Integrated product and process development methodologies for environmentally conscious electronic products

This research focuses on integrated product and process development (IPPD) methodologies for environmentally conscious electronic products. After a review of current research issues in the field of product and process development, a generic framework for IPPD is proposed which describes most of the concerned issues formally as constrained optimization problems. These problems may include such optimization objectives as cost, benefit, and environmental impact. Based on this framework, an IPPD methodology is proposed as a systems approach to competitive and environmentally conscious product and process development. A case study on personal computer development is performed illustrating how to apply the methodology meaningfully and efficiently. Eco-compass concept is then integrated into the methodology to evaluate environmental impact, and a case study on business telephone development is performed. To automate the design of products and processes, a solution methodology for IPPD based on logical representation of process relations is proposed with two illustrating product development examples. Finally, a timed IPPD methodology is introduced with increased modeling capability and decision accuracy. It considers the execution duration of processes and their time-varying characteristics. The timed methodology is applied to the life cycle development of flexible manufacturing systems (FMSs) and provides a new way to develop cost-effective, high-quality, and environmentally conscious FMSs

New Vistas in Chemical Product and Process Design

Design of chemicals-based products is broadly classified into those that are process centered and those that are product centered. In this article, the designs of both classes of products are reviewed from a process systems point of view; developments related to the design of the chemical product, its corresponding process, and its integration are highlighted. Although significant advances have been made in the development of systematic model-based techniques for process design (also for optimization, operation, and control), much work is needed to reach the same level for product design. Timeline diagrams illustrating key contributions in product design, process design, and integrated product-process design are presented. The search for novel, innovative, and sustainable solutions must be matched by consideration of issues related to the multidisciplinary nature of problems, the lack of data needed for model development, solution strategies that incorporate multiscale options, and reliability versus predictive power. The need for an integrated model-experiment-based design approach is discussed together with benefits of employing a systematic computer-aided framework with built-in design templates. </jats:p

AN INTEGRATED SYSTEMS ENGINEERING METHODOLOGY FOR DESIGN OF VEHICLE HANDLING DYNAMICS

The primary objective of this research is to develop an integrated system engineering methodology for the conceptual design of vehicle handling dynamics early on in the product development process. A systems engineering-based simulation framework is developed that connects subjective, customer-relevant handling expectations and manufacturers\u27 brand attributes to higher-level objective vehicle engineering targets and consequently breaks these targets down into subsystem-level requirements and component-level design specifications. Such an integrated systems engineering approach will guide the engineering development process and provide insight into the compromises involved in the vehicle-handling layout, ultimately saving product development time and costs and helping to achieve a higher level of product maturity early on in the design phase. The proposed simulation-based design methodology for the conceptual design of vehicle handling characteristics is implemented using decomposition-based Analytical Target Cascading (ATC) techniques and evolutionary, multi-objective optimization algorithms coupled within the systems engineering framework. The framework is utilized in a two-layer optimization schedule. The first layer is used to derive subsystem-level requirements from overall vehicle-level targets. These subsystem-level requirements are passed on as targets to the second layer of optimization, and the second layer derives component-level specifications from the subsystem-level requirements obtained from the first step. The second layer optimization utilizes component-level design variables and analysis models to minimize the difference between the targets transferred from the vehicle level and responses generated from the component-level analysis. An iterative loop is set up with an objective to minimize the target/response consistency constraints (i.e., the targets at the vehicle level are constantly rebalanced to achieve a consistent and feasible solution). Genetic Algorithms (GAs) are used at each layer of the framework. This work has contributed towards development of a unique approach to integrate market research into the vehicle handling design process. The framework developed for this dissertation uses Original Equipment Manufacturer\u27s (OEM\u27s) brand essence information derived from market research for the derivation and balancing of vehicle-level targets, and guides the chassis design direction using relative brand attribute weights. Other contributions from this research include development of empirical relationships between key customer-relevant vehicle handling attributes selected from market survey and the various scenarios and objective metrics of vehicle handling, development of a goal programming based approach for the selection of the best solution from a set of Pareto-optimal solutions obtained from genetic algorithms and development of Vehicle Handling Bandwidth Diagrams

Optimization-Based Architecture for Managing Complex Integrated Product Development Projects

