Process Optimization Strategies Research Articles

Hybrid manufacturing thermal environment cycle and industrial economic transformation based on artificial intelligence

With the continuous development of global manufacturing, hybrid manufacturing has become an important way to improve production efficiency and respond to market demand. However, traditional manufacturing enterprises face many challenges in thermal environment management and energy consumption, and urgently need to achieve transformation and upgrading through artificial intelligence technology. This article aims to explore the thermal environment cycle of hybrid manufacturing based on artificial intelligence and its impact on industrial economic transformation, in order to provide theoretical support and practical guidance for manufacturing enterprises in intelligent manufacturing and energy management. We have researched and designed an intelligent manufacturing system, clarified the design principles and requirements of the system, constructed the system architecture, and designed the functional modules in detail. By collecting and processing data, using intelligent scheduling prediction technology to simulate the thermal environment of the manufacturing workshop, and then simulate the energy consumption situation. Finally, a manufacturing workshop scheduling model was constructed, process optimization strategies were proposed, and the transformation path of industrial economy was explored. Research has shown that through the implementation of intelligent manufacturing systems, the energy consumption of manufacturing workshops is significantly reduced, the thermal environment is effectively improved, and the application of process optimization and scheduling models greatly improves production efficiency. The exploration of the path of industrial economic transformation provides new ideas for the sustainable development of enterprises in the new situation.

Data-driven macro-scale simulation for rapid electrolyte wetting in lithium-ion batteries

The electrolyte wetting process in lithium-ion battery manufacturing is a critical part of processes that affects battery performance and productivity. However, it is difficult to accurately measure and optimise this process with existing technologies. In this study, a three-dimensional macroscopic filling and wetting simulation model based on actual battery material parameters is constructed, which can simulate the wetting process of electrolyte in porous electrodes and separators. The model is employed to analyse effects of filling pressure pattern, boundary conditions and other process parameters on the wetting rate. The results demonstrate that the use of multilateral cyclic pressure filling can optimise the electrolyte wetting, which is in accordance with industry practice. Consequently, the study proceeds to investigate potential process optimisation strategies, including electrolyte selection and ambient temperature. These findings not only address limitations of traditional models, which employ simplified parameters, but also present a novel approach to enhance manufacturing efficiency and reduce cost of lithium batteries. This is of paramount importance for the sustainable development of lithium-ion battery industry.

Low temperature powder bed fusion of polyamide 6: transient process characteristics and process-dependent part properties

AbstractThe powder-based additive manufacturing of high-melting polyamides is impeded by unfavorable fracture properties and oxidation tendencies, limiting the technical applicability and the reuse of unfused powder. Overcoming these limitations through new processing strategies provides the opportunity for significantly extending the range of materials applicable for powder bed fusion processes. Based on fractal, locally quasi-simultaneous exposure strategies, a mesoscale compensation of thermal and crystallization-induced shrinkage is obtained, enabling the non-isothermal processing and stable layer formation of polyamide 6 at powder bed temperatures of 25 °C, 50 °C, and 75 °C. Thermographic in situ investigations reveal the layerwise quenching of discrete layers, leading to cooling rates exceeding − 250 K s−1 at 130 °C. Based on microscopic, mechanical, thermal, and spectroscopic investigations, insights into the structure formation and corresponding mechanical properties can be obtained, yielding reduced fractions of α-PA6 while increasing the elongation at break. The inherent layerwise quenching promotes the formation of microspherulitic morphologies while minimizing oxidation-induced material degradation, reflected in infrared spectroscopic material characteristics. Relying on obtained findings, process optimization strategies can be derived that enable the processability of high-melting polyamides at reduced temperatures, reducing the energy consumption of the build process while adapting underlying material characteristics based on accelerated intermediate cooling conditions.

