Process Optimization Framework Research Articles

Integration of Data-Driven Process Re-Engineering and Process Interdependence for Manufacturing Optimization Supported by Smart Structured Data

Process re-engineering and optimization in manufacturing industries is a big challenge because of process interdependencies characterized by a high failure rate. Research has shown that over 70% of approaches fail because of complexity as a result of process interdependencies during the implementation phase. This paper investigates data from a manufacturing operation and designs a filtration algorithm to analyze process interdependencies as a new approach for process optimization. The algorithm examines the data from a manufacturing process to identify limitations through cause and effect relationships and implements changes to achieve an optimized result. The proposed cause and effect approach of re-engineering is termed the Khan-Hassan-Butt (KHB) methodology, and it can filter the process interdependencies and use those as key decision-making tools. It provides an improved process optimization framework that incorporates data analysis along with a cause and effect algorithm to filter out the process interdependencies as an approach to increase output and reduce failure factors simultaneously. It also provides a framework for filtering the manufacturing data into smart structured data. Based on the proposed KHB methodology, the study investigated a production line process using the WITNESS Horizon 22 simulation package and analyzed the efficiency of the proposed approach for production optimization. A case study is provided that integrated the KHB methodology with data-driven process re-engineering to analyze the process interdependencies to use them as decision-making tools for production optimization.

Method for Attaining Dimensionally Accurate Conditions for High-Resolution Three-Dimensional Printing Ceramic Composite Structures Using MicroCLIP Process.

Continuous liquid interface production (CLIP) utilizes projection ultraviolet (UV) light and oxygen inhibition to transform the sequential layered three-dimensional (3D) manufacturing into a continuous fabrication flow with tremendous improved fabrication speed and structure integrity. Incorporating ceramic particles to the photo-curable polymers allows for additive manufacturing of ceramic parts featuring sophisticated geometries, mitigating the difficulties associated with traditional manufacturing processes. The presence of ceramic particles within the ink, however, strongly scatters the incident UV light. In the high-resolution CLIP (microCLIP) process, the scattering effect can significantly alter the process characteristics, resulting in broadening of lateral feature dimensions alongside curing depth reduction. Varying exposure conditions to accommodate scattering additionally affects the oxygen deadzone thickness (DZ), which is dependent on power of incident light. This introduces a systematic defocusing error for large deadzone thickness to further complicate process control, such as the unwanted narrowing of part features. In this work, we developed a systematic framework for process optimization by balancing those effects via experimental characterization. We showed that the reported method can provide a set of optimal process parameters (UV power and stage speed) for high-resolution 3D fabrication in accommodating the distinct characteristics of given photo-curable ceramic ink. The method to optimize process parameter was validated experimentally via fabricating a gradient index Luneburg lens comprising densely packed woodpile building-blocks with a strut width of 100 μm and a layer thickness of 60 μm using microCLIP at dimensionally accurate exposure conditions.

Multi-objective accelerated process optimization of mechanical properties in laser-based additive manufacturing: Case study on Selective Laser Melting (SLM) Ti-6Al-4V

Process optimization of Laser-Based Additive Manufacturing (LBAM) systems is often complicated by the tradeoff between different mechanical properties as well as the relative density window. For instance, parts with similar relative densities can have noticeably different tensile mechanical properties (e.g., elongation-to-failure, yield strength, ultimate tensile strength, Young’s modulus). This phenomenon can be attributed to the variation of size and distribution of fabrication-induced voids within the final parts. To overcome the aforementioned challenge, we applied an efficient sequential multi-objective process optimization framework to optimize the quality of LBAM-fabricated parts with respect to multiple non-correlated mechanical properties within the optimal relative density regime. The applied Multi-objective Accelerated Process Optimization (m-APO) method indirectly accounts for the effect of size and distribution of voids on the final parts’ mechanical properties. The m-APO decomposes the master multi-objective optimization problem into a sequence of single-objective sub-problems constructed from mathematically convex combination of individual unknown objective functions. At each step, the m-APO smartly maps the experimental data from previous sub-problems to the remaining sub-problems. Therefore, the information captured from previous sub-problems is leveraged to accelerate the master multi-objective process optimization problem. The m-APO exhibited capability to achieve a set of process parameter setups, resulting in the best trade-off between conflicting mechanical properties in the optimal window. The m-APO methodology is employed to maximize relative density and elongation-to-failure of Ti-6Al-4 V parts fabricated by Selective Laser Melting (SLM) system. The results show that the m-APO achieved the optimal process parameter setups while reducing the time-and cost-intensive experiments by 51.8%, compared with an extended full factorial design of experiments plan.

