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Angular-2 – Developers Preview

Angular 2 is a big upgrade from Angular 1. It is a framework for mobile apps and can be used for desktop as well. Like Angular 1, Angular 2 (currently in alpha) is built on a set of concepts that are used throughout the framework and they would be used directly or, indirectly while writing applications.

Angular 2 separates updating the application model and reflecting the state of the model in the view into two distinct phases. The developer is responsible for updating the application model. Angular, by means of change detection, is responsible for reflecting the state of the model in the view. The framework does it automatically on every VM turn.

Angular 2 Features:

Component-based UI
Angular is adopting a component-based UI, a concept that might be familiar to React developers. In a sense, the Angular 1.x controllers and directives blur into the new Angular 2 Component. This means that in Angular 2 there are no controllers and no directives. Instead, a component has a selector which corresponds to the html tag that the component will represent and View to specify an HTML template for the component to populate.

User Input with the Event Syntax
Angular 2 applications now respond to user input by using the event syntax. The event syntax is denoted by an action surrounded by parenthesis (event). You can also make element references available to other parts of the template as a local variable using the #var syntax.

Goodbye $scope
Even though ‘$scope’ has been replaced by “controller as” as a best practice since Angular 1.2, it still lingers in many tutorials. Angular 2 finally kills it off, as properties are bound to components.

Better Performance
With an ultra fast change detection and  immutable data structures, Angular 2 promises to be both faster and more memory efficient. Also, the introduction of uni-directional data flow, popularized by Flux, helps to ease some of the concern in debugging performance issues with an Angular app. This also means no more two-way data binding which was a popular feature in Angular 1.x. Not to worry, even though ng-model is no more, the same concept can be solved in a similar way with Angular 2.CWcQuqmWsAE8UKK

In any front-end web, frameworks is the technique used for change detection. Angular 2 adds a powerful and much flexible technique to detect changes on the objects used in the application. In Angular 1, the only way the framework detects changes, is through dirty checking. Whenever digest cycle runs in Angular 1, the framework checks for changes on all objects bound to the view and it applies the changes wherever they are needed. The same technique is used for any kind of objects. In AngularJS 2, we don’t have a chance to leverage the powers available in objects – like observable and immutable. Angular 2 opens this channel by providing a change detection system that understands the type of the object being used.

In addition, the change detectors in Angular 2 follow a tree structure to detect changes. This makes the system predictable and it reduces the time taken to detect changes.

If plain JavaScript objects are used to bind data on the views, Angular 2 has to go through each node and check for changes on the nodes, with each browser event. Though it sounds similar to the technique in Angular 1 but the checks happen very fast as the system has to parse a tree in a known order. If we use Observables or, Immutable objects instead of the plain mutable objects, the framework understands them and provides better change detection.

Angular 2 is written from the ground-up using the latest features available in the web ecosystem and it brings several significant improvements over the framework’s older version. While it retires a number of Angular 1 features, it also adopts a number of core concepts and principles from an older version of the framework.angular-2-better-or-worse-26-638-1

Short Summary:

  • Angular 2 separates updating the application model and updating the view.
  • Event bindings are used to update the application model.
  • Change detection uses property bindings to update the view. Updating the view is unidirectional and top-down. This makes the system a lot more predictable and performant.
  • Angular 2 embraces unidirectional data-flow.
  • You can use the same mindset when building Angular 1.x applications.

The team has collaborated with the TypeScript team at Microsoft, both the teams are working really hard to create a great framework and they are also working with TC39 team to make JavaScript a better language. The best is yet to come and hence the future is going to be exciting for all developers.

In case, you have any queries on Angular 2 framework, feel free to approach us on hello@mantralabsglobal.com, our developers are here to clear confusions and it might be a good choice based on your business and technical needs.

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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