Lean Manufacturing and Process Optimization : Enhancing Efficiency in Modern Production

This comprehensive article explores the transformative impact of lean manufacturing and process optimization in modern industrial settings. It delves into these methodologies' principles, tools, and benefits, highlighting their crucial role in enhancing operational efficiency, reducing costs, and improving product quality. The article presents key statistics and case studies, including Japanese automotive manufacturer's benchmark lean manufacturing model, to demonstrate the significant improvements in productivity, quality, and cost-effectiveness achieved through these approaches. It also discusses the implementation strategies for lean and process optimization, emphasizing the importance of leadership commitment, employee training, and technology integration. Integrating Industry 4.0 technologies with lean principles, termed Lean 4.0, is introduced as the next frontier in operational excellence.

Preparation of continuous carbon fiber reinforced PA6 prepreg filaments with high fiber volume fraction

High-performance continuous carbon fiber prepreg filaments have been a research hotspot in the field of additive manufacturing in recent years, and are considered to be an effective option for improving the mechanical strength of thermoplastic composite parts. However, the effect of fiber volume fraction on the microstructure and tensile properties of 3D printed prepreg filaments remains miss. Therefore, this study selected suitable impregnation processes and materials to prepare prepreg filaments with different fiber volume fraction and investigated their mechanical properties and microscopic morphologies. The results show that the increase of fiber volume fraction effectively promoted the interface bonding between fiber and resin, and increased capacity for load transfer. When the fiber volume fraction was 54.0 %, the tensile strength of the prepreg filaments and specimens reached 1977 MPa and 334 MPa, respectively. This study can provide a process optimization strategy for the preparation of more types of continuous fiber prepreg filaments with high fiber volume fraction, as well as a data reference for the preparation of high-performance 3D-printed thermoplastic composites.

Inverse design of chemoenzymatic epoxidation of soyabean oil through artificial intelligence-driven experimental approach

This paper presents an inverse design methodology that utilizes artificial intelligence (AI)-driven experiments to optimize the chemoenzymatic epoxidation of soyabean oil using hydrogen peroxide and lipase (Novozym 435). First, experiments are conducted using a systematic 3-level, 5-factor Box-Behnken design to explore the effect of input parameters on oxirane oxygen content (OOC (%)). Based on these experiments, various AI models are trained, with the support vector regression (SVR) model being found to be the most accurate. SVR is then used as a fitness function in particle swarm optimization, and the suggested optimal conditions, upon experimental validation, resulted in a maximum OOC of 7.19 % (∼98.5 % relative conversion of oil to epoxy). The results demonstrate the superiority of the proposed approach over existing methods. This framework offers a general intensified process optimization strategy with minimal resource utilization that can be applied to any other process.

AI Challenges and Strategies for Business Process Optimization in Industry 4.0: Systematic Literature Review

The rise of Industry 4.0 has introduced significant transformations in business operations, with Artificial Intelligence (AI) playing a central role in automating processes, enhancing decision-making, and improving efficiency. However, despite its potential, many organizations face critical challenges in implementing AI, particularly in terms of technological infrastructure readiness, workforce capabilities, and ethical considerations. This research aims to identify the challenges and propose effective strategies for AI implementation in business processes, particularly within the context of Industry 4.0. A systematic literature review (SLR) was employed to analyze peer-reviewed studies that discuss AI integration across various industrial sectors. The findings indicate that the primary challenges include the integration of AI with existing legacy systems, data privacy concerns, and employee resistance due to the perceived threat of automation. Additionally, the lack of adequate training and infrastructure investment further hampers AI adoption. In response, the research highlights strategies such as upgrading technological infrastructure, continuous employee training, and the development of clear ethical guidelines to address these challenges. The research contributes by providing a comprehensive framework for businesses to successfully navigate AI implementation, offering a novel perspective on balancing technological advancements with human resource development and ethical concerns, essential for long-term business sustainability in the digital era

Enhancing machining efficiency in hybrid metal matrix composites through EDM parameter optimization via grey relational analysis