Process Optimization Based on Knowledge Flow in Engineering Change

In order to improve the efficiency of engineering change (EC) processes, the mechanism of knowledge flow in EC process is described via analyzing knowledge transformation and transmission between knowledge carriers. Given the parallel and discrete feature of EC activities and the tacit/explicit status variation of intellectual content, a modeling approach based on improved Petri net is proposed for the knowledge flow in EC process. The four kinds of knowledge activity unit (KAU) are used to depict the tacit/explicit status change in knowledge transmission process. A knowledge–flow–oriented Petri net (KF-OPN) model is established subsequently. Based on the KF-OPN model the knowledge transfer path searching algorithm is proposed, and the EC process optimization framework is constructed. A case of EC process in an aircraft manufacture enterprise was studied to illustrate the effectiveness and feasibility of the proposed method.

Darwin at High Temperature: Advancing Solar Cell Material Design Using Defect Kinetics Simulations and Evolutionary Optimization

Material defects govern the performance of a wide range of energy conversion and storage devices, including photovoltaics, thermoelectrics, and batteries. The success of large‐scale, cost‐effective manufacturing hinges upon rigorous material optimization to mitigate deleterious defects. Material processing simulations have the potential to accelerate novel energy technology development by modeling defect‐evolution thermodynamics and kinetics during processing of raw materials into devices. Here, a predictive process optimization framework is presented for rapid material and process development. A solar cell simulation tool that models defect kinetics during processing is coupled with a genetic algorithm to optimize processing conditions in silico. Experimental samples processed according to conditions suggested by the optimization show significant improvements in material performance, indicated by minority carrier lifetime gains, and confirm the simulated directions for process improvement. This material optimization framework demonstrates the potential for process simulation to leverage fundamental defect characterization and high‐throughput computing to accelerate the pace of learning in materials processing for energy applications.

An efficient multi criteria process optimization framework: Sustainable improvement of the Dimethyl Ether Process

No longer is it appropriate to evaluate processes for economic feasibility alone; social benefits and environmental impacts must also be considered. This work develops a multi criteria systematic framework for optimum process design with sustainability considerations and addresses a major challenge in process design as engineers will now be able to design for multiple criteria, specifically sustainability concerns. A novel multi objective optimization algorithm capable of handling a multitude of objectives in mixed integer nonlinear search space and a novel decision making methodology are integrated with a sequential modular process simulator to optimize processes with a multitude of objectives rigorously and efficiently. The application of this research extends well beyond sustainability considerations to the very vast area of optimum design with multi criteria. The application of the proposed framework is demonstrated through a case study for process retrofit of Dimethyl Ether production plant.

