Application of Neural Network in Shop Floor Quality Control in a Make to Order Business

A make to order business has to produce the products that are customized to the customer\u27s current need. The customization can be realized by assembling different standard parts with various \u27configurations\u27. The oil field service industry is a typical example where most products produced are cylindrical assemblies made up of standard parts customized in their size, material specifications, coating specifications, and threading suited for the particular load rating and environment. As business cycles go up and down, hiring and firing of personnel is the routine of the day. Thus, it is very hard to keep experienced inspectors due to high turnover of the staff on shop floor and thus intensive endeavor to train the inspectors for the same recurrent problems of the same standard parts is required. This paper proposes a neural network model to help the industrial practitioners address such a concern. The neural network is trained with ample \u27judgment calls\u27 from the manufacturing experts so that it can properly generate the decision to \u27scrap\u27, \u27rework\u27 or \u27use as is\u27 for the inspected parts. The real quality data from an oil field service industry is used to validate the effectiveness of the proposed tool

Reward/Penalty Design in Demand Response for Mitigating Overgeneration Considering the Benefits from Both Manufacturers and Utility Company

The high penetration of renewable sources in electricity grid has led to significant economic, environmental, and societal benefits. However, one major side effect, overgeneration, due to the uncontrollable property of renewable sources has also emerged, which becomes one of the major challenges that impedes the further large-scale adoption of renewable technology. Electricity demand response is an effective tool that can balance the supply and demand of the electricity throughout the grid. In this paper, we focus on the design of reward/penalty mechanism for the demand response programs aiming to mitigate the overgeneration. The benefits for both manufacturers and utility companies are formulated as the function of reward and penalty. The formulation is solved using particle swarm optimization so that the benefit from both supply side can be maximized under the constraint the benefit of customer side is not sacrificed. A numerical case study is used to verify the effectiveness of the proposed method

Joint Control of Manufacturing and Onsite Microgrid System Via Novel Neural-Network Integrated Reinforcement Learning Algorithms

Microgrid is a promising technology of distributed energy supply system, which consists of storage devices, generation capacities including renewable sources, and controllable loads. It has been widely investigated and applied for residential and commercial end-use customers as well as critical facilities. In this paper, we propose a joint state-based dynamic control model on microgrids and manufacturing systems where optimal controls for both sides are implemented to coordinate the energy demand and supply so that the overall production cost can be minimized considering the constraint of production target. Markov Decision Process (MDP) is used to formulate the decision-making procedure. The main computing challenge to solve the formulated MDP lies in the co-existence of both discrete and continuous parts of the high-dimensional state/action space that are intertwined with constraints. A novel reinforcement learning algorithm that leverages both Temporal Difference (TD) and Deterministic Policy Gradient (DPG) algorithms is proposed to address the computation challenge. Experiments for a manufacturing system with an onsite microgrid system with renewable sources have been implemented to justify the effectiveness of the proposed method

A General Algorithm for Assessing Product Architecture Performance Considering Architecture Extension in Cyber Manufacturing

In modern manufacturing, the product architecture design options are usually restricted to those that can be produced with 100% confidence using those proven technologies to satisfy the existing customer requirement. As a result, the inefficiencies of architecture design are considerable due to such limitations. This issue is of particular interests in cyber manufacturing when exploring the tradeoff between generality and feasibility in product design and manufacturing. It can be expected that the improvement and extension of the existing product architecture may be required to meet new customer requirement when new technologies become available. An effective system performance assessment algorithm is necessary to facilitate the extension of existing product architecture. Though there has been a lot of research on architecture assessment, there is no well-defined model for level by level architecture assessment considering architecture extension. In this paper, we propose a general architecture assessment model considering the integration of additional functionality requirements and performance metrics to evaluate the architecture performance along its value pathway to meet stakeholder\u27s requirements. A numerical case study focusing on a hypothetical auto cooling system is used to validate the effectiveness of the proposed model

Energy Consumption Modeling of Stereolithography-Based Additive Manufacturing Toward Environmental Sustainability