In summary, this study utilized stir casting to develop three distinct aluminium hybrid composites incorporating boron carbide and alumina. These composites were identified as Al7075 + 12%B4C + 6%Al2O3, Al7075 + 6%B4C + 6%Al2O3, and Al7075 + 6%B4C + 12%Al2O3. The novelty of this research lies in the unique combination of materials and their proportions, offering enhanced properties yet unexplored in this specific configuration. The research focused on machining these composite materials using EDM and employed a Taguchi L27 orthogonal array design to plan the machining tests. Key process parameters considered were gap voltage (V), duty cycle (%), current (A), and different percentages of reinforcement content. This aspect adds a significant novel dimension to the study, as the impact of EDM machining parameters on hybrid composites with this particular composition has not been extensively explored previously. The study applied Grey Relational Analysis to evaluate the impact of EDM machining process parameters on critical output responses, specifically material removal rate (MRR) and tool wear rate (TWR). SEM images revealed redeposited molten material, craters, and surface cracks, emphasizing the importance of controlling discharge energy and current levels for desired surface quality. An optimal configuration was identified peak current 2 A, pulse duration 100 μs, duty factor 20%, Composition 2 for improved surface attributes. These qualitative and quantitative insights inform process optimization strategies for EDM machining of aluminium hybrid composites.

Interpretable prediction model for decoupling hot rough rolling camber-process parameters

The presence of asymmetrical camber defects in reversible hot rough rolling processes significantly impacts the quality of plate shapes. Understanding and quantifying the intricate relationship between process parameters and camber is essential for developing cost-effective strategies to rectify plate shape defects. Previous research predominantly concentrated on specific couplings between process variables and camber, employing simplified mathematical models with physical mechanisms. However, these approaches may overlook critical factors inherent in the entire rough rolling process, which can substantially influence plate shape deformation. Furthermore, heavy reliance on expertise within the rolling domain may limit the capability and accuracy of coupling analysis models. In this paper, we introduce an Interpretable Prediction Model for Decoupling Camber-process Parameters (called IP-DCP), to investigate the process coupling within the entire rough rolling process. IP-DCP employs conditional modeling to integrate the intricate dependencies of process parameters during rough rolling, dynamically shaping composite features that enhance accurate camber predictions. Additionally, we introduce statistical interpretations for camber predictions using SHapley Additive exPlanation (SHAP) models. This enables a comprehensive comparison of critical process factors influencing camber and coupling analysis in various rolling scenarios, including the overall rough-rolled slab and specific steel grades such as Q335B and ST12. Extensive experiments have been conducted on actual rough roughing datasets, demonstrating the superior performance of our camber prediction model compared to baselines. Furthermore, we have conducted a quantitative analysis of their overall effect, main effect, and interaction effect. These coupling analysis results consistently align with established theoretical insights, showing their substantial significance in advancing precise process optimization strategies and cost-efficient measures for controlling camber.

Developing deep learning surrogate models for digital twins in mineral processing – A case study on data-driven multivariate multistep forecasting

The escalating demand for environmental and social sustainability underscores the critical need for large industries such as mining and metallurgy to function optimally. Achieving optimal operation of a mineral processing plant requires timely access to information and rapid implementation of corrective actions. Essential information, such as current and future production, plays a pivotal role in enhancing process efficiency by facilitating adjustments in key aspects, including feed, control and operating variables. This study delves into the application of deep learning algorithms to forecast the gold concentrate grade of a flotation process half an hour in advance, a valuable tool for short-term decision-making, e.g., with regard to optimising the operation and process control. Multivariate time series data from a real gold processing plant was acquired through the process instrumentation and control system and augmented with simulated data at 5-minute intervals over 236 days. Surrogate models based on LSTM, GRU and CNN architectures were developed and analysed for predicting the gold grade one to six steps ahead. Evaluation of the testing dataset indicates that the CNN exhibits superior performance, achieving an average error reduction of 36 %, 45 % and 35 % for the performance metrics RMSE, MAE and MAPE respectively compared to the baseline model. These findings reveal the high potential of deep learning surrogate methods for accurately forecasting process outputs, enabling effective data-driven process control and optimisation strategies.