Methodological approach to efficient modeling and optimization of thermal processes taking place in a die: Application to pultrusion

Optimization of manufacturing processes involves the optimal choice of many process parameters. Usual strategies proceed by defining a trial choice of those parameters and then solving the resulting model. Then, an appropriate cost function is evaluated and its optimality checked. While the optimum is not reached, the process parameters should be updated by using an appropriate optimization procedure, and then the model must be solved again for the updated process parameters. Thus, a direct numerical solution is needed for each choice of the process parameters, with the subsequent impact on the computing time. In this work we propose a methodological approach to the efficient numerical modeling and optimization of thermal processes taking place in a die. This scenario is usually encountered in polymer and composites processing where material flows inside a die equipped with different heating devices. An example of such kind of processes concerns the pultrusion of composites. The main aim of this work is to described an original approach for modeling and then optimizing the thermal process by solving only once the thermal model, and then, optimizing the process without the necessity of performing new solutions of the thermal model. For this purpose we introduce the temperatures of the heaters as extra-coordinates in the thermal model. The solution of the resulting multi-dimensional heat equation gives the temperature field for any choice of the temperature prescribed in the heaters. The curse of dimensionality is circumvented by invoking the Proper Generalized Decomposition – PGD – introduced in our former works but never until now used in the framework of process optimization.

Medical diagnostic process optimization through the semantic integration of data resources

In this paper we study the optimization of medical diagnostic process from the data access point of view. According to many studies which showed that optimized diagnostic process can considerably improve efficiency in health care industry, we present a new approach to data integration within a diagnostic process. It is our belief that a unified access to data resources throughout the whole diagnostic process considerably improves the efficiency of the process itself. When combining the optimized data access with an existing algorithmic optimization method an optimized process can be achieved that takes into account the quality of a diagnosis, the individual needs of each patient, the associated costs, and the utilization of personnel/equipment. To enable an efficient management of data, we developed a semantic web based system for the integration of data resources within a medical diagnostic process. Then we combined the unified data access with our existing diagnostic process optimization framework that uses machine learning techniques and evolutionary algorithms. The new defined diagnostic process framework is finally used in a case-study for optimizing the diagnosing of the mitral valve prolapse syndrome in a regional hospital department.

Optimizing control based on output feedback

In the framework of process optimization, the use of measurements to push the plant to the economically optimal operating point has re-emerged as an active area of research. One of the ideas therein is to adapt the inputs in order to track the active constraints and push certain sensitivities to zero. There are several ways of doing this. With perturbation-based optimization, the sensitivities are evaluated by perturbation of the inputs and measurement of the cost function. However, since more measurements (typically the outputs) than just the cost function are available, the idea developed in this paper is to use both the nominal model and measured outputs to optimize the process. This is done by extending the neighboring-extremal scheme to the case of output measurements. In the case of parametric uncertainty, and if measurement noise is negligible, the approach is shown to converge to the optimum in at most two iterations. The effect of measurement noise is also investigated. The use of neighboring-extremal output feedback for optimization is illustrated via the simulation of a continuous chemical reactor.

Tracking the necessary conditions of optimality with changing set of active constraints using a barrier-penalty function

In the framework of process optimization, the use of measurements to compensate the effect of uncertainty has become an active area of research. One of the ideas therein is to enforce optimality by tracking the necessary conditions of optimality (NCO tracking). Most techniques assume that the set of active constraints remains the same even in the presence of uncertainty and disturbances. Consequently, changes in the active set are difficult to handle. In this paper, this assumption on active set tracking is relaxed by using a logarithmic-linear barrier-penalty function. This way, none of the constraints is active and no assumption regarding the active set is required. Optimization with this barrier-penalty function is shown to have the same convergence properties as optimization with the standard barrier function while, at the same time, avoiding a separate logic to guarantee feasibility. Thus, the adaptation can be more aggressive and lead to better performance. The gradient of the augmented objective function is computed using finite perturbations and forced to zero with PI-type controllers. The approach is illustrated in simulation via the static optimization of an isothermal continuous stirred-tank reactor.

REAL-TIME OPTIMIZATION OF CONTINUOUS PROCESSES VIA CONSTRAINTS ADAPTATION

In the framework of process optimization, measurements can be used to compensate for the effect of uncertainty. The method studied in this paper combines a process model and measurements to iteratively improve the operation of continuous processes. Unlike many existing real-time optimization schemes, the measurements are not used to update the process model, but to adapt the constraints in the optimization problem. Upon convergence, all the constraints are respected even in the presence of large model mismatch. Moreover, it is shown that constraints adaptation can handle changes in the set of active constraints. The approach is illustrated, via numerical simulation, for the optimization of a continuous stirred-tank reactor.