By the mid-1990\u27s, the importance of early introduction of new products to both market share and profitability became fully understood. Thus, reducing product time-to-market became an essential requirement for continuous competition. Integrated Product Development (IPD) is a holistic approach that helps to overcome problems that arise in a complex product development project. IPD emphasis is to provide a framework for an effective planning and managing of engineering projects. Coupled with the fact that about 70% of the life cycle cost of a product is committed at early design phases, the motivation for developing and implementing more effective methodologies for managing the design process of IPD projects became very strong. The main objective of this dissertation is to develop an optimization-based architecture that helps guiding the project manager efforts for managing the design process of complex integrated product development projects. The proposed architecture consists of three major phases: system decomposition, process re-engineering, and project scheduling and time-cost trade-off analysis. The presented research contributes to five areas of research: (1) Improving system performance through efficient re-engineering of its structure. The Dependency Structure Matrix (DSM) provides an effective tool for system structure understanding. An optimization algorithm called Simulated Annealing (SA) was implemented to find an optimal activity sequence of the DSM representing a design project. (2) A simulation-based optimization framework that integrates simulated annealing with a commercial risk analysis software called Crystal Ball was developed to optimally re-sequence the DSM activities given stochastic activity data. (3) Since SA was originally developed to handle deterministic objective functions, a modified SA algorithm able to handle stochastic objective functions was presented. (4) A methodology for the conversion of the optimally sequenced DSM into an equivalent DSM, and then into a project schedule was proposed. (5) Finally, a new hybrid time-cost trade-off model based on the trade-off of resources for project networks was presented. These areas of research were further implemented through a developed excel add-in called “optDSM”. The tool was developed by the author using Visual Basic for Application (VBA) programming language

Innovative Digital Manufacturing Curriculum for Industry 4.0

Manufacturing companies across all major industries are facing serious challenges trying to competitively design and manage modern products, which are becoming increasingly complex multi-domain systems or “systems of systems”. Model-based systems driven product development (or SDPD, for Systems Driven Product Development) has been proposed as a solution based on driving the product lifecycle from the systems requirements and tracing back performance to stakeholders’ needs through a RFLP (Requirement, Functional, Logical, Physical) traceability process. The SDPD framework integrates system behavioral modeling with downstream product design and manufacturing process practices to support the verification/validation of the systems behavior as products progress through all phases of the lifecycle, as well as the optimization of trade-offs decisions by maintaining the cross-product digital twin and thread for global decision optimization in an efficient and effective way. We have developed an innovative digital manufacturing curriculum (designed around the SDPD paradigm) that is based on the digitalization of the SE (Systems Engineering) process through the integration of modelling and simulation continuum, in the form of Model-based Systems Engineering (MBSE), with Product lifecycle management (PLM). At the core of this curriculum is a shift of focus from theory to implementation and practice, through an applied synthesis of engineering fundamentals and systems engineering, that is driven by a state-of-the-art digital innovation platform for product (or system) development consisting of integrated software (digital) tools spanning the complete lifecycle. The curriculum consists of three key components, namely, modelling and simulation continuum, traceability, and digital thread. The curriculum provides a foundation for implementing the digital twin and supports the training of the next generation of engineers for Industry 4.0. The digital manufacturing (or SDPD) framework is applied in the design and optimization of an electric skateboard. The implementation demonstrates: 1) The benefits of digitalization/model-based engineering when developing complex multi-domain products or systems; 2) The ability of students to effectively complete a real-life modern product development within the time line of one semester; 3) The provision of MBSE curriculum for Engineering Education 4.0, characterized by key, integrated skills for the digital enterprise and Industry 4.0

Accelerated process development for integrated end-to-end biologics manufacturing

With the exception of monoclonal antibodies, biologics typically require bespoke manufacturing processes that vary widely in the type of and number of unit operations. This constraint leads to custom facility designs and unique strategies for process development for every new molecule. To enable flexible, multi-product manufacturing facilities and to reduce the speed to clinic for new molecules, streamlined manufacturing processes and associated strategies for process development are needed. We have developed a bench-scale, integrated and automated manufacturing platform capable of rapidly producing a variety of recombinant proteins with phase-appropriate quality for early development1. The system comprises three modules for fermentation via perfusion, straight-through chromatographic purification, and formulation. To facilitate the production of multiple products on the same system, we have also developed a holistic strategy for process design to manufacture new products in as few as twelve weeks after obtaining the product sequence. While upstream process development in our host (Pichia pastoris) has been relatively straightforward, there are not many tools currently available for developing fully integrated straight-through chromatographic processes. Therefore, we developed an in silico tool for the prediction of fully integrated purification processes based on a one-time collection of host-related data combined with conventional high-throughput chromatographic screening data for each new target molecule2. We used this tool to develop fully integrated, end-to-end production processes for three molecules (hGH, IFNα-2b, and G-CSF) with at least 45% fewer steps than traditional processes. While our in silico tool allows for rapid resin selection, it may not predict the optimal process for each individual molecule since it is based on conventional high-throughput screening techniques which seek to optimize each chromatographic step independently rather than optimizing a fully integrated, multi-column process. To address this limitation, we have also developed a DoE-like framework for the optimization of fully integrated purification processes once the resins have been selected. First, a series of range finding experiments are carried out on each individual column, similar to conventional screening but with limited analytics. Next, we carry out fully integrated (multi-column) testing of the proposed operational area with more extensive analytics, including host cell protein, DNA, and yield measurements. We use this methodology to develop optimized processes for the end-to-end production of a variety of single domain antibodies with high yield and purity. Further, we present a method for predicting the optimal operating conditions for a new molecule within the same class based only on its biophysical characteristics, reducing the timeline from sequence to early stage, phase-appropriate product to only six weeks. Using these holistic strategies for process development, we have produced over ten different recombinant proteins on our manufacturing platform including enzymes, cytokines, singe domain antibodies, and vaccine subunits. We believe that such integrated strategies for process design could enable the rapid translation from sequence to early stage clinical development of products for a variety of molecules and potentially allow clinical testing of a greater number of high quality molecules for vaccines and biopharmaceuticals. 1. Crowell, L. E. et al. On-demand manufacturing of clinical-quality biopharmaceuticals. Nat. Biotechnol. (2018). doi:10.1038/nbt.4262 2. Timmick, S. M. et al. An impurity characterization based approach for the rapid development of integrated downstream purification processes. Biotechnol. Bioeng. 1–13 (2018). doi:10.1002/bit.2671