Additive manufacturing (AM), also referred as three-dimensional printing or rapid prototyping, has been implemented in various areas as one of the most promising new manufacturing technologies in the past three decades. In addition to the growing public interest in developing AM into a potential mainstream manufacturing approach, increasing concerns on environmental sustainability, especially on energy consumption, have been presented. To date, research efforts have been dedicated to quantitatively measuring and analyzing the energy consumption of AM processes. Such efforts only covered partial types of AM processes and explored inadequate factors that might influence the energy consumption. In addition, energy consumption modeling for AM processes has not been comprehensively studied. To fill the research gap, this article presents a mathematical model for the energy consumption of stereolithography (SLA)-based processes. To validate the mathematical model, experiments are conducted to measure the real energy consumption from an SLA-based AM machine. The design of experiments method is adopted to examine the impacts of different parameters and their potential interactions on the overall energy consumption. For the purpose of minimization of the total energy consumption, a response optimization method is used to identify the optimal combination of parameters. The surface quality of the product built using a set of optimal parameters is obtained and compared with parts built with different parameter combinations. The comparison results show that the overall energy consumption from SLA-based AM processes can be significantly reduced through optimal parameter setting, without observable product quality decay

Joint Manufacturing and Onsite Microgrid System Control using Markov Decision Process and Neural Network Integrated Reinforcement Learning

Onsite microgrid generation systems with renewable sources are considered a promising complementary energy supply system for manufacturing plant, especially when outage occurs during which the energy supplied from the grid is not available. Compared to the widely recognized benefits in terms of the resilience improvement when it is used as a backup energy system, the operation along with the electricity grid to support the manufacturing operations in non-emergent mode has been less investigated. In this paper, we propose a joint dynamic decision-making model for the optimal control for both manufacturing system and onsite generation system. Markov Decision Process (MDP) is used to formulate the decision-making model. A neural network integrated reinforcement learning algorithm is proposed to approximately estimate the value function given policy of MDP. A case study based on a manufacturing system as well as a typical onsite microgrid generation system is conducted to validate the proposed MDP model as well as the solution strategy

A Model to Estimate the Lifetime of BESS for the Prosumer Community of Manufacturers with OGS

Onsite generation system (OGS) with renewable sources for modern manufacturing plant is considered as a critical alternative energy source for the manufacturers. Prosumer community can be formed by aggregating such manufacturers to achieve a mutual goal of sustainable and resilient power system. As the sustainability of the network depends on the reliable operations of each component in the network, it is required to monitor the performance and lifetime of the components existed in the network. One of the critical as well as costly components used to enhance the reliability and performance of the network is the battery energy storage system (BESS). The paper proposes a lifetime estimation model for the BESS using an integrated approach of cellular automata and system dynamic (SD) to prevent any sudden power outage and build a reliable energy management framework for the community. The major factors such as energy demand of the manufacturing plant, intermittent generation from the OGS, energy sharing capability of the prosumers etc. are considered to simulate the model and determine the amount of battery degradation. Based on the estimated lifetime of the battery, the manufacturers further can control the energy management plan (charging/discharging scheme) to prolong the battery lifetime and ensure a reliable operation for the community. A numerical case study is simulated to illustrate the effectiveness of the model

Design the Capacity of Onsite Generation System with Renewable Sources for Manufacturing Plant

The utilization of onsite generation system with renewable sources in manufacturing plants plays a critical role in improving the resilience, enhancing the sustainability, and bettering the cost effectiveness for manufacturers. When designing the capacity of onsite generation system, the manufacturing energy load needs to be met and the cost for building and operating such onsite system with renewable sources are two critical factors need to be carefully quantified. Due to the randomness of machine failures and the variation of local weather, it is challenging to determine the energy load and onsite generation supply at different time periods. In this paper, we first propose time series models to describe and predict the variation of the energy load of manufacturing system and the irradiation of solar energy. After that, a case study utilizing the predicted data is implemented. The case study includes different scenarios with respect to generation capacities, considering different predicted energy loads from manufacturing system. The cost for building and running such an onsite generation system and its corresponding service level are examined and discussed

A Framework of Integrating Manufacturing Plants in Smart Grid Operation: Manufacturing Flexible Load Identification

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation may occur in the real-time market and the dispatching plan needs to be adjusted to respond to the variation. To reduce the gap between the day-ahead and real-time dispatching plans, a modified framework, i.e., a three-settlement process considering the integration of the manufacturing plants into the existing two-settlement process is proposed in this study. The manufacturing end-use customers report the flexibility of their loads to the ISO so that the ISO can update the day-ahead price through an updated dispatching plan that utilizes the feedback of the load flexibility from the manufacturers. A mathematical model is developed to identify the flexible and non-flexible loads of the manufacturers. Particle Swarm Optimization (PSO) is used to solve this mathematical model and a case study is conducted to illustrate the effectiveness of the model