Optimal Process Plan Research Articles

A knowledge graph-based intelligent planning method for remanufacturing processes of used parts

Intelligent remanufacturing process planning is crucial for the efficient and high-quality remanufacturing of used parts with complex failure characteristics. However, due to the varied failure characteristics of used parts, the diversity of remanufacturing processes, and complex non-linear relationships among remanufacturing process elements, relying solely on mathematical programming or manual empirical is difficult to effectively model and optimise the remanufacturing process planning. To this end, a knowledge graph-based intelligent planning method for remanufacturing processes is proposed to enhance efficiency and quality by combining mathematical programming and knowledge reuse. Firstly, with failure characteristics as decision nodes, a full-element remanufacturing process ontology model is constructed, linking used parts, failure characteristics, and corresponding process plans. The BERT-BiLSTM-CRF model extracts remanufacturing process entities, and a remanufacturing process knowledge graph (RPKG) is constructed. Secondly, an intelligent decision-making model based on graph multi-node path retrieval is proposed. Aim to minimise carbon emissions, time, and cost, combining feature similarity calculations and nearest neighbour search (NNS) to efficiently retrieve the optimal process plan for each failure characteristic. Then, the optimal process plans are merged based on process constraints to create the complete plan. Finally, a concrete case is given to verify the effectiveness and advantages of this method.

Optimizing quality and cost in remanufacturing under uncertainty

AbstractIn the context of growing sustainability demands, businesses are increasingly adapting their production practices by integrating remanufacturing. However, companies often face challenges in profitably implementing remanufacturing due to complexities arising from uncertainties in processes, product quality, and market conditions. This highlights the need for effective decision support in remanufacturing processes. Addressing this challenge, our research introduces an algorithm designed to identify cost-efficient process plans that optimize order fulfillment while considering a company’s specific capabilities and inventory levels. By modeling the remanufacturing planning process as a Markov process, our algorithm comprehensively accounts for both process-related and quality-related uncertainties. This approach enables the evaluation of all Pareto optimal process plans in terms of cost efficiency and reliability. We validate our methodology through a real-world application in the automation industry, specifically focusing on the remanufacturing of variable speed drives. This case study demonstrates the practical relevance of our approach and a potential for significant cost reductions, enhanced process efficiency, and improved labor productivity. Overall, businesses gain critical insights into the financial prospects of their remanufacturing efforts, identifying opportunities for optimization and expansion into new product quality categories. This enhances their economic potential and aligns with consumer preferences for distinct product qualities.

Preoperative chemo-CIRT in Re/BRe pancreatic cancer: Insights from a multicenter prospective phase II clinical study (NCT03822936).

There is debate about the optimal management of borderline resectable (bRe) and resectable (Re) pancreatic ductal adenocarcinoma (PDAC). Both preclinical and clinical evidence showed that carbon ion radiotherapy (CIRT) produces superior control on radioresistant histologies compared to conventional photon beam radiotherapy (RT). However, so far there is a lack of data concerning the integration of CIRT in a multimodal approach with chemotherapy and surgery for bRe/Re. We recently presented the first analysis of a multicenter prospective phase II clinical study aimed at assessing the feasibility and effectiveness of a neoadjuvant chemotherapy + short course of CIRT followed by surgery and adjuvant chemotherapy in the management of bRe/Re PDAC. The study was terminated early due to low patient enrollment.Herein, we reported a post-hoc analysis focusing on toxicity, dosimetry and translational assessment. In our experience, CIRT can be integrated into a multimodal treatment strategy for bRe/Re PDAC, alongside chemotherapy and surgery. A case of fatal liver failure occurring three months post-surgery has been documented, likely related to the combination approach. Although the treatment plans were satisfactory according to the Local Effect Model (LEM) model, recalculations using the modified Microdosimetric Kinetic Model (mMKM) revealed suboptimal target coverage. Additionally, we observed an increased expression of PD-L1 following CIRT. This multimodal approach was well tolerated; however, clinicians should carefully monitor for vascular disorders during follow-up and further investigate surgical techniques after CIRT. The increased PD-L1 expression supports the immunogenic effects of particle therapy and lays the groundwork for future studies. To enhance the therapeutic ratio of CIRT treatments, integrating dose-averaged LETd (LETd-based objectives into the plan optimization process should be considered. ClinicalTrials.gov Identifier: NCT03822936.

