IC defect sensitivity for footprint-type spot defects

Considering the real employment situation of the disabled, it was concluded that there is demand for a robot system that can be used in a real manufacturing environment. To derive our mission statement, we conducted a survey with three criteria: `A. to assist the disabled as much as possible'; `B. to consider the real situation of employment for the disabled'; and `C. to provide assistance for the most necessary tasks for the disabled in the work space'. Based on the survey results, our goal can be stated as follows: To develop work assistive mobile robots to help persons with limb disabilities obtain jobs in manufacturing environments where they can perform simple manual labor. Here, simple labor includes such tasks as circuit inspection and moving boxes. For our goal, we developed two types of work assistive mobile robots, which are small enough to work in the confined environments of real factories. Work assistive mobile robot type I is equipped with a forklift to move boxes and a robotic arm to help with jobs requiring more dexterity. Robot type II is compact in size, adopting an omni-wheel driving mechanism, and is equipped with a lift having a foldable fork. We gathered feedback from empirical work done on the two robots.

A method based on a deterministic geometrical construction of critical areas is presented for determining the sensitivity of layouts to spot defects. The models for fatal faults considered are bridges and cuts related to patterns in one layer. The approach, based on the concept of susceptible sites, has a complexity O(N log N), where N is the number of line segments. Only two scans are necessary to extract all susceptible sites, which then are used to compute the critical areas for a whole set of points in a domain of defect size. >

Augmented reality (AR) is increasingly adopted in industrial environments as a tool for improving employee training and supporting complex assembly operations. The purpose of this study was to investigate the design, implementation, and strategic potential of AR-based work instructions using Microsoft HoloLens 2 in a real manufacturing environment. The study proposes and applies an integrated evaluation framework combining direct observation, performance evaluation, semi-structured interviews, quantitative SWOT analysis, PDCA-based process assessment, and economic cost analysis to assess AR-based training in a real manufacturing environment. AR training was implemented through Microsoft Dynamics 365 Guides for a standardized assembly procedure and evaluated with respect to training efficiency, user interaction, and feasibility of deployment. The results indicate improved task guidance consistency and descriptive performance indicators, suggesting enhanced training support under real production conditions. The SWOT analysis identified a favorable SO strategic position, highlighting strong internal capabilities and promising external opportunities for further deployment. The cost analysis shows that AR-based training becomes economically advantageous when applied to a larger number of trainees, despite high initial investment costs. Overall, the study demonstrates that AR-based training, when evaluated through a structured strategic and economic framework, represents a promising and strategically advantageous approach for industrial education, provided that ergonomic challenges, user adaptation, and financial constraints are systematically addressed.

In Chapter 10, we presented the host environment for the case study. We now proceed to the application of the architecture, design tools and implementation models in a real manufacturing environment. The PAC development life cycle (outlined in Chapter 9), provides an excellent opportunity to experiment with possible PAC solutions. But, it may not adequately emphasize the difficulties of the transition from PAC simulation to full implementation in a manufacturing site. This migration path is paved with an array of site-specific technical and social parameters within the target environment, including the integration with existing applications (such as MRP and data collection systems), and the promotion of PAC, as a viable operational production control method, among those currently responsible for production operations.

From COTS Simulation Software to an Open-source Platform: A Use Case in the Medical Device Industry

Towards the improvement of tool condition monitoring systems in the manufacturing environment

In a real manufacturing environment, the set of tasks that should be scheduled is changing over the time, which means that scheduling problems are dynamic. Also, in order to adapt the manufacturing systems with fluctuations, such as machine failure and create bottleneck machines, various flexibilities are considered in this system. For the first time, in this research, we consider the operational flexibility and flexibility due to Parallel Machines (PM) with non-uniform speed in Dynamic Job Shop (DJS) and in the field of Flexible Dynamic Job-Shop with Parallel Machines (FDJSPM) model. After modeling the problem, an algorithm based on the principles of Genetic Algorithm (GA) with dynamic two-dimensional chromosomes is proposed. The results of proposed algorithm and comparison with meta-heuristic data in the literature indicate the improvement of solutions by 1.34 percent for different dimensions of the problem.

In this work two built-in database technologies were compared and applied to aggregation of data from a network of sensor equipped data collection nodes in a real manufacturing environment. The methods investigated are: data sharding and foreign data wrappers. The availability and feature set differences of the methods on some popular database management systems was compared and then a detailed case study on PostgreSQL DBMS was performed. For testing the concept in more realistic environment the sensor network was implemented using a number of Raspberry Pi computers with local relational database and with wireless connectivity to main aggregation database running on a much powerful server computer. Both database methods were applied on all nodes and the server and performance was measured for different types of aggregation queries. The very same data aggregation concepts presented here could be applied to any sensor network or to any Internet of things network.

In industrial control, it frequently happens that, while the low-level controller performs very well, high-level supervision is required to maintain good overall performance that is usually beyond the capability of direct machine control. A hybrid control methodology combining conventional and intelligent techniques has been introduced to replace human supervision for a DCS-controlled laminar cooling process. The industrial experiments show the improved performance of the proposed hybrid control model and confirm its validity in a real manufacturing environment. The results can be extended to a wide range of processes with similar features. The methodology is superior in terms of high performance, reliability, simplicity and ease of construction.

Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. In real manufacturing environment, multi-objectives should be taken into consideration simultaneously during the machining process. Meanwhile, the processing time for each job is often imprecise in many real applications. Therefore, multi-objective integrated process planning and scheduling (IPPS) problem with fuzzy processing time is addressed in this paper. The processing time is described as triangular fuzzy number. A multi-objective genetic algorithm (MOGA) is designed to search for the Pareto solutions of multi-objective IPPS problem with fuzzy processing time. An instance has been designed to test the performance of proposed algorithm. The experiment result shows that the proposed MOGA could obtain satisfactory Pareto solutions for the multi-objective IPPS problem with fuzzy processing time.

After an integrated circuit (IC) design is complete, but before first silicon arrives from the manufacturing facility, the design team prepares a set of test patterns to isolate defective parts. Applying this test pattern set to every manufactured part reduces the fraction of defective parts erroneously sold to customers as defect-free parts. This fraction is referred to as the defect level (DL). However, many IC manufacturers quote defective part level, which is obtained by multiplying the defect level by one million to give the number of defective parts per million. Ideally, we could accurately estimate the defective part level by analyzing the circuit structure, the applied test-pattern set, and the manufacturing yield. If the expected defective part level exceeded some specified value, then either the test pattern set or (in extreme cases) the design could be modified to achieve adequate quality. Although the IC industry widely accepts stuck-at fault detection as a key test-quality figure of merit, it is nevertheless necessary to detect other defect types seen in real manufacturing environments. A defective-part-level model combined with a method for choosing test patterns that use site observation can predict defect levels in submicron ICs more accurately than simple stuck-at fault analysis.

In a real manufacturing environment, different parts usually need to be processed using a single resource. Whenever a different part is introduced to a resource a new setup is needed, and this setup includes tool change costs. In most realistic problems the setup costs depend on the type of job just completed as well as on the job to be processed. In general, this problem can be likened to the “travelling salesman problem” that has been described in the literature. In this paper a new heuristic approach is introduced to be used whenever the cost of implementing an optimum seeking method is prohibitive. This method does not guarantee an optimum solution for all problems, but the importance lies in its ability to generate a very good solution, both rapidly and easily.

It is of interest to obtain the thermomechanical response of tools, equipment, and structural components in a real manufacturing environment. Microfabricated thin film thermocouples and strain gages are attractive for their small size and fast response. It is novel and challenging to fabricate these sensors on metal substrate and finally embed them into location of interest. Consequently, the materials (dielectric and metallic) constituting the sensor need to be characterized and optimized properly for reliability. We present here results of characterization and optimization of materials making a thermomechanical sensor to be embedded in metal structure.

Drilling a hole on a curved surface with a desired angle is prone to failure when done manually, due to the difficulties in drill alignment and also inherent instabilities of the task, potentially causing injury and fatigue to the workers. On the other hand, it can be impractical to fully automate such a task in real manufacturing environments because the parts arriving at an assembly line can have various complex shapes where drill point locations are not easily accessible, making automated path planning difficult. In this work, an adaptive admittance controller with 6 degrees of freedom is developed and deployed on a KUKA LBR iiwa 7 cobot such that the operator is able to manipulate a drill mounted on the robot with one hand comfortably and open holes on a curved surface with haptic guidance of the cobot and visual guidance provided through an AR interface. Real-time adaptation of the admittance damping provides more transparency when driving the robot in free space while ensuring stability during drilling. After the user brings the drill sufficiently close to the drill target and roughly aligns to the desired drilling angle, the haptic guidance module fine tunes the alignment first and then constrains the user movement to the drilling axis only, after which the operator simply pushes the drill into the workpiece with minimal effort. Two sets of experiments were conducted to investigate the potential benefits of the haptic guidance module quantitatively (Experiment I) and also the practical value of the proposed pHRI system for real manufacturing settings based on the subjective opinion of the participants (Experiment II). The results of Experiment I, conducted with 3 naive participants, show that the haptic guidance improves task completion time by 26% while decreasing human effort by 16% and muscle activation levels by 27% compared to no haptic guidance condition. The results of Experiment II, conducted with 3 experienced industrial workers, show that the proposed system is perceived to be easy to use, safe, and helpful in carrying out the drilling task.

Background and Objectives: Real-life walking environment comprises various attentionally demanding activities that require intact dual-tasking ability. Dual-task cost is higher in older adults with type 2 diabetes mellitus (T2DM) while walking due to the alterations in cognition and gait. However, contextual variations in the familial, social, and environmental conditions also influence walking in real-life environments. There is a scarcity of literature exploring the challenges linked with contextual variations while dual-task walking in real-life environments for older adults with T2DM. Hence, this study aims to identify barriers and facilitators to dual-task walking in real-life environments for T2DM elderly. Methods: The study used a phenomenological approach and interviewed older adults with and without T2DM using a semi-structured interview guide. It comprised open-ended questions on walking difficulties in a real-life environment. Results: Four themes were identified which comprised individual, social, environmental, and policy-level factors influencing walking. Role transitions within the family, fear, infrastructural impediments, and overprotectiveness of family members were identified as barriers whereas health literacy, positive outlook, occupational demands, and devout engagement in religious participation were identified as facilitators to walking in the elderly. The participants expressed several policy-level challenges, and emphasis was made on outdoor modifications and the need for community programs. Conclusion: The findings of this study provide insights into various constructs close to real-life walking environment that needs to be included in dual-task cost evaluation tools and several measures that can be taken at the familial and policy levels to increase the community mobility of older adults with T2DM.