Empowering employees for digital transformation in manufacturing enterprises: A case study

Purpose: This study explores how manufacturing enterprises can effectively promote digital transformation through employee empowerment with ability, motivation, and opportunity empowerment at different stages. Design/methodology/approach: Drawing on ability, motivation, and opportunity (AMO) theory, this qualitative research takes the Jiangsu Shangshang Cable Group as a typical case to discuss manufacturing enterprises’ evolving employee empowerment model. Gioia structured data analysis method was adopted to analyse the data. Findings/results: In the stage of information technology (IT) application, the main task of employee empowerment is strengthening the employees’ abilities (‘can do’) and motivation (‘want to do’) by training their IT knowledge and skills, changing their mindset towards IT application, and letting them experience the efficiency of the latter. In the intelligence manufacturing stage, training employees to do better is the core content of employee empowerment. Enterprises can strengthen employees’ intelligent manufacturing skills, practice data-based performance appraisal and salary management, and let employees participate in intelligent manufacturing. In the stage of digital transformation of the whole process, employees are required to ‘do their best in the long run’. Enterprises could improve employee collaboration capabilities, build growth mechanisms, and involve every employee in innovation. Practical implications: In the digital transformation process, manufacturing enterprises could empower employees according to the different goals and requirements in the three stages of IT application, intelligence manufacturing, and digital transformation of the whole process. It has reference value for manufacturing enterprises to achieve digital transformation. Originality/value: This study explores a three-dimensional evolving employee empowerment model to promote manufacturing enterprises’ digital transformation

Grindability and Surface Integrity of Cast Nickel-based Superalloy in Creep Feed Grinding with Brazed CBN Abrasive Wheels

AbstractThe technique of creep feed grinding is most suitable for geometrical shaping, and therefore has been expected to improve effectively material removal rate and surface quality of components with complex profile. This article studies experimentally the effects of process parameters (i.e. wheel speed, workpiece speed and depth of cut) on the grindability and surface integrity of cast nickel-based superalloys, i.e. K424, during creep feed grinding with brazed cubic boron nitride (CBN) abrasive wheels. Some important factors, such as grinding force and temperature, specific grinding energy, size stability, surface topography, microhardness and microstructure alteration of the sub-surface, residual stresses, are investigated in detail. The results show that during creep feed grinding with brazed CBN wheels, low grinding temperature at about 100 °C is obtained though the specific grinding energy of nickel-based superalloys is high up to 200-300 J/mm3. A combination of wheel speed 22.5 m/s, workpiece speed 0.1 m/min, depth of cut 0.2 mm accomplishes the straight grooves with the expected dimensional accuracy. Moreover, the compressive residual stresses are formed in the burn-free and crack-free ground surface

Wear evolution and stress distribution of single CBN superabrasive grain in high-speed grinding

In this study, both finite element analysis (FEA) and experimental observations were used to investigate the single CBN grain wear in high-speed grinding of Inconel 718 superalloy. The wear characteristics for each grinding pass were numerically assessed utilizing the tensile and compressive strength limits of the cutting grain. Additionally, stress distribution within the grain, chip formation and grinding force evolution during multiple passes were investigated. The combined experimental and numerical results show that the CBN grain wear has two major modes: the macro fracture on the grain top surface propagating from the rake surface, and the micro fracture near the cutting edges. The resultant tensile stress is the main factor inducing grain wear. The cutting edges will be under self-sharpening due to the grain wear. With multiple micro cutting edges engaged in grinding process, the limited material removal region was divided into different sliding, ploughing and cutting dominant regions. Overall, the ratio of material elements removed by a cutting process ranges from 80% to 20%, and continue to decrease during the grinding process. With a stronger effect of the cutting process, larger fluctuation of the grinding force will commence, however its average value remains below that with stronger sliding and ploughing process characteristics

Thermodynamic Mechanism of Nanofluid Minimum Quantity Lubrication Cooling Grinding and Temperature Field Models