Efficient production of itaconic acid from the single-carbon substrate methanol with engineered Komagataella phaffii

BackgroundAmidst the escalating carbon dioxide levels resulting from fossil fuel consumption, there is a pressing need for sustainable, bio-based alternatives to underpin future global economies. Single-carbon feedstocks, derived from CO2, represent promising substrates for biotechnological applications. Especially, methanol is gaining prominence for bio-production of commodity chemicals.ResultsIn this study, we show the potential of Komagataella phaffii as a production platform for itaconic acid using methanol as the carbon source. Successful integration of heterologous genes from Aspergillus terreus (cadA, mttA and mfsA) alongside fine-tuning of the mfsA gene expression, led to promising initial itaconic acid titers of 28 g·L−1 after 5 days of fed-batch cultivation. Through the combined efforts of process optimization and strain engineering strategies, we further boosted the itaconic acid production reaching titers of 55 g·L−1 after less than 5 days of methanol feed, while increasing the product yield on methanol from 0.06 g·g−1 to 0.24 g·g−1.ConclusionOur results highlight the potential of K. phaffii as a methanol-based platform organism for sustainable biochemical production.

Performance and modeling of freezing crystallization in desalination: Heat transfer and impurities flow

This work applies the freezing crystallization in the desalination and establishes the theoretical models with experiments, to correlate the technical parameters with product properties. We first establish the heat flow models to simulate the 3-dimensional spatiotemporal temperature distribution and deduce the two heat flows through the ice layer, in which the latent heat from the phase transition plays a dominant role in heat transfer. Next, mass transfer and boundary layer models are proposed to evaluate the desalination efficiency and demonstrate the roles of kinetic and thermodynamic factors on the impurity migration. Later, three process optimization strategies, including cooling rate regulation, air bubble-assisted, and crystal seed-induced techniques, are imported to enhance the separation efficiency. At last, sweating is incorporated to reach the secondary purification effect, in which the desalination ratio under the optimal trajectory exceeds 83 %. The established sweating models accurately predict the product properties with time and can trace the impurities flow from the ice layer and adherent inclusion. The inclusion occupies a major discharged melt at the initial sweating stage, but more ice layer begins to melt and enter into the sweating liquid, confirming that a slight increase in product purity accompanies the huge yield loss.

Additive manufacturing of AISI 304L stainless steel: A review of processing parameters and mechanical performance

Additive manufacturing (AM) has become a favorable method for producing 304L stainless steel (SS) for various industrial applications, which is owing to its favorable characteristics including corrosion resistance, mechanical performance, and design flexibility. This review paper presents a comprehensive overview of the processing factors along with the mechanical performance of AM-fabricated 304L SS (AM304LSS). Firstly a discussion is provided for the fundamental principles of AM techniques that are common for processing SS304L. This includes selective laser melting (SLM), laser beam powder bed fusion (LB-PBF), direct metal laser sintering (DMLS), directed energy deposition (DED), wire-and-arc additive manufacturing (WAAM). Subsequently, the impact of key processing factors i.e. laser power, and powder characteristics on the microstructure and mechanical properties of AM304LSS is presented. In addition, this article examines recent progress in process optimization strategies and post-processing techniques for improving and enhancing the mechanical properties and surface finish of AM 304L stainless steel components. Finally, significant insights are provided for researchers, engineers, and practitioners involved in the advancement and application of AM304LSS components.

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification.This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

Energy Efficiency Trends in Petroleum Extraction: A Bibliometric Study

This comprehensive bibliometric analysis investigates energy-saving strategies in petroleum extraction, shedding light on key research areas, trends, and collaborations. The analysis covers 98 research articles spanning from 2003 to 2024, sourced from the Web of Science (WOS) database and analyzed using the Bibliometrics R package v.4.1.3, including descriptive statistics, network analysis, and factorial analysis. Findings reveal significant contributions from China, Canada, Russia, and the USA, with notable collaborations and thematic clusters identified. Top journals, prolific authors, and leading institutions are highlighted, showcasing global efforts in advancing sustainability in the oil industry. Institutions like the University of Calgary and authors such as Gates ID, Ren SR, and Zhang L play significant roles in advancing knowledge in this domain. Keyword analysis underscores prevalent themes such as optimization, simulation, and energy efficiency. Technological innovations, process optimization, and organizational strategies emerge as crucial avenues for reducing electrical energy consumption in oil extraction operations. However, limitations include database constraints and language bias. Overall, this study offers valuable insights for researchers, policymakers, and industry stakeholders, informing future research directions and policy initiatives for enhancing energy efficiency and sustainability in petroleum extraction.