Control and optimization of defects in die casting of complicated right crankcase cover

The die-casting process of ADC12 aluminum alloy right crankcase cover was simulated based on AnyCasting software, and the influence of pouring temperature, initial mold temperature and injection speed on the filling and solidification process of ADC12 right crankcase cover casting was explored by orthogonal experiment method. The optimal process parameters were obtained through optimization, and the die-casting mold was designed. The program was verified in production. The results show that the porosity and shrinkage of the right crankcase cover are mainly concentrated in the thick wall part. The sequence of process parameters that affect the residual melt modulus is pouring temperature, initial mold temperature, and injection speed; The best combination of process parameters: pouring temperature is ∼680 °C, initial mold temperature is ∼200 °C, injection speed is ∼5 m/s. By analyzing the filling process and temperature field simulation results, the casting structure, mold structure and cooling system were optimized, and the process plan was improved. The production verification was carried out according to the optimized process plan. The casting quality is good and has no obvious defects, which meets the actual production needs.

HYBRID PLANNING A SIMPLE TECHNIQUE FOR CRANIOSPINAL IRRADIATION (CSI)

Purpose: A simple planning technique for craniospinal irradiation using Eclipse treatment planning system. Material and methods: In RT treatment planning, base plan optimization feature is used in sequential RT planning by anticipating the dose delivered to organ at risk (OAR) & planning target volume (PTV) in the base plan. In hybrid planning technique, the whole PTV is divided into two parts, Brain PTV & Spine PTV. Spine PTV is overlapped minimum 2-3 cm over the Brain PTV at their junction while creating structures for RT planning. Brain PTV is planned with conventional RT technique & the Spine PTV planned with rapid arc technique (VMAT). In the plan optimization process of spine PTV, first base plan is selected then the optimization parameter is set accordingly as per total prescribed dose of 36 Gy for both the PTVs. The base plan is the dose calculated plan which is incorporated in the rapid arc plan optimization hence the pre-existed doses of Brain PTV & OAR help to understand the plannerfor achieving desired planning objectives during Spine PTV, RT plan optimization. Finally for the dose calculation of Spine PTV RT plan, planner has to copy fields of Brain PTV & paste it in Spine PTV plan and need to calculate dose for these copied fields. The final plan contains a dose calculated of Brain PTV fields and Spine PTV fields termed as hybrid plan. Result: Reduction in the total number of monitor units is observed for the hybrid plan compared to single VMAT plan, which is statistically significant (p<0.05). Conclusion: Present study introduced the hybrid planning technique which can exploit benefits of conventional and modern techniques.

Pilot study to assess the early cardiac safety of carbon ion radiotherapy for intra- and para-cardiac tumours.

Modern photon radiotherapy effectively spares cardiac structures more than previous volumetric approaches. Still, it is related to non-negligible cardiac toxicity due to the low-dose bath of surrounding normal tissues. However, the dosimetric advantages of particle radiotherapy make it apromising treatment for para- and intra-cardiac tumours. In the current short report, we evaluate the cardiac safety profile of carbon ion radiotherapy (CIRT) for radioresistant intra- and para-cardiac malignancies in areal-world setting. We retrospectively analysed serum biomarkers (TnI, CRP and NT-proBNP), echocardiographic, and both 12-lead and 24-hour Holter electrocardiogram (ECG) data of consecutive patients with radioresistant intra- and para-cardiac tumours irradiated with CIRT between June 2019 and September 2022. In the CIRT planning optimization process, to minimize the delivered doses, we contoured and gave ahigh priority to the cardiac substructures. Weekly re-evaluative 4D computed tomography scans were carried out throughout the treatment. Atotal of 16patients with intra- and para-cardiac localizations of radioresistant tumours were treated up to atotal dose of 70.4 Gy relative biological effectiveness (RBE) and amean heart dose of 2.41 Gy(RBE). We did not record any significant variation of the analysed serum biomarkers after CIRT nor significant changes of echocardiographic features, biventricular strain, or 12-lead and 24-hour Holter ECG parameters during 6months of follow-up. Our pilot study suggests that carbon ion radiotherapy is apromising radiation technique capable of sparing off-target side effects at the cardiac level. Alarger cohort, long-term follow-up and further prospective studies are needed to confirm these findings.