Efficient model driven design of cell-based product manufacturing

Advanced Therapy Medicinal Products (ATMPs) pose a continuing challenge to manufacturing process development. Despite the adoption of a structured approach to development through systematic frameworks such as Quality by Design, a costly rate of process failure or underperformance is encountered at key transitions such as transfer to contract manufacture organizations or changes of scale or equipment. The complexity of the products, particularly the cell culture step, is frequently a contributor to such issues; this complexity challenges many process development tools, in particular the number of potential process variables and consequences results in a lower quality of evidence base informing early risk assessments, creates difficulties in experimental prioritization and efficiency, and can result in poor experimental coherence over the course of product development (i.e. a failure to efficiently harness/record all data and apply to manufacturing goals). We have proposed that rooting development in a suitable process model would lessen these issues. We have developed a framework that takes advantage of the commonality across ATMP manufacturing processes(1). For example, features such as cell growth, paracrine inhibition or lineage selection, and cell death are representable by a limited set of mathematical building blocks. These behaviors interact with process operation to determine critical manufacturing outcomes such as product cost and identity. From an operational perspective there are a limited number of common process operations such as dilutions, purification or factor additions. This enables a modelling framework that can be constrained whilst still representing a wide range of process dynamic hypotheses and associated manufacturing scenarios. Case studies will be presented across a variety of platforms. These include intensification of hematopoietic lineage cell processing in suspension bioreactors (ambr15) including erythroblast and T-cell processing. In each of these cases a model of cell population growth was developed to optimize short term cell volume productivity. This was applied over a longer timeframe to quantify risks (on yield and phenotypic selection) of longer term operational strategies and control such as feed rates or variability in timings and volumes. This provides a basis to specify manufacture based on cost targets, operational constraints (e.g. feed frequency, reactor size) and risk tolerance. We will further present application of the same approach to gain insight into optimization of specific culture phenomena, such as lag phase and growth factor delivery, that have a potentially high impact on manufacturing outcomes. (1) AJ Stacey, EA Cheeseman, KE Glen, RLL Moore, RJ Thomas. Experimentally integrated dynamic modelling for intuitive optimization of cell-based processes and manufacture. Biochem Eng J. 2018. 132: 130-13

Implementasi Framework GTK dalam Pembuatan Aplikasi Desktop Monitoring Mesin Produksi

Desktop-based applications are currently very popular due to several advantages such as being able to access the application offline, having a fast response time, rich user experience, and now they are also developed to improve performance optimization of machines integrated with industrial tools such as automated production process machinery. PT. Stechoq Robotika Indonesia is one of the industrial manufacturing companies that has a main product of Digital Control System. In the process, the application built with desktop and electron framework serves to monitor its product machines. From the observations that have been made by the author, there is a condition that needs improvement, which is the replacement of the electron framework which is considered to require a large amount of resources during desktop application development. Based on the research that has been conducted, the monitoring system that implements the gtk framework as the framework for the desktop monitoring application is more effective and efficient because it has a smaller size of 394.7 kb and uses less CPU at only 0.78%, while the application built using the electron framework has a larger size of 232.2 mb and uses CPU at 1.20%

Life cycle assessment (LCA) applied to the process industry: a review

Purpose : Life cycle assessment (LCA) methodology is a well-established analytical method to quantify environmental impacts, which has been mainly applied to products. However, recent literature would suggest that it has also the potential as an analysis and design tool for processes, and stresses that one of the biggest challenges of this decade in the field of process systems engineering (PSE) is the development of tools for environmental considerations. Method : This article attempts to give an overview of the integration of LCA methodology in the context of industrial ecology, and focuses on the use of this methodology for environmental considerations concerning process design and optimization. Results : The review identifies that LCA is often used as a multi-objective optimization of processes: practitioners use LCA to obtain the inventory and inject the results into the optimization model. It also shows that most of the LCA studies undertaken on process analysis consider the unit processes as black boxes and build the inventory analysis on fixed operating conditions. Conclusions : The article highlights the interest to better assimilate PSE tools with LCA methodology, in order to produce a more detailed analysis. This will allow optimizing the influence of process operating conditions on environmental impacts and including detailed environmental results into process industry