Grinding is an indispensable form of machining, in which, a large amount of heat is transferred into workpiece surface, causing surface burn of the workpiece. Flood grinding is easy to cause pollution to the environment while dry grinding and minimum quantity lubrication (MQL) is insufficient of cooling and lubrication effect. The appearance of nanofluid minimum quantity lubrication cooling (NMQLC) technique can effectively solve the problem of heat transfer in grinding zone and also enhance the lubrication characteristics. In this chapter, NMQLC technique, including nanofluid preparation and atomization is summarized first; then a review on the mechanism of grinding thermodynamics under NMQLC condition is presented based on published literatures. Most of the studies, including investigation of grinding forces and temperatures, indicate that NMQLC has realized a lubrication-cooling effect close to that of flood lubrication. According to existing investigations, theoretical models of temperature field are concluded, heat source distribution model, thermal distribution coefficient model, and heat transfer coefficient model under NMQLC condition are developed, and temperature field control equation are determined. This chapter reviews and amasses the current state of the mechanism of grinding thermodynamics and also recommends ways to precision control the grinding temperature field

Effect of unilateral training and bilateral training on physical performance: A meta-analysis

Background: In Unilateral (UNI) exercises are more effective than bilateral (BI) exercises in improving athletic performance is debatable.Objectives: this meta-analysis investigated the effects of UNI and BI exercises on different effect indicators of jump ability, sprint ability, maximal force, change of direction ability, and balance ability.Data Sources: PubMed, Google Scholar, Web of science, CNKI, Proquest, Wan Fang Data.Study Eligibility Criteria: To be eligible for inclusion in the meta-analysis, the study had to be: 1) athletes; 2) UNI training and BI training; 3) the intervention period had to be more than 6 weeks and the intervention frequency had to be more than 2 times/week; 4) the outcome indicators were jumping ability, sprinting ability, maximum strength, and change of direction and balance.Study Appraisal and Synthesis Method: We used the random-effects model for meta-analyses. Effect sizes (standardized mean difference), calculated from measures of horizontally oriented performance, were represented by the standardized mean difference and presented alongside 95% confidence intervals (CI).Results: A total of 28 papers met the inclusion criteria, and Meta-analysis showed that UNI training was more effective than BI training in improving jumping ability (ES = 0.61.0.23 to 0.09; Z = 3.12, p = 0.002 < 0.01), sprinting ability (ES = −0.02, −0.03 to −0.01; Z = 2.73, p = 0.006 < 0.01), maximum strength (ES = 8.95,2.30 to 15.61; Z = 2.64, p = 0.008 > 0.05), change of direction ability (ES = −0.03, −0.06 to 0.00; Z = 1.90, p = 0.06 > 0.01) and balance ability (ES = 1.41,-0.62 to 3.44; Z = 1.36, p = 0.17 > 0.01). The results of the analysis of moderating variables showed that intervention period, intervention frequency and intervention types all had different indicators of effect on exercise performance.Conclusion: UNI training has a more significant effect on jumping and strength quality for unilateral power patterns, and BI training has a more significant effect on jumping and strength quality for bilateral power patterns

Surgical treatment of patellar dislocation: A network meta-analysis of randomized control trials and cohort studies