Finite element simulation and experimental verification of laser machining of nitinol tubes

Nitinol tubes are widely used to cut various biological stents, especially cardiovascular stents. However, stent fabrication defects, such as residual stress, microcracks, and heat-affected zones (HAZs) from the laser cutting, can lead to severe clinical complications. In this study, a multiphase flow coupling model for fiber laser cutting simulation is established by considering surface tension, gravity, and recoil pressure and combining the characteristics of fluid heat transfer with phase transition dynamics. The accuracy of the model is verified through the fiber laser cutting experiments. The simulation model is used to explore the influence of the key process parameters of laser cutting on the kerf width, stress, temperature distribution, and the HAZ. The microscopic removal mechanism of the material during the laser cutting is revealed, thus minimizing the problems generated during stent forming. The simulation model can provide an optimized process plan for fiber laser cutting.

Improving the performance of deep learning models in predicting and classifying gamma passing rates with discriminative features and a class balancing technique: a retrospective cohort study

BackgroundThe purpose of this study was to improve the deep learning (DL) model performance in predicting and classifying IMRT gamma passing rate (GPR) by using input features related to machine parameters and a class balancing technique.MethodsA total of 2348 fields from 204 IMRT plans for patients with nasopharyngeal carcinoma were retrospectively collected to form a dataset. Input feature maps, including fluence, leaf gap, leaf speed of both banks, and corresponding errors, were constructed from the dynamic log files. The SHAP framework was employed to compute the impact of each feature on the model output for recursive feature elimination. A series of UNet++ based models were trained on the obtained eight feature sets with three fine-tuning methods including the standard mean squared error (MSE) loss, a re-sampling technique, and a proposed weighted MSE loss (WMSE). Differences in mean absolute error, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between the different models.ResultsThe models trained with feature sets including leaf speed and leaf gap features predicted GPR for failed fields more accurately than the other models (F(7, 147) = 5.378, p < 0.001). The WMSE loss had the highest accuracy in predicting GPR for failed fields among the three fine-tuning methods (F(2, 42) = 14.149, p < 0.001), while an opposite trend was observed in predicting GPR for passed fields (F(2, 730) = 9.907, p < 0.001). The WMSE_FS5 model achieved a superior AUC (0.92) and more balanced sensitivity (0.77) and specificity (0.89) compared to the other models.ConclusionsMachine parameters can provide discriminative input features for GPR prediction in DL. The novel weighted loss function demonstrates the ability to balance the prediction and classification accuracy between the passed and failed fields. The proposed approach is able to improve the DL model performance in predicting and classifying GPR, and can potentially be integrated into the plan optimization process to generate higher deliverability plans.Trial registration: This clinical trial was registered in the Chinese Clinical Trial Registry on March 26th, 2020 (registration number: ChiCTR2000031276). https://clinicaltrials.gov/ct2/show/ChiCTR2000031276

Hybrid Model Based Autonomous System for Controlling Double Side Grinding Process

Manufacturing uncertainty is rising due to diverse customer demands and new technologies. These challenges are propelling us into the Industry 4.U (Industrial Revolution 4.0 Upgrade) and Industry 5.0 era, focusing on human-centric manufacturing and value creation, necessitating digital transformation. This transformation relies on the core technologies of the 4th Industrial Revolution (Data Analytics, Connectivity, Autonomy, and Collaboration) to perform value-creating processes autonomously and reliably. To adapt, Industry 5.0-oriented Smart Manufacturing architecture was designed. In this system, process planners collaborate with generative AI to generate optimal process plans based on new specifications, pursuing human-centered manufacturing. The manufacturing system is reconfigured with modular technology, and each process is autonomously controlled as a 4.U/5.0-oriented production system. This paper focuses on developing an autonomous process control system for Industry 4.U/5.0-oriented Smart Manufacturing. The study aims to stabilize the quality of the brake disc grinding process in an automotive parts company. The developed control system has been tested on a car’s brake disc manufacturing line, with experimental results showing the reliability of the DT and AI in controlling the grinding process.