The Trade-Off between Combustion and Partial Oxidation during Chemical Looping Conversion of Methane

The chemical looping reforming and combustion of methane have attracted increasing interest as processes for clean energy and syngas production, with potential to reduce carbon dioxide emissions. Previous literature on the development of oxygen carriers evidenced the effects that oxygen availability exerts on the selectivity of the oxidation reaction. In the present paper, we evaluate the performance of chromite sand (Chro), cerium dioxide (CeO2), and mixed cerium–copper oxide (Ce–Cu) as oxygen carriers for either reforming or combustion according to their oxygen availability. The oxides are tested in 2 to 5 min reduction intervals in a CH4/N2 mixture (5, 10 and 20% vol.) followed by regeneration in O2/N2 (3, 5, or 21% vol.), with redox cycles conducted either at 850 °C or 950 °C. The obtained rank of selectivity towards complete CH4 combustion is Ce–Cu > CeO2 > Chro. Another relevant finding is the role of the degree of carrier conversion in promoting partial or total oxidation. In particular, the selectivity towards CO2 markedly decreases at increasing carrier conversion, disclosing new strategies for process design and optimization by controlling the carrier conversion degree.

Enhancing the efficiency of high solid anaerobic digestion of empty fruit bunches under thermophilic conditions by particle size reduction and co-digestion with palm oil mill effluent

This study investigates the impact of thermophilic high solid anaerobic digestion (HS-AD) on biogas production from empty fruit bunches (EFB), focusing on the effects of total solids (TS) loading (5–40 %), particle size reduction (0.5, 3.25, and 6 cm), and co-digestion with palm oil mill effluent (POME) (10–30 % VS basis). The HS-AD at a 15–20 % TS loading has a methane yield of 103.4–105.3 mL CH4/g-VS with 24.6–25.1 % biodegradability. Particle size reduction to 0.5 cm enhanced methane yield by 54–61 % and improved hydrolysis rates by 45 % compared to the untreated EFB (6 cm) at a 15–20 % TS loading. Co-digestion of EFB with POME at a ratio of 31:1 based on VS basis led to a synergistic effect of 17.77 mL CH4/g-VS, increasing methane yield by 24–46.5 % and improving process stability, as evidenced by a 22.8–38.1 % reduction in volatile fatty acids (VFAs) accumulation. Microbial community analysis showed a 2-fold increase in the relative abundance of hydrogenotrophic methanogens (Methanothermobacter sp. and Methanoculleus sp.) during co-digestion, while the abundance of key cellulolytic bacteria (Clostridium sp. and Fibrobacter sp.) increased by 1.5-fold. The optimized HS-AD process achieved a maximum methane yield of 287.77 mL CH4/g-VS and a biodegradability of 61.2 % under thermophilic conditions, with a 20 % POME co-digestion addition (31:1 VS ratio) and 0.5 cm particle size. These findings demonstrate the potential of thermophilic HS-AD for the sustainable management of EFB and highlight the importance of process optimization and co-digestion strategies for enhanced biogas production from EFB.

Taxonomy of performance shaping factors in manufacturing: A systematic literature review

AbstractHuman error in manufacturing can have substantial consequences, including loss of life, injuries, productivity, and financial losses. Human reliability analysis (HRA) methods can be used to evaluate the likelihood of human error in manufacturing tasks and identify potential sources of error. Performance shaping factors (PSFs) are internal and external factors that influence human performance and can affect the likelihood of human reliability estimates in HRA methods. Understanding the impact of PSFs on human performance in manufacturing is essential for developing effective strategies to minimize the likelihood of human error and improve the safety and efficiency of manufacturing processes. This systematic review scrutinizes the literature on PSFs within manufacturing, highlighting HRA applications. Using the PRISMA protocol, studies from 2000 to 2024 across engineering and psychology were examined, culminating in the analysis of 35 pertinent works. The review identifies and contrasts various PSF taxonomies from established HRA methods like SPAR‐H, HEART, CREAM, and THERP, revealing their diverse applications in different manufacturing settings. The review also uncovers a tendency to devise taxonomies through the lens of experts' domain knowledge, particularly tailored to discrete manufacturing contexts. A critical gap is observed in the lack of a uniform PSF framework, with the current literature reflecting a disparate understanding of PSFs' roles, definitions, and interrelations. This absence is further pronounced by the inadequate integration of human factors in the dialogue surrounding Industry 4.0. The analysis points to the necessity of harmonizing PSFs to better assess human reliability amid technological integration. The findings emphasize the need for an industry‐specific PSF framework that aligns with the intricacies of manufacturing operations, thus enabling more accurate HRA outcomes and informing strategies for error reduction and process optimization.

