Doctor of Philosophy, The Ohio State University, 2023, Mechanical Engineering

Traditional design tasks and manufacturing systems often require a multitude of manual efforts to fabricate sophisticated artifacts with desired performance characteristics. Engineers typically iterate on a first principles-based model to make design decisions and then iterate once more by manufacturing the artifacts to take manufacturability into design consideration. Such manual decision-making is inefficient because it is prone to errors, labor intensive and often fails to discover process-structure-property relationships for novel materials. As robotics is an integral part of modern manufacturing, building autonomous robots with decision-making capabilities is of crucial importance for this application domain. Unfortunately, most of the robots and manufacturing systems in the industries lack such cognitive abilities. We argue that this whole process can be made more efficient by utilizing machine learning (ML) approaches, more specifically by leveraging sequential decision-making, and thus making these robots, design processes and manufacturing systems autonomous. Such data-driven decision-making has multiple benefits over traditional approaches; 1) machine learning approaches may discover interesting correlations in the data or process-structure-property relationship, 2) ML algorithms are scalable, can work with high dimensional unstructured problems and learn in highly nonlinear systems where a model is not available or feasible, 3) thousands of man-hour and extensive manual labor can be saved by building autonomous data-driven methods. Due to the sequential nature of the problem, we consider reinforcement learning (RL), a type of machine learning algorithm that can take sequential decisions under uncertainty by interacting with the environment and observing the feedback, to build autonomous manufacturing systems (AMS). Unfortunately, traditional RL is not suitable for such hardware implementation because (a) data collection for AMS is expensive and (b) tradit (open full item for complete abstract)

Committee: David Hoelzle (Advisor); Parinaz Naghizadeh (Committee Member); Jieliang Luo (Committee Member); Kira Barton (Committee Member); Michael Groeber (Committee Member) Subjects: Artificial Intelligence; Design; Mechanical Engineering; Robotics

Master of Science (MS), Wright State University, 2021, Human Factors and Industrial/Organizational Psychology MS

Four interfaces were developed to factorially apply two principles of ecological interface design (EID; direct perception and direct manipulation) to a flexible manufacturing system (FMS). The theoretical foundation and concepts employed during their development, with findings related to more significant issues regarding interface design for complex socio-technical systems, are discussed. Key aspects of cognitive systems engineering (CSE) and EID are also discussed. An FMS synthetic task environment was developed, and an experiment was conducted to evaluate real-time decision support during supervisory operations. Participants used all four interfaces to supervise and maintain daily part production at systematically varied levels of difficulty across sessions. Significant results provide evidence that the incorporation of direct perception and direct manipulation in interface design produced an additive effect, allowing for greater support for the supervisory agents.

Committee: Kevin B. Bennett Ph.D. (Advisor); Scott Watamaniuk Ph.D. (Committee Member); John Flach Ph.D. (Committee Member) Subjects: Experimental Psychology; Psychology; Systems Design

Doctor of Philosophy (PhD), Ohio University, 2019, Industrial and Systems Engineering (Engineering and Technology)

As a strategic decision in the supply chain design, the manufacturing system design impacts the quality, flexibility and the profitability of the entire supply chain. The global supply chain network confronts challenges such as the uncertain market demand and the global trade tariffs. The main goal of this dissertation is to design global supply chain under the stochastic demand considering manufacturing operations and the impact of tariffs. The methodology consists of eight steps. First, the local manufacturer, located in USA, groups the similar products into families to save time, effort and cost. Second, as a clustering approach, the p-median model is studied and then modified to identify families considering the minimum average family similarity. Third, the manufacturer decides the best design for the manufacturing operations, in this step, the classical-cellular manufacturing system is designed under the stochastic market demand. Fourth, the expected revenues generated by the cells open for product families, considering the expected sales and selling prices, are determined. Besides, the expected manufacturing costs including the labor, machine, material and shortage costs are determined as well. Eventually, the expected profits are calculated and the optimal number of cells is identified based on the highest profits generated. Although, the optimal design of the manufacturing system generates higher profits, the demand may not be fully covered.Fifth, based on the optimal design obtained in the third step, the optimal expected profits of the product, based on the scenarios of restrictions on their demand coverage probabilities, are determined by a proposed mathematical model. In this step, if there is no restriction, the maximum profits are made when only one product is produced and sold. This is due to that it has the lowest processing time among all products. When restrictions are applied on the demand coverage, other products are produced and sold; however, l (open full item for complete abstract)