Research on the significance of coupling effects of multiple process parameters in 3D printing based on orthogonal experimental method

Abstract For the semi-cylindrical model, orthogonal experiments and range analysis methods are used to explore the effects of four process parameters, namely layer thickness, printing speed, nozzle temperature, and bottom plate temperature, on the cross-sectional profile error and height error of FDM type 3D printing. This paper summarizes the optimal process plan for various errors and the significance of their impact, providing a certain reference for the coupling mechanism and significance research of process parameters.

An end-to-end deep convolutional neural network-based dose engine for parotid gland cancer seed implant brachytherapy.

Seed implant brachytherapy (SIBT) is a promising treatment modality for parotid gland cancers (PGCs). However, the current clinical standard dose calculation method based on the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) Report oversimplifies patient anatomy as a homogeneous water phantom medium, leading to significant dose calculation errors due to heterogeneity surrounding the parotid gland. Monte Carlo Simulation (MCS) can yield accurate dose distributions but the long computation time hinders its wide application in clinical practice. This paper aims to develop an end-to-end deep convolutional neural network-based dose engine (DCNN-DE) to achieve fast and accurate dose calculation for PGC SIBT. A DCNN model was trained using the patient's CT images and TG-43-based dose maps as inputs, with the corresponding MCS-based dose maps as the ground truth. The DCNN model was enhanced based on our previously proposed model by incorporating attention gates (AGs) and large kernel convolutions. Training and evaluation of the model were performed using a dataset comprising 188 PGC I-125 SIBT patient cases, and its transferability was tested on an additional 16 non-PGC head and neck cancers (HNCs) I-125 SIBT patient cases. Comparison studies were conducted to validate the superiority of the enhanced model over the original one and compare their overall performance. On the PGC testing dataset, the DCNN-DE demonstrated the ability to generate accurate dose maps, with percentage absolute errors (PAEs) of 0.67%±0.47% for clinical target volume (CTV) D90 and 1.04%±1.33% for skin D0.1cc. The comparison studies revealed that incorporating AGs and large kernel convolutions resulted in 8.2% (p<0.001) and 3.1% (p<0.001) accuracy improvement, respectively, as measured by dose mean absolute error. On the non-PGC HNC dataset, the DCNN-DE exhibited good transferability, achieving a CTV D90 PAE of 1.88%±1.73%. The DCNN-DE can generate a dose map in less than 10 ms. We have developed and validated an end-to-end DCNN-DE for PGC SIBT. The proposed DCNN-DE enables fast and accurate dose calculation, making it suitable for application in the plan optimization and evaluation process of PGC SIBT.

First direct machine-specific parameters incorporated in Spot-scanning Proton Arc (SPArc) optimization algorithm.

Spot-scanning Proton Arc (SPArc) has been of significant interest in recent years because of its superior plan quality. Currently, the primary focus of research and development is on deliverability and treatment efficiency. To address the challenges in generating a deliverable and efficient SPArc plan for a proton therapy system with a massive gantry, we developed a novel SPArc optimization algorithm (SPArcDMPO) by directly incorporating the machine-specific parameters such as gantry mechanical constraints and proton delivery sequence. SPArc delivery sequence model (DSMarc) was built based on the machine-specific parameters of the prototype arc delivery system, IBA ProteusONE®, including mechanical constraint (maximum gantry speed, acceleration, and deceleration) and proton delivery sequence (energy and spot delivery sequence, and irradiation time). SPArcDMPO resamples and adjusts each control point's delivery speed based on the DSMarc calculation through the iterative approach. In SPArcDMPO, users could set a reasonable arc delivery time during the plan optimization, which aims to minimize the gantry momentum changes and improve the delivery efficiency. Ten cases were selected to test SPArcDMPO. Two kinds of SPArc plans were generated using the same planning objective functions: (1) original SPArc plan (SPArcoriginal); (2) SPArcDMPO plan with a user-pre-defined delivery time. Additionally, arc delivery sequence was simulated based on the DSMarc and was compared. Treatment delivery time was compared between SPArcoriginal and SPArcDMPO. Dynamic arc delivery time, the static irradiation time, and its corresponding time differential (time differential=dynamic arc delivery time-static irradiation time) were analyzed, respectively. The total gantry velocity change was accumulated throughout the treatment delivery. With a similar plan quality, objective value, number of energy layers, and spots, both SPArcoriginal and SPArcDMPO plans could be delivered continuously within the±1 degree tolerance window. However, compared to the SPArcoriginal, the strategy of SPArcDMPO is able to reduce the time differential from 30.55±11.42%(90±32 s) to 14.67±6.97%(42±20 s), p<0.01. Furthermore, the corresponding total variations of gantry velocity during dynamic arc delivery are mitigated (SPArcoriginal vs. SPArcDMPO) from 14.73±9.14 degree/s to 4.28±2.42 degree/s, p<0.01. Consequently, the SPArcDMPO plans could minimize the gantry momentum change based on the clinical user's input compared to the SPArcoriginal plans, which could help relieve the mechanical challenge of accelerating or decelerating the massive proton gantry. For the first time, clinical users not only could generate a SPArc plan meeting the mechanical constraint of their proton system but also directly control the arc treatment speed and momentum changes of the gantry during the plan optimization process. This work paved the way for the routine clinical implementation of proton arc therapy in the treatment planning system.