BackgroundCurrently, there are many surgical options for patellar dislocation. The purpose of this study is to perform a network meta-analysis of the randomized controlled trials (RCTs) and cohort studies to determine the better treatment.MethodWe searched the Pubmed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, clinicaltrials.gov and who.int/trialsearch. Clinical outcomes included Kujala score, Lysholm score, International Knee Documentation Committee (IKDC) score, redislocation or recurrent instability. We conducted pairwise meta-analysis and network meta-analysis respectively using the frequentist model to compare the clinical outcomes.ResultsThere were 10 RCTs and 2 cohort studies with a total of 774 patients included in our study. In network meta-analysis, double-bundle medial patellofemoral ligament reconstruction (DB-MPFLR) achieved good results on functional scores. According to the surface under the cumulative ranking (SUCRA), DB-MPFLR had the highest probabilities of their protective effects on outcomes of Kujala score (SUCRA 96.5 %), IKDC score (SUCRA 100.0%) and redislocation (SUCRA 67.8%). However, DB-MPFLR (SUCRA 84.6%) comes second to SB-MPFLR (SUCRA 90.4%) in Lyshlom score. It is (SUCRA 70%) also inferior to vastus medialis plasty (VM-plasty) (SUCRA 81.9%) in preventing Recurrent instability. The results of subgroup analysis were similar.ConclusionOur study demonstrated that MPFLR showed better functional scores than other surgical options

The functional graphene/epoxy resin composites prepared by novel two-phase extraction towards enhancing mechanical properties and thermal stability

Epoxy resins, known for their excellent properties, are widely used thermosetting resins, but their tendency towards brittle fracture limits their applications. This study addresses this issue by preparing graphene oxide via the Hummer method, modifying it with hyperbranched polyamide ester, and reducing it with hydrazine hydrate to obtain functionalized graphene. This functionalized graphene improves compatibility with epoxy resin. Using a novel two-phase extraction method, different ratios of functionalized graphene/epoxy composites were prepared and tested for mechanical properties and thermal stability. The results showed significant improvements: the tensile strength of composites with 0.1 wt% functionalized graphene increased by 77% over pure epoxy resin, flexural strength by 56%, and glass transition temperature by 50°C. These enhancements, attributed to the improved compatibility between graphene and epoxy resin, demonstrate the potential of functionalized graphene to mitigate the brittleness of epoxy resins, expanding their application potential

Fracture behavior and self-sharpening mechanisms of polycrystalline cubic boron nitride in grinding based on cohesive element method

Unlike monocrystalline cubic boron nitride (CBN), polycrystalline CBN (PCBN) shows not only higher fracture resistance induced by tool-workpiece interaction but also better self-sharpening capability; therefore, efforts have been devoted to the study of PCBN applications in manufacturing engineering. Most of the studies, however, remain qualitative due to difficulties in experimental observations and theoretical modeling and provide limited in-depth understanding of the self-sharpening behavior/mechanism. To fill this research gap, the present study investigates the self-sharpening process of PCBN abrasives in grinding and analyzes the macro-scale fracture behavior and highly localized micro-scale crack propagation in detail. The widely employed finite element (FE) method, together with the classic Voronoi diagram and cohesive element technique, is used considering the pronounced success of FE applications in polycrystalline material modeling. Grinding trials with careful observation of the PCBN abrasive morphologies are performed to validate the proposed method. The self-sharpening details, including fracture morphology, grinding force, strain energy, and damage dissipation energy, are studied. The effects of maximum grain cut depths (MGCDs) and grinding speeds on the PCBN fracture behavior are discussed, and their optimum ranges for preferable PCBN self-sharpening performance are suggested

Experimental study on LBL beams

Six specimens were made and tested to study the mechanical properties of LBL beams. The mean ultimate loading value is 68.39 MPa with a standard deviation of 6.37 MPa, giving a characteristic strength (expected to be exceeded by 95% of specimens) of 57.91 MPa, and the mean ultimate deflection is 53.3 mm with a standard deviation of 5.5 mm, giving the characteristic elastic modulus of 44.3 mm. The mean ultimate bending moment is 20.18 kN.m with a standard deviation of 1.88 kN.m, giving the characteristic elastic modulus of 17.08 kN.m. The mean elastic modulus is 9688 MPa with a standard deviation of 1765 MPa, giving the characteristic elastic modulus of 6785 MPa, and the mean modulus of rupture is 93.3 MPa with a standard deviation of 8.6 MPa, giving the characteristic elastic modulus of 79.2 MPa. The strain across the cross-section for all LBL beams is basically linear throughout the loading process, following standard beam theory

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5