Maintenance-based strategies for environmental risk minimization in the process industries

Industry, environmental agencies and the scientific community have all emphasized the need to include environmental impact considerations next to profitability objectives in the design phase of modern chemical processes, responding to the increasing social concern over environmental degradation in the past years. Most environmental impact assessment and minimization approaches, however, are rather qualitative, providing general guidelines. In this work, to overcome their limitations and rigorously represent the defining elements of environmental risk, i.e. the mechanism of occurrence of unexpected events usually related to equipment failure and the severity of their consequences, detailed process, reliability and maintenance characteristics are incorporated within a process optimization framework. The objective concerns the optimization of overall process performance defined as a system effectiveness vector characterized by both the environmental and the profitability functions of the system. Implementation of the framework on a process example identifies the optimal combination of process design and operation as well as preventive maintenance strategies that accomplish the conflicting environmental and profitability targets and quantifies the existing trade-offs between them.

A SYSTEMATIC STRATEGY FOR OPTIMIZING MANUFACTURING OPERATIONS

A manufacturing optimization strategy is developed and demonstrated, which combines an asset utilization model and a process optimization framework with multivariate statistical analysis in a systematic manner to focus and drive process improvement activities. Although this manufacturing strategy is broadly applicable, the approach is discussed with respect to a polymer sheet manufacturing operation. The asset utilization (AU) model demonstrates that efficient equipment utilization can be monitored quantitatively and improvement opportunities identified so that the greatest benefit to the operation can be obtained. The process optimization framework, comprised of three parallel activities and a designed experiment, establishes the process‐product relationship. The overall strategy of predictive model development provided from the parallel activities comprising the optimization framework is to synthesize a model based on existing data, both qualitative and quantitative, using canonical discriminant analysis, to identify main effect variables affecting the principal efficiency constraints identified using AU, operator knowledge and order‐of‐magni‐tude calculations are then employed to refine this model using designed experiments, where appropriate, to facilitate the development of a quantitative, proactive optimization strategy for eliminating the constraints. Most importantly, this overall strategy plays a significant role in demonstrating, and facilitating employee acceptance, that the manufacturing operation has evolved from an experienced‐based process to one based on quantifiable science.

Methodology for Environmental Risk Assessment of Industrial Nonroutine Releases

While increasing social concern and strict legislation have resulted in expanding the conventional design objectives of profitability to include environmental impact and operability aspects, traditional practices regarding environmental risk assessment (ERA) have mainly focused on providing qualitative guidelines to evaluate the likelihood and consequence of undesired events to the environment. By linking process reliability considerations to environmental impact analysis within a process optimization framework, this work presents a systematic method for the quantification and at source minimization of combined adverse environmental effects of routine (i.e., waste water effluent streams) and nonroutine releases (i.e., leaks, emissions from equipment breakdown). Trade-offs are explored regarding cost and routine/nonroutine environmental impact objectives, while opportunities for effective maintenance strategies are identified. The steps of the theoretical analysis and the potential of the proposed methodology are illustrated with two example problems, a simplified chemical reaction-separation scheme and a methane chlorination process.

Minimizing the environmental impact of process Plants: A process systems methodology

A Methodology for Environmental Impact Minimization (MEIM) is presented in this paper. The methodology, which embeds Life Cycle Analysis principles within a process optimization framework, involves proper definition of a consistent boundary around the process of interest, identification of the emissions inventory, quantification of environmental impact via proper metries (for air and water pollution, solid waste, global warming and ozone depletion) and inclusion of environmental criteria within process modelling and optimization tools. A case study, the production of VCM from ethylene, is presented to illustrate the basic features of MEIM.