Research on optimization method of railway construction scheme based on multidimensional combination weighting and improved grey theory

The optimization of railway construction schemes is a complexity system engineering task with multiple dimensions, diverse conditional constraints, and multifaceted objective assessments. The decision-making and scheme evaluation entail subjectivity, randomness, and fuzziness. To address the comprehensive optimization challenge in construction schemes effectively and efficiently, we investigate an optimization method for railway construction schemes. This method is based on multi-dimensional combination weighting and improved grey theory. After analyzing the primary influencing factors, we established a railway construction plan optimization index system comprising 4 dimensions and 18 factors. The weight combination coefficient is determined using the pros and cons solution distance method, and the optimal weight set for the index is determined through the multi-dimensional combination weighting approach. Utilizing the method of superior and inferior solution distance coupled with grey theory, we ascertain the order of advantages and disadvantages for each construction scheme, subsequently achieving construction scheme optimization. To illustrate this, we employ the optimization process for a high-speed railway section in Guangxi as an exemplar. The verification results indicate that the gray relative closeness values for schemes A, B, and C are 0.7089, 0.4813, and 0.4463, respectively. Scheme A has the highest gray relative closeness value, thus making it the optimal route scheme. The optimal results obtained through this method align with the outcomes of expert validation and existing research, thereby validating the effectiveness and practicality of the model. By employing a multidimensional combination weighting method, the deficiencies of traditional indicator weight calculations are mitigated, resulting in indicator weights that are more reflective of the actual circumstances. At the same time, the application of improvements in the grey theory comprehensive evaluation method enables the integration and computation of indicator data for each construction plan. Through the intuitive representation of grey relative closeness, the advantages and disadvantages of each plan are effectively characterized. This enhances the scientific rigor and applicability of the railway construction plan optimization process. The research findings can serve as a reference for similar railway construction scheme selection problems in the future.

New method to optimize protection of the organs at risk allows to regain coverage and homogeneity in the Planning Target Volume

The construction of auxiliary structures within the Planning Target Volume (PTV) is proposed as a method to recover coverage and homogeneity, and to optimize the protection of the organs at risk (OARs) in radiotherapy treatment plans. To this purpose, it was performed the Volumetric Modulated Arc Therapy (VMAT) treatment planning of the “mock prostate” test. Three plans were optimized in the EclipseTM treatment planning system. In plan 1 (P1), it was defined an initial objective function only for the PTV. Then, the dose received by the OARs was restricted, guaranteeing the PTV level of coverage. Plan 2 (P2) used the same dose-volume objectives for the PTV as in P1, but more restrictive dose constraints were set for the OARs. From the dose distribution obtained in P2, it was built auxiliary subvolumes contained within the PTV to regain coverage and homogeneity in plan 3 (P3). To this end, it was compared the coverage (D95%), the Homogeneity Index (HI) and dose-volume histogram for the PTV, and OARs dose sparing for rectum and Bladder. P1 and P3 resulted in similar PTV coverage and HI values, however, the OARs received a lower dose in P3. Despite in P2 it was achieved a higher OARs protection, the PTV coverage and HI were considerably reduced. In this sense P3 allowed to reach the best balance between coverage, HI, and OARs protection. As a consequence of these results, it has been shown that the introduction of auxiliary structures as target subvolumes constitute a powerful and easy to implement tool in the treatment planning optimization process and can be employed in any pathology that requires VMAT.