Committee: Gürsel Süer (Advisor); Khurrum Bhutta (Committee Member); Gary Weckman (Committee Member); Tao Yuan (Committee Member); Ashley Metcalf (Committee Member) Subjects: Business Administration; Industrial Engineering; Management; Systems Science

Doctor of Philosophy (PhD), Ohio University, 2016, Mechanical and Systems Engineering (Engineering and Technology)

Global supply chain decisions, such as facility location, manufacturing system design, resource allocation, and distribution center location are long-term strategic decisions in nature and involve many uncertainties. Traditionally, a hierarchical approach is used design supply chain networks and manufacturing systems. First, the location of the facilities are determined, and then the manufacturing systems are designed at the selected locations. In this dissertation, a multi-stage supply chain network model is developed where locations of the plants and inner manufacturing system design are determined simultaneously for labor-intensive manufacturing companies. This dissertation aims to develop a decision making framework to integrate manufacturing systems and supply chain network design decisions considering optimal operator assignment and layered cellular manufacturing in mind. The industry studied is fashion jewelry manufacturing where labor cost is one of the major cost factors. Hence, optimizing the number of workers required for each operation, cell, and plant is critical for the cost efficiency of the entire supply chain. The optimal number of operators are determined for each manufacturing process, and then the optimal cell sizes are found for each manpower level using a heuristic procedure. The optimal number of manufacturing cells required to cover the uncertain demand is determined with mathematical modeling, and the designed layered cellular manufacturing systems for manufacturing stages are evaluated using Arena simulation models. The results of these models and methods are used as inputs while finding the optimal locations of the plants and allocating the optimal number of cells, workers, and machines for each selected plant. Different supply chain design alternatives considering various factors such as the shortest lead times, minimum capacity allocations, and multiple shifts are also studied.

Committee: Gursel A. Suer Ph.D. (Advisor) Subjects: Industrial Engineering; Operations Research

Master of Science (MS), Ohio University, 1999, Electrical Engineering & Computer Science (Engineering and Technology)

Merging data from multiple manufacturing software systems

Committee: Robert Judd (Advisor) Subjects:

Doctor of Philosophy (PhD), Ohio University, 2013, Mechanical and Systems Engineering (Engineering and Technology)

Strategy denotes actions or patterns of actions intended for the attainment of goals. In an organizational setting, the term strategy covers more than just intended or planned strategy; it also includes the sequence of decisions that exhibit posteriori consistencies in decisional behavior, involving the selection of product markets or industries and the allocation of resources among them. Within the broader purpose of developing a decision making framework for competitive strategy development practices, the thrust of this study is to investigate the impact of environmental uncertainty on corporate strategy, and the influence of corporate strategy on business performance, operational structure and market dynamics. Another incidental purpose of the study is to review, classify, clarify, define, and integrate ideas and concepts from diverse disciplines including Engineering, Economics and Business Administration to consequently establish a strategic decision making framework. The factors influencing the short term and long term standing of companies in a particular market are focused with the objectives of increasing the business capability and profitability as well as improving the market share. The case studied is the global blood sugar monitoring industry. The demand structure of the market is modeled considering four major companies in three regional markets; Asia, Europe, North America. LifeScan Inc., a Johnson & Johnson Company, is selected as the focus of greater discussions. The decision making framework is established for LifeScan Inc. incorporating a layered cellular manufacturing design integrated with different supply chain alternatives. The framework is then employed in a multi-period strategic analysis where competition games are developed and studied in three categories; price competition, quality/reputation competition and product competition. The outcomes of different competition strategies are presented and evaluated in terms of profitability and (open full item for complete abstract)

Committee: Gursel Suer (Advisor); Douglas Adie (Advisor); David Koonce (Committee Member); Dusan Sormaz (Committee Member); Namkyu Park (Committee Member); Ana Feger (Committee Member) Subjects: Business Administration; Economics; Industrial Engineering