Research on active load reducing optimization strategy for 6061Al low cycle fatigue cropping process based on crack propagation mode transformation

Low cycle fatigue cropping technology is a new type of cropping technology with advantages such as no chip waste and low load need. Numerous studies have shown that increasing the fatigue frequency or increasing the displacement load can improve the cropping efficiency, but at the same time, it will reduce the quality of the cross-section and cannot achieve a balance between high efficiency and high quality of cross-section. In response to this issue, this paper found the phenomenon of transition of fracture modes (from fatigue mode to tensile fracture mode) of 6061Al material in LCFC process, and proposed the Active Load Reducing Optimization Strategy for 6061Al in LCFC, which achieves a significant improvement in quality of cross-section with a slight loss of cropping efficiency. Firstly, this paper studied the crack fracture mechanism of 6061Al bar under different displacement loads in the LCFC process. It was found that the transition of crack propagation mode in 6061Al- LCFC process was the main reason for the increase in cross-section roughness. The crack propagation mode changed from fatigue propagation to tensile fracture propagation during the LCFC process, and the roughness of the area significantly increased under the tensile fracture propagation mode. Further research has found that the fundamental reason for the contradiction between the cropping efficiency and section quality of 6061Al is the change in time of crack propagation mode transition and tensile fracture zone area under different displacement loads. Finally, the LCFC process was modeled in a time dimension using acoustic emission(AE) technology, and it was divided into three stages according to the AE signals and the fracture mechanism. As a result, an active load reducing optimization strategy for LCFC process was developed to delay the occurrence of tensile fracture propagation modes. Experiments have shown that compared to the traditional LCFC cropping strategy using constant displacement loads, the active load reducing strategy proposed in this paper can reduce the proportion of tensile fracture area, achieve a significant improvement in quality of cross-section with a slight loss of cropping efficiency, thus achieving a balance between efficiency and section quality to some extent. At the same time, an LCFC process section quality efficiency evaluation index with dual weights of efficiency and section quality has been proposed, which can assist in selecting LCFC active load reducing strategy parameters with consideration of efficiency and section quality weights.

Evolution of microstructure and properties of IN738 alloy under thermal exposure with different post-processing states

The microstructure and properties of nickel-based superalloys under thermal exposure are the key basis for evaluating the high-temperature stability and proposing process optimization strategies. This study investigated the microstructural evolution and the corresponding tensile properties of IN738 alloy with different post-processing states under thermal exposure at 700 °C. The results show that the microstructures of IN738 alloys with different post-processing states (i.e., hot isostatic pressing (HIP) and standard heat treatment (SHT)) are significantly different. For the HIPed sample, large cuboidal γ′ and a small number of ultra-fine spheroidal γ′ precipitates are present in the γ matrix. Under thermal exposure, the size and volume fraction of ultra-fine spheroidal γ′ increase with prolonged thermal exposure time. In comparison, the SHTed sample is characterized as a bimodal distribution of γ′, with large primary cuboidal γ′ and small secondary spherical γ′. The size and volume fraction of primary γ′ increase first and then remain steady under thermal exposure. In particular, the tensile properties under exposure depend highly on the post-processing states. The increase of tensile strength after the thermal exposure in the HIPed sample is attributed to the rising fine spherical γ′ volume fraction. The rising size and volume fraction of primary γ′ in the SHTed sample improve the tensile strength during thermal exposure.