Application of the Integrated Deep Reinforcement Learning Solution in Automatic IMRT Planning for Gastric Cancer

The intensity-modulated radiation therapy (IMRT) treatment planning optimization process is usually a manual, tedious and time-consuming task. An unsupervised integrated automatic IMRT planning solution was discussed and implemented to simulate the manual operation during the whole planning process. Based on knowledge-based planning (KBP) and deep reinforcement learning (DRL) schemes, a novel integrated solution combining the multi-objectives optimization policy network (MOPN) and three-dimensional dose prediction module (3D-DPM) was proposed. The MOPN was trained to learn how to adjust multiple optimization objectives in commercial Eclipse treatment planning system (TPS). The 3D-DPM was developed to generate the patient-specific initial optimization objectives to reduce the overall exploration space during MOPN training. The Eclipse 15.6 TPS Scripting Application Programming Interface (ESAPI) was used to realize the automatic interaction between models and TPS. In this study, 100 previously treated gastric cancer cases were selected from the clinical database, 70 cases were used for the training of dose prediction model and MOPN, the remaining 30 cases for evaluating the feasibility and effectiveness of automatic treatment planning solution. For all tested cases, the complete automatic treatment plan for a new case was generated based on the integrated solution, with about 6 min. Compared with the MOPN initial plans, the actual dose of spinal cord, liver, kidney-left, kidney-right, and intestine in the MOPN final plans reduced 42.4%, 25.2%, 52.4%, 37.8%, 11.8% respectively. The dose result of OARs in the MOPN final plans was similar to those in the clinical plans. In addition, a new case was able to complete optimization with about 15 adjustment steps by the trained MOPN. We successfully developed an integrated solution for automatic treatment planning. It first includes the dose prediction module for obtaining patient-specific initial optimization objectives. We demonstrated that the trained MOPN can mimic the operations of the physicians to adjust multiple objectives and obtain a high-quality plan in a shorter time. This integrated solution contributes to improving the efficiency of the overall planning workflow and reducing the variation of plan quality in clinical practice. Although improvement is warranted, the proposed integrated solution is a promising practical and effective approach for automatic planning in commercial TPS.

Improved crow search algorithm for optimal flexible manufacturing process planning

The enhancement of manufacturing processes due to the growing demand for products and services is a problem that must be constantly updated. Several schemas have been conducted to improve the manufacturing processes. These schemas are based on the flexible use of machines, tools, and tool access directions to determine the best process planning (PP) for a specific piece or part. The combination of different tools and machines leads to the formulation of complicated combinatory problems that can be treated as optimization problems. On the other hand, bioinspired optimization techniques have been widely employed for solving several complex engineering and real-life problems showing competitive results. The crow search algorithm (CSA) is an optimization technique based on the intelligent foraging behavior of crows. Our work uses an improved CSA (ICSA) to determine the optimal flexible process planning (FPP) with a low computational effort. Several AND/OR networks FPP problems are employed in the experimentation stage, where the production cost and production time are considered as fitness functions. To determine the performance and accuracy of the proposed technique, different results previously reported in the literature are used for a comparison showing that ICSA-FPP is able to outperform similar approaches.

