obtained utilizing common existing reduction methods and novel enhancements to them utilizing SoG Representations. Efficacy of the SoG enhanced representations were studied utilizing common Machine Learning (ML) interpretable classifier designs with the SoG representations indicating significant improvement of common analysis metrics

Integrating Six Sigma into a Quality Management System in the Medical Device Industry

Six Sigma is a valuable management strategy to improve business processes, reduce development and production costs, increase profit margin and improve customer satisfaction. The purpose of this paper is to describe how applicable Six Sigma concepts may complement and support formal quality management systems (QMS) in the medical device industry. A significant number of issues, which increase the development costs and times, is often found during different phases of a medical device life cycle. Some defects with high patient safety risk may result in dangerous and very costly product recalls. The basic idea of this paper is to explore the possibilities of integrating Six Sigma techniques with an existing QMS throughout the entire life cycle of a medical device. This paper addresses how Six Sigma techniques, when appropriately integrated into the QMS at medical device companies, can eliminate defects earlier in the medical device life cycle, identify major opportunities for cost savings, focus on customer needs and expectations, and improve the overall business processes

History Based Multi Objective Test Suite Prioritization in Regression Testing Using Genetic Algorithm

Regression testing is the most essential and expensive testing activity which occurs throughout the software development life cycle. As Regression testing requires executions of many test cases it imposes the necessity of test case prioritization process to reduce the resource constraint. Test case prioritization technique schedule the test case in an order that increase the chance of early fault detection. In this paper we propose a genetic algorithm based prioritization technique which uses the historical information of system level test cases to prioritize test cases to detect most severe faults early. In addition the proposed approach also calculates weight factor for each requirement to achieve customer satisfaction and to improve the rate of severe fault detection. To validate the proposed approach we performed controlled experiments over industry projects which proved the proposed approach effectiveness in terms of average percentage of fault detected