Optimal Process Planning for Hybrid Additive and Subtractive Manufacturing

AbstractHybrid manufacturing technology has enabled manufacturers to combine advantages of mainly subtractive and additive manufacturing technologies. A single machine supports producing products with complex geometry, at high quality, and with a high degree of automation. To benefit from these advantages, decisions taken in the process planning stage of such a sophisticated manufacturing system should be optimized. The objective of this paper is to determine the optimal process plan considering both the engineering and manufacturing aspects of the hybrid technology. A comprehensive process planning model is proposed. The model specifies the optimal sequence of additive and subtractive features that minimizes the production cycle time. In addition, the model sets the optimal part orientations such that the time needed for building support structures, performing post-processing and inspection operations, changing cutting tools and printing nozzles, and unclamping the part is minimized. The model is comprehensive as it considers productive and non-productive times, precedence, technological, quality, and manufacturing restrictions imposed on hybrid manufacturing systems. The proposed model is nonlinear; due to this nonlinearity, the model is intractable. A linearization scheme is applied to formulate an equivalent linear model that is solvable to optimality by commercial solvers. Case studies on test and industrial parts are provided to evaluate the computational performance of the proposed model. Integrating the proposed model in hybrid manufacturing (HM) systems ensures adopting the HM technology in its optimal direction. HM technology is an enabler of establishing a smart manufacturing system which is one of the pillars of Industry 4.0.

Mathematical programming analysis of additive manufacturing process planning total cost evaluation model for multiple parts

It has been established that an effective and efficient cost evaluation of advanced manufacturing techniques, to a large extent, enhances process planning. In other to achieve optimal advanced manufacturing process planning, a good estimation and evaluation of the process cost are required. In fact, an optimal total processing cost will help to achieve optimal process planning. This study analyzed an adapted advanced manufacturing process planning cost evaluation model, using a mathematical programming technique. The results of the analyses were discussed and represented with graphs, from where conclusions were drawn. The significant status of the different cost components in all the eight cases considered, concerning the achievement of the optimal total cost at the level of planning of the advanced manufacturing process, was revealed by the graphs. Specifically, it was observed that both the processing modules cost and the material handling cost are very significant in minimizing the additive manufacturing process planning total cost of multiple parts.

Unified AC Transmission Expansion Planning Formulation incorporating VSC-MTDC, FACTS devices, and Reactive Power compensation

The main aim of the static Transmission Network Expansion Planning (TNEP) is to determine which and where new transmission equipment must be installed. The complexity added by the non-linearities leads to simplifications, which include the DC model. However, most non-linearities are solvable nowadays. Thus, the new scenario of large non-dispatchable power sources penetration and the several developments in technologies, e.g, Flexible AC Transmission Systems (FACTS) devices and High Voltage Direct Current (HVDC) interconnections, motivate the use of the AC model with its non-linearities. Some research works address the use of some of those technologies for TNEP in an independent fashion, which can lead to sub-optimal solutions. In this work, Voltage Source Controlled-Multiterminal HVDC (VSC-MTDC) systems, FACTS devices, and Reactive Power Planning (RPP) are integrated into the same planning optimization process, so that a unified AC TNEP formulation is proposed. A non-linear mathematical programming technique and a differential evolution based metaheuristics are chosen to achieve an optimal transmission configuration. To evaluate the benefits of the proposed approach, two IEEE modified test systems (9 and 118 buses) are used. Results suggest that more economical solutions can be obtained if different types of reinforcement strategies are taken into account in a unified approach.

Extrusion 3D printing of a multiphase collagen-based material: An optimized strategy to obtain biomimetic scaffolds with high shape fidelity.

Extrusion printing represents one of the leading additive manufacturing techniques for tissue engineering purposes due to the possibility of achieving accurate control of the final shape and porosity of the scaffold. Despite many polymeric materials having already been optimized for this application, the processing of biopolymer-based systems still presents several limitations mainly ascribed to their poor rheological properties. Moreover, the introduction of inorganic components into the biomaterial formulation may introduce further difficulties related to system homogeneity, finally compromising its extrudability. In this context, the present study aimed at developing a new multi-phase biomaterial ink able to mimic the native composition of bone extracellular matrix, combining type-I-collagen with nano-hydroxyapatite and mesoporous bioactive glass nanoparticles. Starting from a comprehensive rheological assessment, computational-fluid-dynamics-based models were exploited to describe the material flow regime and define the optimal printing process planning. During printing, a gelatin-based bath was exploited to support the deposition of the material, while the gelation of collagen and its further chemical crosslinking with genipin enabled the stabilization of the printed structure, characterized by high shape fidelity. The developed strategy enables the extrusion printing of complex multi-phase systems and the design of high-precision biomimetic scaffolds with great potential for bone tissue engineering.